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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/COMMAND.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/COMMAND.json new file mode 100644 index 0000000000000000000000000000000000000000..8bfcaaf7a4915627f56f5f6a1b92cd5fc9fd0191 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/COMMAND.json @@ -0,0 +1,20 @@ +[ + "docker", + "exec", + "--user", + "0:0", + "-e", + "CHAIN_PROJECT_ROOT=/mnt/agent-bench-data/lingjun/ziche", + "-e", + "CHAIN_RENDER=1", + "-e", + "OMP_NUM_THREADS=2", + "-e", + "OPENBLAS_NUM_THREADS=1", + 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/CONTACT_CAPTURE.json @@ -0,0 +1,14 @@ +{ + "schema": "ziche.contact_capture.v1", + "requested_targets": [ + "/World/Assets/ShelfItems/coke_c3", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/ShelfItems/sprite_c1" + ], + "filter_semantics": "robot_or_any_requested_target", + "all_physics_ticks": true, + "control_ticks": 26654, + "dropped_rows": 0, + "callback_rows": 1525871, + "stream": "contacts.jsonl.gz" +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/CONTACT_CAPTURE_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/CONTACT_CAPTURE_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..4dc730fe4b1b7d0daad4391a92becce47a400d40 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/CONTACT_CAPTURE_AUDIT.json @@ -0,0 +1,25 @@ +{ + "passed": true, + "receipt": { + "schema": "ziche.contact_capture.v1", + "requested_targets": [ + "/World/Assets/ShelfItems/coke_c3", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/ShelfItems/sprite_c1" + ], + "filter_semantics": "robot_or_any_requested_target", + "all_physics_ticks": true, + "control_ticks": 26654, + "dropped_rows": 0, + "callback_rows": 1525871, + "stream": 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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..652b931e6546227c56409ddc0ce3c11828b2e2b9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json @@ -0,0 +1,66 @@ +{ + "passed": true, + "items": [ + { + "item_index": 0, + "target": "/World/Assets/ShelfItems/coke_c3", + "hand": "r", + "passed": true, + "native_hz": 60, + 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"filter_window_ticks": 7 + } + ], + "semantics": "Measured hand pose relative to moving base and each active target pose relative to its assigned hand; all item carries use the unchanged single-item thresholds." +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/INITIAL_PROP_NO_FALL.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/INITIAL_PROP_NO_FALL.json new file mode 100644 index 0000000000000000000000000000000000000000..6657e870f65670eaaa881bf65439fd4aed9d663c --- /dev/null +++ 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/INPUT_QUALIFICATION.json new file mode 100644 index 0000000000000000000000000000000000000000..400aa47424f983f12054272d130a3c4caa0587c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/INPUT_QUALIFICATION.json @@ -0,0 +1,66 @@ +{ + "schema": "shelf_to_table_multi10.fresh_input_qualification.v1", + "case_id": 2, + "passed": true, + "native_physics_still_required": true, + "checks": { + "fresh_no_rollout": true, + "empty_initial_tray": true, + "item_count_3_or_4": true, + "unique_targets": true, + "all_targets_active": true, + "all_paths_new_root": true, + "controls_valid": true, + "slot_floor_containment": true, + 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true, + "all_preroll_samples": true, + "translation": true, + "downward": true, + "rotation": true + }, + "evidence_sha256": { + "PREROLL_EVIDENCE.npz": "ebe2f3e22cae08d82930804f7e17719e7561df37b51cf8f7bb3d3a94ae990021", + "full_state.npz": "ce2ebe26bf8cacde891f334aec83be6f9c26e44f136022f7e4352791746bd940", + "INITIAL_PROP_NO_FALL.json": "15c5c51e31b06acd518a2a4ea9deefe859a861cd4db85326d30965082b687a38" + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json new file mode 100644 index 0000000000000000000000000000000000000000..49cc9dbe0e5173cb300e66feedf3cbd2d7f4a389 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json @@ -0,0 +1,196 @@ +{ + "schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": "2026-10-06T22:13:41.416521+00:00", + "layout_id": 2, + "passed": true, + "dataset": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/datasets/layout_02", + "reader": "lerobot LeRobotDataset(video_backend='pyav')", + "dataset_length": 8884, + "parquet_rows": 8884, + "metadata_frames": 8884, + "fps": 20, + "sample_indices": [ + 0, + 4442, + 8883 + ], + "samples": [ + { + "index": 0, + "passed": true, + "action_shape": [ + 22 + ], + "state_shape": [ + 23 + ], + "scalar_alignment": { + "frame_index": true, + "episode_index": true, + "source_control_tick": true, + "item_index": true, + "phase_index": true, + "timestamp": true, + "simulator_time_s": true + }, + "task": "Starting with an empty tray, pick up coke_c3, oreo_gf, sprite_c1 from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items.", + "images": { + "observation.images.top_head": { + "passed": true, + "shape": [ + 3, + 800, + 1280 + ], + "minimum": 0.0, + "maximum": 1.0, + "mean": 0.42735904455184937, + "standard_deviation": 0.2276434302330017 + }, + "observation.images.hand_left": { + "passed": true, + "shape": [ + 3, + 480, + 848 + ], + "minimum": 0.0, + "maximum": 0.9450980424880981, + "mean": 0.3761589229106903, + "standard_deviation": 0.2258310616016388 + }, + "observation.images.hand_right": { + "passed": true, + "shape": [ + 3, + 480, + 848 + ], + "minimum": 0.0, + "maximum": 1.0, + "mean": 0.36154595017433167, + "standard_deviation": 0.20048215985298157 + } + } + }, + { + "index": 4442, + "passed": true, + "action_shape": [ + 22 + ], + "state_shape": [ + 23 + 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"files": [], + "planning_wait_references": [], + "native_planning_waits_preserved": true, + "all_referenced_repairs_portable": true +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RESULT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RESULT.json new file mode 100644 index 0000000000000000000000000000000000000000..2bf0b540a45d7be782c9e98c41585bbef3440573 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RESULT.json @@ -0,0 +1,19 @@ +{ + "status": "PHYSICS_COMPLETE_UNVERIFIED", + "accepted": false, + "error": null, + "control_ticks": 26654, + "rendered": true, + "requested_objects": [ + "/World/Assets/ShelfItems/coke_c3", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/ShelfItems/sprite_c1" + ], + "completed_objects": [ + "/World/Assets/ShelfItems/coke_c3", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/ShelfItems/sprite_c1" + ], + "item_count": 3, + "requires": "independent collision, task, drive, continuity and export validation" +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RETURN_ARRIVAL.item_00.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RETURN_ARRIVAL.item_00.json new file mode 100644 index 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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RETURN_HEAD_TRANSITION.item_00.raise_before_walking.tick_00005449.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RETURN_HEAD_TRANSITION.item_00.raise_before_walking.tick_00005449.json new file mode 100644 index 0000000000000000000000000000000000000000..c3b38c65944b06b1b7d1188bb5ab5b3d2f60aa8c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/RETURN_HEAD_TRANSITION.item_00.raise_before_walking.tick_00005449.json @@ -0,0 +1,29 @@ +{ + "schema": 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measured poses immediately before this item pickup in the same uninterrupted episode" + } + ], + "actual_item_count": 3 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/WRIST_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/WRIST_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..e19ba2f5afb22776f3334806aa5eb2208d2eee8c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/WRIST_AUDIT.json @@ -0,0 +1,21 @@ +{ + "passed": true, + "policy": "wrist_camera_upright_v2", + "hands": { + "hand_left": { + "passed": true, + "minimum_height_m": 0.06959460598490652, + "minimum_up_z": 0.7931134027683661, + "minimum_height_tick": 22613, + "minimum_up_tick": 22612 + }, + "hand_right": { + "passed": true, + "minimum_height_m": 0.06070155992845931, + "minimum_up_z": 0.7821248377043478, + "minimum_height_tick": 22621, + "minimum_up_tick": 22619 + } + }, + "actual_samples": 26654 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/action_space.schema.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/action_space.schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b57d8dcb05b4dcae2b555d1a87772be3b0d72361 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/action_space.schema.json @@ -0,0 +1,66 @@ +{ + "version": "g1_omnipicker_jointpos20_basevw_v1", + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "state_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_x_world_m", + "base_y_world_m", + "base_yaw_world_unwrapped_rad" + ], + "dtype": "float32", + "physics_hz": 240, + "control_hz": 60, + "training_hz": 20, + "action_semantics": "applied_absolute_joint_position_targets_and_body_forward_yaw_velocity", + "state_semantics": "post_control_step_measured_joint_positions_and_measured_base_pose", + "sample_native_indices": "arange(2,N,3)", + "action_state_shift": 0, + "base_limits": { + "forward_m_s": 0.18, + "yaw_rate_rad_s": 0.25, + "linear_acceleration_m_s2": 0.12, + "angular_acceleration_rad_s2": 0.3 + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/collision_profile.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/collision_profile.usda new file mode 100644 index 0000000000000000000000000000000000000000..ecc8d60c80515027dc8bcdc6ae6d0559b7c4a475 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/collision_profile.usda @@ -0,0 +1,4500 @@ +#usda 1.0 + +over "World" +{ + over "Assets" + { + over "ShelvingUnit" + { + over "o" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "root" + { + over "GLTF_SceneRootNode" + { + over "shelf_boards_0" + { + over "Object_4" + { + over "Object_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "horizontal_brackets_1" + { + over "Object_6" + { + over "Object_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "vertical_beams_2" + { + over "Object_8" + { + over "Object_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Object_9" + { + over "Object_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "plastic_feet_3" + { + over "Object_11" + { + over "Object_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Sofa" + { + over "Obj" + { + over "Meshes" + { + over "FAB_converted_model" + { + over "root" + { + over "GLTF_SceneRootNode" + { + over "__seat_sofa_0" + { + over "Object_4" + { + over "Object_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Clock" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_e85f8bde8074f96b8d272e4074a5c8d_fbx" + { + over "RootNode" + { + over "clock_frame" + { + over "clock_frame_clock_frame_0" + { + over "clock_frame_clock_frame_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_face" + { + over "clock_face_clock_face_0" + { + over "clock_face_clock_face_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_big_hand" + { + over "clock_big_hand_clock_pointer_0" + { + over "clock_big_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_second_hand" + { + over "clock_second_hand_clock_pointer_0" + { + over "clock_second_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_small_hand" + { + over "clock_small_hand_clock_pointer_0" + { + over "clock_small_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Tv" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "Livingroom_TV_fbx" + { + over "RootNode" + { + over "Livingroom_TV" + { + over "TV_Legs" + { + over "Object_5" + { + over "TV_Legs_Livingroom_TV_0" + { + over "TV_Legs_Livingroom_TV_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "TV_Screen" + { + over "Object_8" + { + over "TV_Screen_Livingroom_TV_0" + { + over "TV_Screen_Livingroom_TV_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + } + + over "TvTable" + { + over "Obj" + { + over "Meshes" + { + over "FAB_converted_model" + { + over "tv_table_fbx" + { + over "RootNode" + { + over "P0627_Media_Storage01" + { + over "P0627_Media_Storage01_P0627_Media_Storage01_MAT01_0" + { + over "P0627_Media_Storage01_P0627_Media_Storage01_MAT01_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "CoffeeTable" + { + over "Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "TrashBinRed" + { + over "geo" + { + over "Cube_022" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Trash_Bag" + { + over "Trash_Bag_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "TrashBinTeal" + { + over "geo" + { + over "Cube_022" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Trash_Bag" + { + over "Trash_Bag_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "Door" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_9be505408364da7a3ccc07ac09dbe59_fbx" + { + over "RootNode" + { + over "Door_Frame" + { + over "Door_Frame_Door_0" + { + over "Door_Frame_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door" + { + over "Door_Door_0" + { + over "Door_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Lever" + { + over "Lever_Door_0" + { + over "Lever_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Latch_Bolt" + { + over "Latch_Bolt_Door_0" + { + over "Latch_Bolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Deadbolt" + { + over "Deadbolt_Door_0" + { + over "Deadbolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + } + + over "Fireplace" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "fireplace_fbx" + { + over "RootNode" + { + over "fireplace" + { + over "fireplace_fireplace_0" + { + over "fireplace_fireplace_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "SmallCoffeeTable" + { + over "geo" + { + over "组003" + { + over "Rectangle058" + { + over "Rectangle058" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle059" + { + over "Rectangle059" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle060" + { + over "Rectangle060" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle061" + { + over "Rectangle061" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle062" + { + over "Rectangle062" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle079" + { + over "Rectangle079" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Line055" + { + over "Line055" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle063" + { + over "Rectangle063" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle064" + { + over "Rectangle064" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle067" + { + over "Rectangle067" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "组001" + { + over "Line056" + { + over "Line056" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Rectangle074" + { + over "Rectangle074" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle075" + { + over "Rectangle075" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle076" + { + over "Rectangle076" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "组002" + { + over "Line057" + { + over "Line057" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Rectangle077" + { + over "Rectangle077" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle078" + { + over "Rectangle078" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle054" + { + over "Rectangle054" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Line054" + { + over "Line054" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + + over "TableclothBasket" + { + over "riser" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "model" + { + over "Geometry" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_dc85028501a4347a20df6a75b3e3044_fbx" + { + over "RootNode" + { + over "pCube2" + { + over "pCube2_Texture_0" + { + over "pCube2_Texture_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + + over "CoatRack" + { + over "MainTrunk" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseHub" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch01Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch01Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch02Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch02Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch03Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch03Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch04Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch04Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch05Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch05Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch06Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch06Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch07Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch07Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch08Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch08Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg01Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg01Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg02Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg02Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg03Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg03Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "HouseEntryDoor" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_9be505408364da7a3ccc07ac09dbe59_fbx" + { + over "RootNode" + { + over "Door_Frame" + { + over "Door_Frame_Door_0" + { + over "Door_Frame_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door" + { + over "Door_Door_0" + { + over "Door_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Lever" + { + over "Lever_Door_0" + { + over "Lever_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Latch_Bolt" + { + over "Latch_Bolt_Door_0" + { + over "Latch_Bolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Deadbolt" + { + over "Deadbolt_Door_0" + { + over "Deadbolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + + over "LivingRoomWallpaper" + { + over "NorthAccent" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "LivingRoomRug" + { + over "Surface" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "OuterBorder" + { + over "West" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "East" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "South" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "North" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "InnerBorder" + { + over "West" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "East" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "South" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "North" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + } + + over "WestWindowTrim" + { + over "LeftCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "RightCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BottomSill" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CenterMullion" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "EastWindowTrim" + { + over "LeftCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "RightCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopCasing" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BottomSill" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CenterMullion" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "PantrySlidingDoor" + { + over "Plank_01" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_02" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_03" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_04" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_05" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_06" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_07" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_07" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "FrameLeft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameRight" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameBottom" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameTop" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "DiagonalBrace" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Hardware" + { + over "Rail" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Hanger_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wheel_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Hanger_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wheel_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Stop_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Stop_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Pull" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PullMount_73" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PullMount_103" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + } + + over "FireplaceArtwork" + { + over "Model" + { + over "dkg_Wall_Frames_16a" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LivingRoomPlantStands" + { + over "TvSouthWestWindowLeft" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "PLANTER_COSTA_GLAZED_WHITE_PLANTER_COSTA_GLAZED_WHITE_Mat_0" + { + over "PLANTER_COSTA_GLAZED_WHITE_PLANTER_COSTA_GLAZED_WHITE_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "FICUS_LYRATA" + { + over "FICUS_LYRATA_Ficus_Lyrata_Mat_0" + { + over "FICUS_LYRATA_Ficus_Lyrata_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "TvNorth" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "PLANTER_VOLUME_SMALL_PLANTER_VOLUME_SMALL_Mat_0" + { + over "PLANTER_VOLUME_SMALL_PLANTER_VOLUME_SMALL_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "FICUS_ELASTICA" + { + over "FICUS_ELASTICA_FICUS_ELASTICA_Mat_0" + { + over "FICUS_ELASTICA_FICUS_ELASTICA_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "WestWindowRight" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "PLANTER_VOLUME_TALL_PLANTER_VOLUME_TALL_Mat_0" + { + over "PLANTER_VOLUME_TALL_PLANTER_VOLUME_TALL_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "FICUS_AUDREY" + { + over "FICUS_AUDREY_FICUS_AUDREY_Mat_0" + { + over "FICUS_AUDREY_FICUS_AUDREY_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "EastWindowLeft" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "POT_SIMPLE_DARK_PLASTIC_02_POT_SIMPLE_DARK_PLASTIC_Mat_0" + { + over "POT_SIMPLE_DARK_PLASTIC_02_POT_SIMPLE_DARK_PLASTIC_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "RHODEA_JAPONICA_02" + { + over "RHODEA_JAPONICA_02_RHODEA_JAPONICA_Mat_0" + { + over "RHODEA_JAPONICA_02_RHODEA_JAPONICA_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "EastWindowRight" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "Eda_Planter_Small_Eda_Planter_Small_Mat_0" + { + over "Eda_Planter_Small_Eda_Planter_Small_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "BOSTON_FERN" + { + over "BOSTON_FERN_BOSTON_FERN_Mat_0" + { + over "BOSTON_FERN_BOSTON_FERN_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + + over "CoffeeGrinder" + { + over "BodyVisual" + { + over "Model" + { + over "DBM8_Grinder_ROOT" + { + over "Main_Base" + { + over "Cube" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_L_B" + { + over "Cube_003" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Clear" + { + over "Cube_009" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Opening" + { + over "Cylinder_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_180" + { + over "Cube_064" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Bottom_Band" + { + over "Cube_012" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Hopper_Adjustment_Ring" + { + over "Cylinder" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_0" + { + over "Cube_062" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Bean_Hopper_Top_Rim" + { + over "Bean_Hopper_Top_Rim_Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Bean_Hopper_Clear" + { + over "Bean_Hopper_Clear_Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_R_B" + { + over "Cube_005" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Start_Button" + { + over "Cube_069" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Top_Deck" + { + over "Cube_008" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Top_Band" + { + over "Cube_011" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Slider_Groove" + { + over "Cube_067" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Motor_Housing" + { + over "Cube_006" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Stainless_Grind_Selector_Collar" + { + over "Cone" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cuisinart_Label" + { + over "Text" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Base_Stainless_Trim" + { + over "Cube_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Selector_Dots" + { + over "Sphere" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_R_F" + { + over "Cube_004" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Center_Dome" + { + over "Sphere_018" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Start_Label" + { + over "Text_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Motor_Housing_Rear_Cap" + { + over "Cube_007" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Interior_Back" + { + over "Cube_010" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_90" + { + over "Cube_063" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Bottom_Trim" + { + over "Cube_013" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_L_F" + { + over "Cube_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Slider_Knob" + { + over "Cube_068" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_270" + { + over "Cube_065" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Control_Panel" + { + over "Cube_066" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "Task5PackStation" + { + over "Cabinet" + { + over "SideL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "SideR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Back" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Bottom" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Flap0" + { + over "Panel" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat00" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat07" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Recess" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandleBar" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Flap1" + { + over "Panel" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat00" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Recess" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandleBar" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Plinth" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Inside" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Tray" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_99fa031bb144e19baa97558c9a1ba91_fbx" + { + over "RootNode" + { + over "OPM0047" + { + over "OPM0047_OPM0047_0" + { + over "OPM0047_OPM0047_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "ShelfItems" + { + over "gatorade_m" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "gatorade_t" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "good2grow" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "grapefruit_can" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "coke_c3" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "sprite_c1" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + } + + over "Tier2Items" + { + over "pring_bbq" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "oreo_gf" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "skit_littles" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "pring_bbq_copy_01" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "chomps_r_copy_01" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + } + } + + over "Background" + { + over "Shell" + { + over "NY_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NYb_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NYt_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NY_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NYb_5" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NYt_6" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NY_7" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PY_8" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NorthInsetReturn_8c" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NX_9" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NXt_10" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NX_11" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PX_12" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "DrFrL_13" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "DrFrR_14" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "DrFrT_15" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Ceiling_18" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkPY_19" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkPY_Kitchen_19b" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkPY_KitchenBack_19d" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkNorthInsetReturn_19c" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkNX_20" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "SkPX_21" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Floor" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "ConstructorKitchen" + { + over "MetricScale" + { + over "BP_Kitchen7" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen8" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen9" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen10" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Sink" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Tap" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "BP_Kitchen36" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen16" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen25" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen21" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen27" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen18" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen22" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen28" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen32" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightEnd300" + { + over "Module" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "RightRange600" + { + over "Module" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Box_In_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Oven" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Oven_Door" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Stove" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "RightWallBase400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallBase150" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallUpper400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallUpper150" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_C" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnUpper400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnScribe" + { + over "Base" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Upper" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "CornerGapBase300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapBase300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapUpper300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapUpper300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_C" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "SquareEndUpper300" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "WindowEndBase400_01" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "WindowEndBase400_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "WindowEndBase150" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + + over "ContinuousPantryCornerWall" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "SkNorthKitchenReturn_04" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + } + + over "PhysicsScene" ( + prepend apiSchemas = ["PhysxSceneAPI"] + ) + { + bool physxScene:enableCCD = 1 + } + + over "CanonicalTableclothBasketCollisionGroup" + { + rel physics:filteredGroups = None + } +} + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/job.json 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"drive:angular:physics:maxForce": "inf", + "drive:angular:physics:stiffness": "10.0", + "drive:angular:physics:targetPosition": "0.0", + "drive:angular:physics:targetVelocity": "None", + "drive:angular:physics:type": "force", + "physics:axis": "Z", + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.00018224485393147916", + "physics:localPos0": "(0.018118, 0.01574, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(1, 0, 0, 0)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "-2.0", + "physics:upperLimit": "2.0", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx89_gripper_r_outer_joint0": { + "drive:angular:physics:damping": "1.0", + "drive:angular:physics:maxForce": "inf", + "drive:angular:physics:stiffness": "10.0", + "drive:angular:physics:targetPosition": "0.0", + "drive:angular:physics:type": "force", + "physics:axis": "Z", + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "2.2422236725105904e-05", + "physics:localPos0": "(0, 0.01, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(1, 0, 0, 0)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "-114.591552734375", + "physics:upperLimit": "114.591552734375", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx92_gripper_r_center_joint": { + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.0", + "physics:localPos0": "(0, 0, 0.14308)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(0.70710677, 0, 0, -0.70710677)", + "physics:localRot1": "(1, 0, 0, 0)" + }, + "idx53_gripper_l_outer_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, 0.022, 0.073)", + "physics:localPos1": "(-0.032, -0.01, 0)", + "physics:localRot0": "(0.0046482855, 0.7070915, -0.004648252, 0.7070915)", + "physics:localRot1": "(1, 2.5377787e-8, -8.560052e-8, -1.27006e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx54_gripper_l_inner_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, -0.022, 0.073)", + "physics:localPos1": "(-0.032, 0.01, 0)", + "physics:localRot0": "(0.0046482855, 0.7070915, -0.004648252, 0.7070915)", + "physics:localRot1": "(1, 2.5377787e-8, -8.560052e-8, -1.27006e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx93_gripper_r_outer_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, 0.022, 0.073)", + "physics:localPos1": "(-0.032, -0.01, 0)", + "physics:localRot0": "(0.004648279, 0.7070915, -0.0046482584, 0.7070915)", + "physics:localRot1": "(1, 3.778885e-8, 1.02719035e-7, 4.0319208e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx94_gripper_r_inner_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, -0.022, 0.073)", + "physics:localPos1": "(-0.032, 0.01, 0)", + "physics:localRot0": "(0.0046464815, 0.7070915, -0.0046464815, 0.7070915)", + "physics:localRot1": "(1, 0, 0, 0)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "dof_names": [ + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx21_arm_l_joint1", + "idx61_arm_r_joint1", + "idx22_arm_l_joint2", + "idx62_arm_r_joint2", + "idx23_arm_l_joint3", + "idx63_arm_r_joint3", + "idx24_arm_l_joint4", + "idx64_arm_r_joint4", + "idx25_arm_l_joint5", + "idx65_arm_r_joint5", + "idx26_arm_l_joint6", + "idx66_arm_r_joint6", + "idx27_arm_l_joint7", + "idx67_arm_r_joint7", + "idx31_gripper_l_inner_joint1", + "idx41_gripper_l_outer_joint1", + "idx71_gripper_r_inner_joint1", + "idx81_gripper_r_outer_joint1", + "idx32_gripper_l_inner_joint3", + "idx42_gripper_l_outer_joint3", + "idx72_gripper_r_inner_joint3", + "idx82_gripper_r_outer_joint3", + "idx33_gripper_l_inner_joint4", + "idx43_gripper_l_outer_joint4", + "idx73_gripper_r_inner_joint4", + "idx83_gripper_r_outer_joint4", + "idx39_gripper_l_inner_joint0", + "idx49_gripper_l_outer_joint0", + "idx79_gripper_r_inner_joint0", + "idx89_gripper_r_outer_joint0" + ], + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "runtime_gripper_max_effort_Nm": { + "idx41_gripper_l_outer_joint1": 80.0, + "idx81_gripper_r_outer_joint1": 80.0 + }, + "cameras": { + "top_head": { + "path": "/World/Robot/head_link2/Head_Camera", + "attributes": { + "cameraSensorType": "camera", + "clippingPlanes": "[]", + "clippingRange": "(0.01, 10000000)", + "exposure": "0.0", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.8959999084472656", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "640.0", + "omni:lensdistortion:opencvPinhole:cy": "400.0", + "omni:lensdistortion:opencvPinhole:fx": "646.0", + "omni:lensdistortion:opencvPinhole:fy": "646.0", + "omni:lensdistortion:opencvPinhole:imageSize": "(1280, 800)", + "omni:lensdistortion:opencvPinhole:k1": "-0.051394399255514145", + "omni:lensdistortion:opencvPinhole:k2": "0.05811314284801483", + "omni:lensdistortion:opencvPinhole:k3": "-0.017540574073791504", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "-0.0012259036302566528", + "omni:lensdistortion:opencvPinhole:p2": "0.0006320750107988715", + "omni:lensdistortion:opencvPinhole:s1": "0.0", + "omni:lensdistortion:opencvPinhole:s2": "0.0", + "omni:lensdistortion:opencvPinhole:s3": "0.0", + "omni:lensdistortion:opencvPinhole:s4": "0.0", + "projection": "perspective", + "purpose": "default", + "shutter:close": "0.0", + "shutter:open": "0.0", + "stereoRole": "mono", + "verticalAperture": "2.453000068664551", + "verticalApertureOffset": "0.0", + "visibility": "invisible", + "xformOp:orient": "(6.123233995736766e-17, 0.7071067811865476, -4.329780281177467e-17, -0.7071067811865476)", + "xformOp:scale": "(1, 1, 1)", + "xformOp:translate": "(0.0858, -0.04119, 0)", + "xformOpOrder": "[xformOp:translate, xformOp:orient, xformOp:scale]" + }, + "world_extrinsics": "camera_extrinsics_20hz.npz" + }, + "hand_left": { + "path": "/World/Robot/gripper_l_base_link/Left_Camera", + "attributes": { + "cameraProjectionType": "pinhole", + "clippingPlanes": "[]", + "clippingRange": "(0.01, 10000000)", + "exposure": "0.0", + "fisheyeResolutionBudget": "1.5", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.7601311206817627", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:kit:centerOfInterest": "(0, 0, -400)", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "424.9142150878906", + "omni:lensdistortion:opencvPinhole:cy": "241.9685516357422", + "omni:lensdistortion:opencvPinhole:fx": "433.967041015625", + "omni:lensdistortion:opencvPinhole:fy": "433.44677734375", + "omni:lensdistortion:opencvPinhole:imageSize": "(848, 480)", + "omni:lensdistortion:opencvPinhole:k1": "-0.05225364863872528", + "omni:lensdistortion:opencvPinhole:k2": "0.0581255704164505", + "omni:lensdistortion:opencvPinhole:k3": "-0.018849309533834457", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "0.0002689115353859961", + "omni:lensdistortion:opencvPinhole:p2": "0.0001695405662758276", + "omni:lensdistortion:opencvPinhole:s1": "0.0", + "omni:lensdistortion:opencvPinhole:s2": "0.0", + "omni:lensdistortion:opencvPinhole:s3": "0.0", + "omni:lensdistortion:opencvPinhole:s4": "0.0", + "p0": "-0.0003672190650831908", + "projection": "perspective", + "purpose": "default", + "shutter:close": "0.0", + "shutter:open": "0.0", + "stereoRole": "mono", + "verticalAperture": "2.130786657333374", + "verticalApertureOffset": "0.0", + "visibility": "invisible", + "xformOp:orient": "(0.14219926877000713, 0.6868383037850345, -0.6989317357080853, -0.13973883223733854)", + "xformOp:scale": "(1, 1, 1)", + "xformOp:translate": "(-0.07593376119019446, 0.009, 0.0205)", + "xformOpOrder": "[xformOp:translate, xformOp:orient, xformOp:scale]" + }, + "world_extrinsics": "camera_extrinsics_20hz.npz" + }, + "hand_right": { + "path": "/World/Robot/gripper_r_base_link/Right_Camera", + "attributes": { + "cameraProjectionType": "pinhole", + "clippingPlanes": "[]", + "clippingRange": "(0.01, 10000000)", + "exposure": "0.0", + "fisheyeResolutionBudget": "1.5", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.7601311206817627", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:kit:centerOfInterest": "(0, 0, -0.252056884844326)", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "425.91937255859375", + "omni:lensdistortion:opencvPinhole:cy": "236.65130615234375", + "omni:lensdistortion:opencvPinhole:fx": "433.7381286621094", + "omni:lensdistortion:opencvPinhole:fy": "433.2144470214844", + "omni:lensdistortion:opencvPinhole:imageSize": "(848, 480)", + "omni:lensdistortion:opencvPinhole:k1": "-0.053031452000141144", + "omni:lensdistortion:opencvPinhole:k2": "0.05992193520069122", + "omni:lensdistortion:opencvPinhole:k3": "-0.019611800089478493", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "-0.00033373627229593694", + "omni:lensdistortion:opencvPinhole:p2": "6.050317460903898e-05", + "omni:lensdistortion:opencvPinhole:s1": "0.0", + "omni:lensdistortion:opencvPinhole:s2": "0.0", + "omni:lensdistortion:opencvPinhole:s3": "0.0", + "omni:lensdistortion:opencvPinhole:s4": "0.0", + "p0": "-0.0003672190650831908", + "projection": "perspective", + "purpose": "default", + "shutter:close": "0.0", + "shutter:open": "0.0", + "stereoRole": "mono", + "verticalAperture": "2.130786657333374", + "verticalApertureOffset": "0.0", + "visibility": "invisible", + "xformOp:orient": "(-0.1397388322373386, -0.6989317357080854, -0.6868383037850344, -0.14219926877000738)", + "xformOp:scale": "(1, 1, 1)", + "xformOp:translate": "(0.07593, -0.009, 0.021)", + "xformOpOrder": "[xformOp:translate, xformOp:orient, xformOp:scale]" + }, + "world_extrinsics": "camera_extrinsics_20hz.npz" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene.usda new file mode 100644 index 0000000000000000000000000000000000000000..5605ef904aeaf2a23055ce661a2b5324d28cc932 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene.usda @@ -0,0 +1,1169 @@ +#usda 1.0 +( + endTimeCode = 0 + metersPerUnit = 1 + startTimeCode = -1 + subLayers = [ + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/collision_profile.usda@, + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/scene.usda@ + ] + timeCodesPerSecond = 60 + upAxis = "Z" +) + +over "World" +{ + over "Robot" ( + delete apiSchemas = ["PhysicsArticulationRootAPI"] + ) + { + over "gripper_l_inner_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.480268, 3.2272608, -4.965701) + vector3f physics:velocity = (0.33934477, 2.0487342, 2.460084) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (-0.0051912586, 0.9101307, 0.41426617, 0.004328973) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1902935, 0.9822686, 0.9642756) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.5071297, 3.2347078, -7.0275793) + vector3f physics:velocity = (-0.325375, 2.1827388, 2.8760111) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (-0.0011801159, 0.42900184, 0.90327835, 0.006655358) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.081951894, 0.98413426, 0.96442497) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6945715, -3.1949875, 7.048029) + vector3f physics:velocity = (0.3257842, 2.2116559, 2.7649822) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (-0.43227345, 0.010058144, 0.0026072187, 0.9016827) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.081901595, -0.98622406, 0.9649359) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6686597, -3.2258666, 5.1086054) + vector3f physics:velocity = (-0.34156308, 2.0822756, 2.5586102) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (0.9064629, -0.009426032, 0.0043720757, -0.42215762) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.19057359, -0.9843711, 0.9636973) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "base_link" ( + prepend apiSchemas = ["PhysicsArticulationRootAPI", "PhysxArticulationAPI", "PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.10007482, -0.0012623979, 0.0035472638) + vector3f physics:velocity = (-0.0036614821, 0.011222672, 0.06641679) + bool physxArticulation:enabledSelfCollisions = 1 + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, -7.9149174e-7, -0.000052224772, -9.9396466e-8) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.000010142237, -3.4237306e-7, 0.0000039301813) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + over "FixedJoint" + { + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + } + } + + over "body_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.10007482, -0.0012623979, 0.0035472638) + vector3f physics:velocity = (-0.0046902252, -0.057037026, 0.17531484) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, -7.9174805e-7, -0.000052252064, -9.950107e-8) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-0.000057690635, 6.7886606e-7, 0.64927685) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "body_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2962755, -0.00026393068, 0.003546623) + vector3f physics:velocity = (-0.0057210163, -0.056898266, 0.12763926) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70710605, 0.7071071, -0.0005283447, -0.000528339) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1309424, 6.6984444e-7, 0.64929056) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "head_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.29627544, -0.00022350429, 0.030593956) + vector3f physics:velocity = (-0.0065316996, 0.08925952, 0.17159957) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999997, -7.5418393e-7, 0.0007472183, -0.000020784797) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13160127, 0.0000013757304, 1.0902903) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "head_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.6092531, 0.00057793234, 0.030593479) + vector3f physics:velocity = (-0.007506882, 0.072152376, 0.15434086) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70710516, 0.7071062, -0.0012754414, -0.001246075) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.18192898, -8.435972e-7, 1.1502801) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2962755, -0.00026393068, 0.003546623) + vector3f physics:velocity = (-0.0051159915, 0.033401925, 0.18490136) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999997, -7.349024e-7, 0.0007472032, -7.5015386e-8) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139814, 0.000001102499, 0.9542904) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2962755, -0.00026393068, 0.003546623) + vector3f physics:velocity = (-0.0051156166, 0.03302846, 0.18490487) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.7071061, 0.70710707, -0.0005283869, -0.0005283392) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139813, 0.025001038, 0.9542904) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.5342536, -0.0073949113, 0.0035504377) + vector3f physics:velocity = (-0.005116334, 0.031645488, 0.18148759) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70709527, 0.70709634, 0.003945449, 0.003945618) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139825, 0.21090113, 0.95429015) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.5421638, 3.1325002, 0.038593203) + vector3f physics:velocity = (-0.0020902974, 0.027317982, 0.41835466) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.004566687, 0.71102643, 0.004617499, -0.7031353) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139825, 0.21090113, 0.95429015) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.1508313, 3.1412694, 0.086559206) + vector3f physics:velocity = (0.002403613, 0.0137736015, 0.96985626) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.71168166, -0.7024985, 0.001528225, 0.0016510009) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135415, 0.5158756, 0.95825136) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.1785797, 3.1317387, -2.0481205) + vector3f physics:velocity = (0.018087225, 0.14264886, 1.3372159) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0002485937, 0.0022503359, -0.0064927633, 0.99997634) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135427, 0.5158756, 0.95825136) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link5" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.4736792, 3.1267068, -2.0695755) + vector3f physics:velocity = (0.01610988, 0.36837593, 1.6840872) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7116777, -0.70249784, -0.0022058191, -0.0026091223) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13145827, 0.71335906, 0.96081567) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link6" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.42381415, 3.153042, -5.915982) + vector3f physics:velocity = (0.022300156, 0.9318269, 2.0337198) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.006519352, 0.9999357, -0.008651857, 0.0033502846) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13145827, 0.71335906, 0.96081567) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_end_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4919796, 3.2305079, -5.8646865) + vector3f physics:velocity = (0.04282087, 1.3869792, 2.2775424) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7117168, -0.7023972, 0.009502437, -0.0026572044) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13457358, 0.8938167, 0.9631796) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4919796, 3.2305079, -5.8646865) + vector3f physics:velocity = (0.050705235, 1.6815842, 2.4351285) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.5051387, -0.4899505, 0.503389, 0.5013809) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13457346, 0.8938167, 0.96317947) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4739132, 3.2254992, -4.4779363) + vector3f physics:velocity = (0.17738186, 1.8681833, 2.4696352) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9260011, 0.0053605563, -0.0041174893, 0.37746036) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.15504493, 0.9499693, 0.9637284) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4813242, 3.2275536, -5.0467706) + vector3f physics:velocity = (0.32888433, 1.9106079, 2.378331) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0052084774, 0.91177297, 0.410639, 0.004308257) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.19007356, 0.9582713, 0.96396196) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4703727, 3.2245176, -4.2061734) + vector3f physics:velocity = (0.34094557, 2.047755, 2.4566717) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0059869355, 0.9769908, 0.2131742, 0.0031377827) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.18275233, 0.9888359, 0.96433437) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.5100896, 3.2355282, -7.2547655) + vector3f physics:velocity = (-0.07045612, 1.9065448, 2.621078) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.3933756, 0.0009190828, -0.006696434, 0.919353) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.11605239, 0.9506374, 0.9641104) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.502183, 3.2333364, -6.647879) + vector3f physics:velocity = (-0.31182197, 1.9666663, 2.767738) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0011622246, 0.42657685, 0.90442604, 0.006658504) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08133056, 0.96014416, 0.96411014) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.5134666, 3.2364645, -7.5139713) + vector3f physics:velocity = (-0.34701952, 2.1779535, 2.8791766) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.00024149522, 0.23008786, 0.9731464, 0.0067549073) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08970213, 0.99045235, 0.96453524) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_center_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-3.4919796, 3.2305079, -5.8646865) + vector3f physics:velocity = (0.06774593, 2.3302634, 2.782301) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.71171683, -0.70239717, 0.009502395, -0.0026570987) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13704295, 1.0368631, 0.9650532) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2962755, -0.00026393068, 0.003546623) + vector3f physics:velocity = (-0.005114884, 0.033783026, 0.18502225) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.00052848115, 0.0005283185, -0.7071061, 0.707107) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139817, -0.024998834, 0.9542905) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.7233474, -0.0078698415, 0.0035515106) + vector3f physics:velocity = (-0.0051108715, 0.033702426, 0.18164054) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0040696473, -0.0040696887, -0.70709455, 0.70709556) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1313982, -0.21299891, 0.95429087) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.715508, -3.1201196, -0.032273382) + vector3f physics:velocity = (-0.00705541, 0.029823324, 0.4407786) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.7030108, 0.0045899004, 0.71114993, -0.004537372) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139808, -0.21299891, 0.95429075) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.1640121, -3.1097705, -0.0823513) + vector3f physics:velocity = (-0.015123124, 0.017358165, 0.9609793) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0018374302, 0.0017094072, -0.70252657, 0.7116531) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135289, -0.5179736, 0.9582275) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.1927888, -3.0985007, 2.1492996) + vector3f physics:velocity = (-0.027631529, 0.15181765, 1.3280191) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999759, -0.0064522843, 0.0025111786, 0.00040357606) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135289, -0.5179736, 0.9582274) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link5" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.2767353, -3.1043277, 2.1940687) + vector3f physics:velocity = (-0.024430169, 0.38833094, 1.671894) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0051382254, -0.0044827624, -0.702516, 0.71163523) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13151856, -0.7154571, 0.9607756) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link6" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.227972, -3.155562, 5.9665112) + vector3f physics:velocity = (-0.027914144, 0.9616442, 2.0232913) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0067499913, -0.008655484, 0.9999186, -0.006503773) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13151856, -0.7154572, 0.9607756) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_end_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6794368, -3.2130237, 5.9152236) + vector3f physics:velocity = (-0.04699317, 1.4138074, 2.2687488) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.00507369, 0.0070911925, -0.70241594, 0.7117133) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13462706, -0.895915, 0.9631443) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6794368, -3.2130237, 5.9152236) + vector3f physics:velocity = (-0.051683113, 1.7095698, 2.4280689) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.49966967, 0.50169724, -0.49166873, 0.50684506) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13462694, -0.895915, 0.9631444) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6611028, -3.2348726, 4.5429654) + vector3f physics:velocity = (-0.17931366, 1.8987281, 2.5027208) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.009201878, 0.925951, -0.3775007, -0.004826077) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.15509696, -0.9520666, 0.96382797) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6686598, -3.2258663, 5.108614) + vector3f physics:velocity = (-0.33174443, 1.9413296, 2.4728158) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9136925, -0.009348268, 0.0045359028, -0.40627342) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.19012167, -0.9603773, 0.96338296) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6574444, -3.2392325, 4.26914) + vector3f physics:velocity = (-0.34460592, 2.0804865, 2.5584497) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.97751576, -0.008221622, 0.006353686, -0.21060617) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.18292092, -0.9908051, 0.963905) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6978136, -3.1911237, 7.2907066) + vector3f physics:velocity = (0.07154398, 1.9345206, 2.575711) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.00993731, -0.39326096, 0.9193682, -0.0030355393) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1161029, -0.9527368, 0.9639437) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6896751, -3.2008226, 6.6815386) + vector3f physics:velocity = (0.31228286, 1.9950013, 2.661392) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.42454293, 0.010035503, 0.002693175, 0.9053482) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.081384346, -0.9622314, 0.9646225) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.7012634, -3.1870127, 7.5489225) + vector3f physics:velocity = (0.3466823, 2.2074952, 2.7641163) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.22882088, 0.009264917, 0.0047037685, 0.9734131) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.089697585, -0.9924868, 0.96489596) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_center_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.6794368, -3.2130237, 5.9152236) + vector3f physics:velocity = (-0.06639223, 2.361924, 2.7782378) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7117134, 0.7024158, 0.007091277, 0.005073769) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13709106, -1.0389614, 0.9650221) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "joints" + { + over "idx41_gripper_l_outer_joint1" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:maxForce = 80 + float state:angular:physics:position = 44.65971 + float state:angular:physics:velocity = -79.652954 + } + + over "idx81_gripper_r_outer_joint1" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:maxForce = 80 + float state:angular:physics:position = 44.663033 + float state:angular:physics:velocity = -78.826385 + } + + over "idx01_body_joint1" + { + float state:linear:physics:position = 0.0007726789 + float state:linear:physics:velocity = 0.10416909 + } + + over "idx02_body_joint2" + { + float state:angular:physics:position = 0.09161249 + float state:angular:physics:velocity = 22.709274 + } + + over "idx11_head_joint1" + { + float state:angular:physics:position = -0.0023850685 + float state:angular:physics:velocity = 1.5496998 + } + + over "idx12_head_joint2" + { + float state:angular:physics:position = 0.11868557 + float state:angular:physics:velocity = 17.932354 + } + + over "idx21_arm_l_joint1" + { + float state:angular:physics:position = -0.72503453 + float state:angular:physics:velocity = -162.17786 + } + + over "idx61_arm_r_joint1" + { + float state:angular:physics:position = 0.7451457 + float state:angular:physics:velocity = 173.01215 + } + + over "idx22_arm_l_joint2" + { + float state:angular:physics:position = -0.7442689 + float state:angular:physics:velocity = -179.9145 + } + + over "idx62_arm_r_joint2" + { + float state:angular:physics:position = 0.7394991 + float state:angular:physics:velocity = 178.33116 + } + + over "idx23_arm_l_joint3" + { + float state:angular:physics:position = 0.89702743 + float state:angular:physics:velocity = 211.61145 + } + + over "idx63_arm_r_joint3" + { + float state:angular:physics:position = -0.94691116 + float state:angular:physics:velocity = -222.29944 + } + + over "idx24_arm_l_joint4" + { + float state:angular:physics:position = -0.038434077 + float state:angular:physics:velocity = -122.31967 + } + + over "idx64_arm_r_joint4" + { + float state:angular:physics:position = 0.056603607 + float state:angular:physics:velocity = 127.87644 + } + + over "idx25_arm_l_joint5" + { + float state:angular:physics:position = -0.6480377 + float state:angular:physics:velocity = -94.675865 + } + + over "idx65_arm_r_joint5" + { + float state:angular:physics:position = 1.0673703 + float state:angular:physics:velocity = 198.80588 + } + + over "idx26_arm_l_joint6" + { + float state:angular:physics:position = 0.95877856 + float state:angular:physics:velocity = 220.40654 + } + + over "idx66_arm_r_joint6" + { + float state:angular:physics:position = -0.9387759 + float state:angular:physics:velocity = -216.18301 + } + + over "idx27_arm_l_joint7" + { + float state:angular:physics:position = 0.9386283 + float state:angular:physics:velocity = 224.4216 + } + + over "idx67_arm_r_joint7" + { + float state:angular:physics:position = -0.9370309 + float state:angular:physics:velocity = -223.9215 + } + + over "idx31_gripper_l_inner_joint1" + { + float state:angular:physics:position = -44.655773 + float state:angular:physics:velocity = 79.462204 + } + + over "idx71_gripper_r_inner_joint1" + { + float state:angular:physics:position = -44.658764 + float state:angular:physics:velocity = 78.64158 + } + + over "idx32_gripper_l_inner_joint3" + { + float state:angular:physics:position = -4.1371727 + float state:angular:physics:velocity = 32.59479 + } + + over "idx42_gripper_l_outer_joint3" + { + float state:angular:physics:position = 4.17173 + float state:angular:physics:velocity = -34.775196 + } + + over "idx72_gripper_r_inner_joint3" + { + float state:angular:physics:position = -3.5842216 + float state:angular:physics:velocity = 32.41628 + } + + over "idx82_gripper_r_outer_joint3" + { + float state:angular:physics:position = 3.9286258 + float state:angular:physics:velocity = -34.91027 + } + + over "idx33_gripper_l_inner_joint4" + { + float state:angular:physics:position = -0.45627382 + float state:angular:physics:velocity = -4.6453667 + } + + over "idx43_gripper_l_outer_joint4" + { + float state:angular:physics:position = 0.30743402 + float state:angular:physics:velocity = 21.757217 + } + + over "idx73_gripper_r_inner_joint4" + { + float state:angular:physics:position = -1.9999986 + float state:angular:physics:velocity = -0.0004847159 + } + + over "idx83_gripper_r_outer_joint4" + { + float state:angular:physics:position = 0.98043185 + float state:angular:physics:velocity = 21.00288 + } + + over "idx39_gripper_l_inner_joint0" + { + float state:angular:physics:position = 24.32999 + float state:angular:physics:velocity = -43.521713 + } + + over "idx49_gripper_l_outer_joint0" + { + float state:angular:physics:position = -24.204594 + float state:angular:physics:velocity = 27.870745 + } + + over "idx79_gripper_r_inner_joint0" + { + float state:angular:physics:position = 25.627554 + float state:angular:physics:velocity = -48.10822 + } + + over "idx89_gripper_r_outer_joint0" + { + float state:angular:physics:position = -24.770027 + float state:angular:physics:velocity = 28.705297 + } + } + } + + over "PhysicsScene" ( + prepend apiSchemas = ["PhysxSceneAPI"] + ) + { + vector3f physics:gravityDirection = (0, 0, -1) + float physics:gravityMagnitude = 9.81 + uniform token physxScene:broadphaseType = "MBP" + bool physxScene:enableCCD = 1 + bool physxScene:enableGPUDynamics = 0 + bool physxScene:enableStabilization = 0 + uniform token physxScene:solverType = "TGS" + uint physxScene:timeStepsPerSecond = 240 + } + + over "Assets" + { + over "LivingRoomRug" + { + over "Surface" + { + bool physics:collisionEnabled = 0 + } + + over "OuterBorder" + { + over "West" + { + bool physics:collisionEnabled = 0 + } + + over "East" + { + bool physics:collisionEnabled = 0 + } + + over "South" + { + bool physics:collisionEnabled = 0 + } + + over "North" + { + bool physics:collisionEnabled = 0 + } + } + + over "InnerBorder" + { + over "West" + { + bool physics:collisionEnabled = 0 + } + + over "East" + { + bool physics:collisionEnabled = 0 + } + + over "South" + { + bool physics:collisionEnabled = 0 + } + + over "North" + { + bool physics:collisionEnabled = 0 + } + } + } + + over "ShelfItems" + { + over "gatorade_m" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (1.4490863e-8, -5.483158e-14, -2.3519674e-11) + vector3f physics:velocity = (6.2704494e-16, 4.8161497e-10, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9323211, 2.8876459e-11, 1.1294078e-11, -0.36163163) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.1829958, 1.65852, 1.1828283) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "good2grow" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.0000147194, -0.0008901543, 0.0016371284) + vector3f physics:velocity = (0.000026308873, 4.1962357e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.8895989, -3.027817e-7, 6.1380734e-7, -0.45674255) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.5870795, 1.5534439, 1.1828284) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "grapefruit_can" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.061856892, 0.06887248, 0.00034812168) + vector3f physics:velocity = (0.0062073427, -0.004981922, 0.055153668) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.26138777, 0.00014225571, -0.0001772384, 0.96523386) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.8592443, 1.5930173, 1.1833619) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "coke_c3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.0000110081055, 0.000019465066, -0.000025612097) + vector3f physics:velocity = (-4.8124963e-7, -2.72145e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.97298694, -1.299303e-8, 4.4891763e-8, 0.23086007) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.1345992, 1.525286, 1.1829593) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "sprite_c1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0, 0, 0) + vector3f physics:velocity = (0, 0, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.8602618, 0, 0, 0.5098526) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.6994572, 1.5110462, 1.1829593) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + + over "Tier2Items" + { + over "oreo_gf" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-6.200431e-8, 1.3263844e-8, 2.5005353e-10) + vector3f physics:velocity = (8.947044e-10, -2.872795e-9, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9154429, -1.1641532e-10, -9.537971e-11, -0.40244788) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.2148805, 1.5345933, 0.7513302) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "skit_littles" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-8.533251e-9, -4.644486e-10, 1.43986e-10) + vector3f physics:velocity = (-4.3858788e-11, -4.2316844e-10, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.861894, -1.1641532e-10, 3.174705e-11, 0.5070885) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.4755142, 1.5997632, 0.75133014) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "pring_bbq_copy_01" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (9.403879e-9, -3.5457163e-8, 0.0000010779694) + vector3f physics:velocity = (1.498424e-9, 3.8684256e-10, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.99829096, -4.3299227e-8, 7.392392e-7, -0.058439486) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.812485, 1.4615002, 0.75133014) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "chomps_r_copy_01" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.000010298781, 0.000056726345, 0.0007393618) + vector3f physics:velocity = (-0.0000021093094, -4.229305e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.6839448, 6.633927e-7, 6.246402e-7, 0.72953373) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.6931787, 1.5489788, 0.75133014) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + + over "DiningTableMicrowave" + { + over "Asset" + { + over "body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-9.960064e-15, -7.921141e-14, -4.8424802e-8) + vector3f physics:velocity = (-9.690079e-9, -8.7271457e-10, 2.9132252e-11) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, 2.9588682e-8, 2.9588676e-8, -6.6830154e-8) + float3 xformOp:translate = (2.372676e-8, -1.1580328e-7, 1.3589859e-7) + } + } + } + + over "AirFryer" + { + over "Centered" + { + over "Body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "Tray" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.000011355935, -0.0007472519, 1.0527549e-7) + vector3f physics:velocity = (4.3707382e-7, 1.858865e-9, -0.00007321079) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, 2.121396e-9, -1.413489e-7, 0) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (1.385808001685973e-8, -5.960465454535324e-10, 1.279823464983565e-7) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + } + + over "Tray" ( + prepend apiSchemas = ["MaterialBindingAPI"] + ) + { + rel material:binding = ( + bindMaterialAs = "strongerThanDescendants" + ) + } + } + + def PhysicsRevoluteJoint "ChainNavigation_turn" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:damping = 100000 + float drive:angular:physics:maxForce = 100000 + float drive:angular:physics:stiffness = 0 + float drive:angular:physics:targetVelocity = 0 + uniform token drive:angular:physics:type = "force" + uniform token physics:axis = "Z" + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + float physics:lowerLimit = -170 + float physics:upperLimit = 170 + } + + def PhysicsPrismaticJoint "ChainNavigation_drive" ( + prepend apiSchemas = ["PhysicsDriveAPI:linear"] + ) + { + float drive:linear:physics:damping = 100000 + float drive:linear:physics:maxForce = 100000 + float drive:linear:physics:stiffness = 0 + float drive:linear:physics:targetVelocity = 0 + uniform token drive:linear:physics:type = "force" + uniform token physics:axis = "X" + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + float physics:lowerLimit = -15 + float physics:upperLimit = 15 + } + + def PhysicsFixedJoint "ChainNavigation_hold" + { + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + } + + over "Background" + { + over "Shell" + { + over "KitchenRefrigerator" + { + over "body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "left_door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "right_door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + } + } + } + + over "Looks" + { + def Material "ChainOriginalTray" + { + token outputs:surface.connect = + + def Shader "Surface" + { + uniform token info:id = "UsdPreviewSurface" + color3f inputs:diffuseColor = (0.025, 0.025, 0.025) + float inputs:roughness = 0.5 + token outputs:surface + } + } + } +} + +over "Render" ( + no_delete = true +) +{ + over "OmniverseKit" + { + over "HydraTextures" ( + hide_in_stage_window = true + no_delete = true + ) + { + over "omni_kit_widget_viewport_ViewportTexture_0" ( + hide_in_stage_window = true + no_delete = true + ) + { + rel camera = + bool omni:rtx:rt:ecoMode:enabled = 0 + rel orderedVars = + custom bool overrideClipRange = 0 + uniform int2 resolution = (1280, 720) + } + } + } +} + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene_physical.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene_physical.usda new file mode 100644 index 0000000000000000000000000000000000000000..97a289b4e1b7e663d1225b62def3f88014906ae6 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/scene_physical.usda @@ -0,0 +1,8 @@ +#usda 1.0 +( + subLayers = [ + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/collision_profile.usda@, + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/scene.usda@ + ] +) + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/sim_info.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/sim_info.json new file mode 100644 index 0000000000000000000000000000000000000000..70e89c888054877cc7f3bdfd586ed13d43f05fad --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/sim_info.json @@ -0,0 +1,201 @@ +{ + "action_schema": "g1_omnipicker_jointpos20_basevw_v1", + "physics_hz": 240, + "control_hz": 60, + "training_hz": 20, + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "state_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_x_world_m", + "base_y_world_m", + "base_yaw_world_unwrapped_rad" + ], + "phase_names": [ + "initial_upper_shelf_posture", + "item_00_waist_to_selected_shelf", + "item_00_shelf_pick", + "item_00_shelf_exit_reverse", + "item_00_carry_raise", + "item_00_carry_compact", + "item_00_forward_navigation", + "item_00_bridge_to_place_initial", + "item_00_place_descent", + "item_00_place_release", + "item_00_release_retreat", + "item_00_settle", + "item_00_empty_return", + "item_00_complete", + "item_01_waist_to_selected_shelf", + "item_01_shelf_pick", + "item_01_shelf_exit_reverse", + "item_01_carry_raise", + "item_01_carry_compact", + "item_01_forward_navigation", + "item_01_bridge_to_place_initial", + "item_01_place_descent", + "item_01_place_release", + "item_01_release_retreat", + "item_01_settle", + "item_01_empty_return", + "item_01_complete", + "item_02_waist_to_selected_shelf", + "item_02_shelf_pick", + "item_02_shelf_exit_reverse", + "item_02_carry_raise", + "item_02_carry_compact", + "item_02_forward_navigation", + "item_02_bridge_to_place_initial", + "item_02_place_descent", + "item_02_place_release", + "item_02_release_retreat", + "item_02_settle", + "item_02_complete" + ], + "item_index_semantics": "0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase", + "requested_items": [ + { + "item_index": 0, + "target": "/World/Assets/ShelfItems/coke_c3", + "product": "coke_c3", + "hand": "r", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000108", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/controls_item_01.npz", + "destination_xyz": [ + -1.782109472652953, + 0.2616979300731507, + 0.45490211957462423 + ] + }, + { + "item_index": 1, + "target": "/World/Assets/Tier2Items/oreo_gf", + "product": "oreo_gf", + "hand": "r", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000162", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/controls_item_02.npz", + "destination_xyz": [ + -1.8051634367402825, + 0.19649129567819962, + 0.45490211957462423 + ] + }, + { + "item_index": 2, + "target": "/World/Assets/ShelfItems/sprite_c1", + "product": "sprite_c1", + "hand": "l", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000634", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_02/controls_item_03.npz", + "destination_xyz": [ + -1.6241274714076577, + 0.2575111503297965, + 0.45490211957462423 + ] + } + ], + "initial_tray_items": 0, + "continuous_native_episode": true, + "action_units": [ + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "rad", + "rad", + "rad", + "rad", + "rad", + "m/s", + "rad/s" + ], + "state_units": [ + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "m", + "rad" + ], + "normalization": null, + "time_semantics": "same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll", + "unrecorded_preroll_duration_s": 0.7833333333333333, + "image_generation_mode": "native_live_three_view", + "manual_visual_review_required": false, + "user_visual_acceptance_record": "USER_ACCEPTANCE.json", + "gripper_max_effort_Nm": { + "left": 80, + "right": 80 + }, + "base_policy": "stationary_turn_or_straight_body_forward_v1", + "source_layout": "/mnt/agent-bench-data/lingjun/releases/living50-candidate-20260919/layouts/ID_02_LIVING_STRAIGHT.usdc" +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_geometry.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..3084090fcc78ffc9efe2c5090ff11c5b0b8bd72a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_geometry.py @@ -0,0 +1,118 @@ +"""Use existing exact/native sampled collision gates for this continuous task.""" +import sys,json,os,subprocess +from pathlib import Path +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]).resolve();out=case/'rollout';kind=sys.argv[2] +if kind=='world' and 'CHAIN_AUDIT_SHARD' not in os.environ: + sys.exit(subprocess.call([sys.executable,str(W/'parallel_world_audit.py'),str(case)])) +shard=int(os.environ.get('CHAIN_AUDIT_SHARD','-1')) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop'),str(R/'work/place_training_tray_20260918')] +job=json.loads((case/'job.json').read_text()) +from multi_validation import items +requested=items(job);requested_targets=[row['target'] for row in requested] +# Optional contemporary cooking representations refine conservative envelopes. +# All actual geometry, filters and tolerances remain unchanged. +import collision_scene as cs +cs.STOCK=list(dict.fromkeys(cs.STOCK+job.get('clutter',[])+[path for row in requested for path in row.get('clutter',[])])) +adapter=None +if (out/'collision_readback/RESULT.json').exists(): + import importlib.util,types,numpy as np + from pxr import Usd + path=R/'work/omni_pick_stage1_20260914/native_cooking_readback/nonrobot_coverage/analysis_extension/adapter_extra.py' + spec=importlib.util.spec_from_file_location('chain_captured_geometry',path);module=importlib.util.module_from_spec(spec) + # This task stores scene/readbacks under case/rollout, with runtime root in case. + # Validate exact same-case paths instead of the legacy directory nesting. + source=path.read_text() + legacy="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve().parent!=snapshot.resolve().parent or snapshot.resolve().parent!=self.readback.parent.parent:raise GeometryReview('Readbacks and snapshot belong to different capture directories')" + current="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve()!=_chain_runtime_root or snapshot.resolve()!=_chain_snapshot or self.readback.parent!=_chain_output or self.native_readback.parent!=_chain_output:raise GeometryReview('Readbacks and snapshot do not match this chain capture')" + assert source.count(legacy)==1 + module.__dict__.update(_chain_runtime_root=Path(job['scene']).resolve(),_chain_snapshot=(out/'scene.usda').resolve(),_chain_output=out.resolve()) + exec(compile(source.replace(legacy,current),str(path),'exec'),module.__dict__) + from collision_inventory import enabled_nonrobot_convex_colliders + module.export.enabled_nonrobot_convex_colliders=enabled_nonrobot_convex_colliders + original_normalize=module.normalize_rigid_mount + def normalize_roundoff(prim,current,captured,cache): + try:return original_normalize(prim,current,captured,cache) + except module.GeometryReview: + before=np.asarray(current['collider_to_rigid_owner_column_matrix'],float);after=np.asarray(captured['collider_to_rigid_owner_column_matrix'],float);points=np.asarray(prim.GetAttribute('points').Get(),float) + if points.ndim!=2 or points.shape[1]!=3:raise + coefficient=float(np.max(abs(before-after)));displacement=float(np.linalg.norm((np.c_[points,np.ones(len(points))]@(before-after).T)[:,:3],axis=1).max()) + if coefficient>1e-10 or displacement>1e-9:raise + return dict(method='bounded_float64_matrix_chain_roundoff',normalized_mount=before.tolist(),raw_end_mount=after.tolist(),raw_end_mount_max_coefficient_difference=coefficient,maximum_geometry_displacement_m=displacement,coefficient_bound=1e-10,geometry_displacement_bound_m=1e-9,normalized_model_is_live_PxMeshScale_identity=False,source_geometry_modified=False) + module.normalize_rigid_mount=normalize_roundoff + stage=Usd.Stage.Open(str(out/'scene.usda'));metadata=np.load(out/'full_state.npz',allow_pickle=False) + wrapped,AnalysisBody,adapter=module.build_extended_adapter(stage,out/'collision_readback',out/'collision_readback_nonrobot',metadata) + (out/('COOKED_ADAPTER_COVERAGE.json' if shard<0 else f'COOKED_ADAPTER_COVERAGE.part_{shard}.json')).write_text(json.dumps(adapter.receipt,indent=2)+'\n') + cs.collider_mesh=wrapped;cs.static_collision_mesh=wrapped;cs.Body=AnalysisBody + +if kind=='self': + import omni_native_audit as native + if adapter is not None:native.trimesh=types.SimpleNamespace(util=types.SimpleNamespace(concatenate=adapter.combine)) + native.audit(out/'physics_ticks/full_state.npz',out/'scene.usda',out/'SELF_COLLISION_AUDIT.json') +elif kind=='world': + item_index=int(os.environ['CHAIN_AUDIT_ITEM']);item=requested[item_index] + audit_target=item['target'];audit_hand=item['hand'] + import collision_initial_layout + from place_tray_layout import scene_geometry + from shapely.geometry import Polygon + original_surfaces=collision_initial_layout._surfaces + def place_surfaces(scene): + surfaces=original_surfaces(scene);geometry=scene_geometry(scene.stage,audit_target);top=float(geometry['tray_xyz'][2]+geometry['tray_floor_z']) + assert abs(top-job['tray_floor_z'])<1e-5 + surfaces.append(dict(top=top,polygon=Polygon(geometry['tray_floor']+geometry['tray_xyz'][:2]),paths=['/World/Assets/Tray']));return surfaces + collision_initial_layout._surfaces=place_surfaces + original_support=cs.is_shelf_support_body + cs.is_shelf_support_body=lambda body:original_support(body) or body.name.startswith('/World/Assets/Tray/') + import collision_sweep as sweep + from contact_policy import install + spec=dict(target=dict(prim_path=audit_target),hand=audit_hand) + import collision_scene as cs + original_static=cs.include_static_collision_geometry + def static_inventory(scene): + result=original_static(scene);templates=[];unresolved=[] + for path in result['undefined_active_enabled_static_colliders']: + prim=scene.stage.GetPrimAtPath(path) + if path.startswith('/Flattened_Prototype_') and not prim.IsDefined():templates.append(path) + else:unresolved.append(path) + result['nonphysical_undefined_template_opinions']=templates;result['undefined_active_enabled_static_colliders']=unresolved;result['complete']=not unresolved + return result + cs.include_static_collision_geometry=static_inventory + (out/f'place_metrics.part_{shard}.json').write_text(json.dumps(dict(item_index=item_index,target=audit_target,hand=audit_hand,destination_body_path='/World/Assets/Tray'))) + import inspect,time + install(sweep,spec) + import collision_acceptance + from task_collision_scope import install as install_task_scope + from task_collision_scope import nonphysical_rug_paths + omitted_rug_paths=set(nonphysical_rug_paths(stage)) + install_task_scope(collision_acceptance,requested_targets,omitted_rug_paths) + source=inspect.getsource(sweep.episode) + target_line=" target_name=str(d['target_prim']); source=_SCENE.objects.get(target_name)" + assert source.count(target_line)==1 + source=source.replace(target_line," target_name=_chain_target; source=_SCENE.objects.get(target_name)") + hand_line=" hand='l'" + assert source.count(hand_line)==1 + source=source.replace(hand_line," hand=_chain_hand",1) + support_line=" support_name=place_metadata.get('destination_body_path') or (str(d['current_furniture_body_path']) if 'current_furniture_body_path' in d else '')" + assert source.count(support_line)==1 + source=source.replace(support_line," hand=_chain_hand\n"+support_line) + begin=source.index(' def register(');end=source.index(' frame_ids=',begin) + register=source[begin:end].replace('_OPTIONS.contact_tolerance_mm','contact_limit') + register=register.replace(' nonlocal exact,cache_hits'," nonlocal exact,cache_hits\n if a.name.split('#')[0] in _chain_nonphysical_rug or b.name.split('#')[0] in _chain_nonphysical_rug:return") + sweep.__dict__['_chain_nonphysical_rug']=omitted_rug_paths + marker_limit=" if contact_limit is not None and row['category'].endswith('_requires_depth_review'):" + register=register.replace(marker_limit," contact_limit=_place_contact_limit(row,_OPTIONS.contact_tolerance_mm)\n"+marker_limit) + source=source[:begin]+register+source[end:] + marker=' for fi,f in enumerate(frame_ids):' + source=source.replace(marker,marker+"\n if not _chain_start<=fi<_chain_end:continue\n _chain_checked.append(fi)\n if fi%30==0:_chain_audit_progress(fi,len(frame_ids))") + def progress(frame,total): + path=out/f'WORLD_AUDIT_PROGRESS.part_{shard}.json';tmp=path.with_suffix('.tmp');tmp.write_text(json.dumps(dict(frame=frame,total=total,updated=time.time())));tmp.replace(path) + sweep.__dict__.update(_chain_audit_progress=progress,_chain_start=int(os.environ['CHAIN_AUDIT_START']),_chain_end=int(os.environ['CHAIN_AUDIT_END']),_chain_checked=[],_chain_target=audit_target,_chain_hand=audit_hand) + exec(compile(source,sweep.__file__,'exec'),sweep.__dict__) + from types import SimpleNamespace + from collision_scene import ROBOT + options=SimpleNamespace(scene=str(out/'scene.usda'),robot=str(ROBOT),output=str(out/'world_geometry'/f'part_{shard}'),scene_per_episode=False,authored_active=True,native_robot_world=True,swept=True,all_scene=False,solid_containment=True,stop_on_forbidden=True,depth=True,depth_points=512,contact_tolerance_mm=.5,stride=1,joint_step=.03,target_step=.005) + sweep.init(options);report=sweep.episode(out/'physics_ticks') + from collision_acceptance import summarize + full_report=json.loads(Path(report['output']).read_text());result=full_report['native_world_geometry_summary'] + result.update(partial_report=True,shard=shard,item_index=item_index,active_target=audit_target,active_hand=audit_hand,checked_frame_indices=sweep.__dict__['_chain_checked'],start=int(os.environ['CHAIN_AUDIT_START']),end=int(os.environ['CHAIN_AUDIT_END']),elapsed_seconds=full_report['elapsed_seconds'],source_geometry_report=report['output']) + (out/f'WORLD_COLLISION_AUDIT.part_{shard}.json').write_text(json.dumps(result,indent=2)+'\n') +else:raise ValueError(kind) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_views.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_views.py new file mode 100644 index 0000000000000000000000000000000000000000..805f409f02d042aa070dd0c8760091a2dfb62568 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/audit_views.py @@ -0,0 +1,54 @@ +"""Actual-pose wrist and initial selected-shelf visibility checks.""" +import sys,json,copy +from pathlib import Path +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation as Rot +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]);out=case/'rollout';job=json.loads((case/'job.json').read_text()) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/shelf_training_visible360_20260916'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +from wrist_upright import camera_mount +from wrist_orientation_policy import MIN_CAMERA_HEIGHT_M,MIN_CAMERA_UP_Z +from validate_export import write +from multi_validation import items,recording_contract +requested=items(job);z=np.load(out/'recording_60hz.npz');phases,item_index,_,_=recording_contract(z,requested);phases=np.asarray(phases) +stage=Usd.Stage.Open(str(out/'scene.usda'));paths=z['robot_link_paths'].tolist();wrist={};extrinsics={};samples=np.arange(2,len(z['q']),3) +for key,owner,cam in [('top_head','head_link2','Head_Camera'),('hand_left','gripper_l_base_link','Left_Camera'),('hand_right','gripper_r_base_link','Right_Camera')]: + prim=stage.GetPrimAtPath('/World/Robot/'+owner);camera=stage.GetPrimAtPath(str(prim.GetPath())+'/'+cam);cache=UsdGeom.XformCache();mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(prim)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + p=z['robot_link_pose'][:,paths.index(str(prim.GetPath()))];rotation=Rot.from_quat(p[:,3:]).as_matrix();world=np.tile(np.eye(4),(len(p),1,1));world[:,:3,:3]=rotation;world[:,:3,3]=p[:,:3];camera_world=world@mount;extrinsics[key]=camera_world[samples] + if key!='top_head': + height=(rotation@mount[:3,3])[:,2];up=(rotation@mount[:3,1])[:,2];wrist[key]=dict(passed=bool(height.min()>=MIN_CAMERA_HEIGHT_M and up.min()>=MIN_CAMERA_UP_Z),minimum_height_m=float(height.min()),minimum_up_z=float(up.min()),minimum_height_tick=int(height.argmin()+1),minimum_up_tick=int(up.argmin()+1)) +write(out/'WRIST_AUDIT.json',dict(passed=all(d['passed'] for d in wrist.values()),policy='wrist_camera_upright_v2',hands=wrist,actual_samples=len(z['q']))) +np.savez_compressed(out/'camera_extrinsics_reconstructed_by_view_audit.npz',**extrinsics,source_control_tick=samples+1) +# At the upper default pose a lower label can be out of view by design. Verify +# every requested item after its measured waist adjustment and before grasping. +d=np.load(out/'full_state.npz') +import collision_scene as cs,trimesh +from omni_randomization import full_environment_bodies +from visible_target_check import TargetCheck +robot=cs.Robot(cs.ROBOT);cam=UsdGeom.Camera(stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera'));head_index=paths.index('/World/Robot/head_link2');props=d['rigid_body_paths'].tolist();reports=[] +# The multi-item scene retains a deliberate subset of the distractors from +# several single-item source episodes. Reconstruct visibility from the actual +# captured rigid-body inventory, using source rows only as asset metadata. +object_catalog={} +for request in requested: + for source in json.loads((Path(request['pick_source'])/'episode.json').read_text())['objects']: + object_catalog.setdefault(source['prim_path'],source) +for item_number,item in enumerate(requested): + phase=f'item_{item_number:02d}_waist_to_selected_shelf';found=np.flatnonzero((item_index==item_number)&(phases==phase)) + if not len(found): + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Missing measured waist-to-selected-shelf phase'));continue + frame=int(found[-1]);spec=json.loads((Path(item['pick_source'])/'episode.json').read_text());base=cs.pose(z['base_pose'][frame,:3],z['base_pose'][frame,[6,3,4,5]]) + check=TargetCheck.__new__(TargetCheck);check.stage=stage;check.robot=robot;check.r=dict(spec['robot'],dof_names=z['dof_names'].tolist());check.base=base;check.fixtures=full_environment_bodies(stage);check.camera=cam;check.focal=float(cam.GetFocalLengthAttr().Get());check.ha=float(cam.GetHorizontalApertureAttr().Get());check.va=float(cam.GetVerticalApertureAttr().Get()) + parent=z['robot_link_pose'][frame,head_index];camera=cs.pose(parent[:3],parent[[6,3,4,5]])@cs.matrix(UsdGeom.Xformable(cam).GetLocalTransformation());placed=[];target=None;cache=UsdGeom.XformCache() + current_catalog={source['prim_path']:source for source in spec['objects']} + for prop_index,path in enumerate(props): + source=current_catalog.get(path,object_catalog.get(path)) + if source is None:continue # articulated/static appliances are fixtures + row=copy.deepcopy(source);row['xyz_m']=d['prop_xyz_m'][frame,prop_index].tolist();row['wxyz']=d['prop_wxyz'][frame,prop_index].tolist();prim=stage.GetPrimAtPath(path);ref=cs.rigid_part(cs.matrix(cache.GetLocalToWorldTransform(prim)));parts=cs.mesh_parts(prim,cache,reference=ref);body=cs.Body(path,trimesh.util.concatenate([m for _,m in parts]),cs.pose(row['xyz_m'],row['wxyz']));placed.append((row,[body],None)) + if path==item['target']:target=row + if target is None: + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Target absent from captured rigid-body inventory'));continue + posture=dict(q=z['q'][frame],camera=camera,actual_links={p.replace('/World/Robot',robot.root,1):cs.pose(z['robot_link_pose'][frame,i,:3],z['robot_link_pose'][frame,i,[6,3,4,5]]) for i,p in enumerate(paths)}) + report=check.visibility(target,placed,posture,actual_camera=camera);report.update(item_index=item_number,target=item['target'],frame=frame,source_control_tick=frame+1,phase=phase,source='Actual measured poses immediately before this item pickup in the same uninterrupted episode') + reports.append(report) +write(out/'VISIBILITY_AUDIT.json',dict(passed=all(row.get('passed') for row in reports),items=reports,actual_item_count=len(reports))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/export_user_accepted.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/export_user_accepted.py new file mode 100644 index 0000000000000000000000000000000000000000..9374919e099409df9cad798f8e71d0190f214dd9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/export_user_accepted.py @@ -0,0 +1,325 @@ +"""Export explicitly user-accepted captures while retaining strict diagnostic results. +No automatic physics audit result is rewritten or claimed to pass. +""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + acceptance=json.loads((case/'USER_ACCEPTANCE.json').read_text()) + assert acceptance.get('accepted') is True and acceptance.get('acceptance_basis')=='explicit_user_visual_review' + assert acceptance['recording_sha256']==sha256(out/'recording_60hz.npz') + result=json.loads((out/'RESULT.json').read_text());assert result['status']=='PHYSICS_COMPLETE_UNVERIFIED' + assert json.loads((out/'RETURN_HEAD_AUDIT.json').read_text())['passed'] + physical_verification=dict(passed=False,status='NOT_AUTOMATICALLY_CERTIFIED',acceptance_basis='explicit_user_visual_review',user_acceptance_sha256=sha256(case/'USER_ACCEPTANCE.json')) + frozen=out + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + destination=W/'datasets'/f"layout_{job['layout_id']:02d}";temp=destination.with_name(destination.name+'.pending');assert not destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + if (out/'physical_evidence').exists():shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=False,user_visual_acceptance_record='USER_ACCEPTANCE.json',gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files if (sim/name).exists()},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=False,official_reader_pending=True,acceptance_basis='explicit_user_visual_review',automatic_physical_checks_all_passed=False) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + shutil.copy2(Path(__file__),validation_code/Path(__file__).name) + write(sim/'USER_ACCEPTED_EXPORT_POLICY.json',dict(acceptance_basis='explicit_user_visual_review',strict_diagnostic_failures_retained=True,automatic_physics_certification_claimed=False,export_script_sha256=sha256(Path(__file__)))) + temp.rename(destination);return destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/grasp_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/grasp_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..cae3f6f9bcdba8840ac201d4af1c174740814f22 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/grasp_stability.py @@ -0,0 +1,73 @@ +"""Measured held-object slip and high-frequency hand motion for every item cycle.""" +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np +from scipy.ndimage import uniform_filter1d +from scipy.spatial.transform import Rotation + +from multi_validation import items, recording_contract + + +def _one(z, phases, item_index, target_pose_all, item, index): + carry = { + f"item_{index:02d}_shelf_exit_reverse", + f"item_{index:02d}_carry_raise", + f"item_{index:02d}_carry_compact", + f"item_{index:02d}_forward_navigation", + } + ids = np.flatnonzero((item_index == index) & np.isin(phases, list(carry))) + if len(ids) < 10: + return dict(item_index=index, target=item["target"], passed=False, reason="Missing item navigation samples") + path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + paths = z["robot_link_paths"].tolist() + if path not in paths: + return dict(item_index=index, target=item["target"], passed=False, reason=f"Missing hand link {path}") + hp = z["robot_link_pose"][ids, paths.index(path)] + tp = target_pose_all[ids, index] + bp = z["base_pose"][ids] + relative = Rotation.from_quat(hp[:, 3:]).inv().apply(tp[:, :3] - hp[:, :3]) + slip = np.linalg.norm(relative - relative[0], axis=1) + hand_in_base = Rotation.from_quat(bp[:, 3:]).inv().apply(hp[:, :3] - bp[:, :3]) + residual = hand_in_base - uniform_filter1d(hand_in_base, size=7, axis=0, mode="nearest") + amplitude = np.linalg.norm(residual, axis=1) + relative_hand = Rotation.from_quat(bp[:, 3:]).inv() * Rotation.from_quat(hp[:, 3:]) + angles = (relative_hand[0].inv() * relative_hand).as_rotvec() + angular_residual = np.linalg.norm(angles - uniform_filter1d(angles, size=7, axis=0, mode="nearest"), axis=1) + bad = (amplitude > .005) | (angular_residual > np.deg2rad(3)) + run = longest = 0 + for value in bad: + run = run + 1 if value else 0 + longest = max(longest, run) + return dict( + item_index=index, target=item["target"], hand=item["hand"], + passed=bool(slip.max() <= .025 and longest < 6), native_hz=60, + navigation_samples=len(ids), first_source_control_tick=int(ids[0] + 1), + last_source_control_tick=int(ids[-1] + 1), + maximum_object_in_hand_translation_drift_m=float(slip.max()), + maximum_hand_highpass_residual_m=float(amplitude.max()), + maximum_hand_highpass_angular_residual_rad=float(angular_residual.max()), + angular_highpass_limit_rad=float(np.deg2rad(3)), + hand_highpass_p99_m=float(np.percentile(amplitude, 99)), + maximum_consecutive_highpass_exceedance_ticks=longest, + slip_limit_m=.025, hand_highpass_limit_m=.005, + sustained_threshold_ticks=6, filter_window_ticks=7) + + +def metrics(z, requested): + phases, item_index, _, target_pose_all = recording_contract(z, requested) + phases = np.asarray(phases) + rows = [_one(z, phases, item_index, target_pose_all, item, i) for i, item in enumerate(requested)] + return dict(passed=all(row["passed"] for row in rows), items=rows, + semantics="Measured hand pose relative to moving base and each active target pose relative to its assigned hand; all item carries use the unchanged single-item thresholds.") + + +if __name__ == "__main__": + case = Path(sys.argv[1]); out = case / "rollout"; path = out / "recording_60hz.npz" + job = json.loads((case / "job.json").read_text()) + report = metrics(np.load(path, allow_pickle=False), items(job)) + report["recording_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + (out / "GRASP_STABILITY_AUDIT.json").write_text(json.dumps(report, indent=2) + "\n") + sys.exit(0 if report["passed"] else 1) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..8e26e6957723fc581c9cfeb7fec463e97a03249a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/multi_validation.py @@ -0,0 +1,301 @@ +"""Shared, strict helpers for the uninterrupted multi-item episode contract.""" +from __future__ import annotations + +import gzip +import json +import re +from pathlib import Path + +import numpy as np + + +ITEM_PHASE = re.compile(r"^item_(\d\d)_(.+)$") +CORE_PHASES = ( + "waist_to_selected_shelf", + "shelf_pick", + "shelf_exit_reverse", + "forward_navigation", + "bridge_to_place_initial", + "place_descent", + "place_release", + "release_retreat", + "settle", + "complete", +) +OPTIONAL_PHASES = {"carry_planning", "carry_raise", "carry_compact", "place_planning", "empty_return"} + + +def items(job): + result = job.get("items") + if not isinstance(result, list) or len(result) not in (3, 4): + raise ValueError("job.items must contain exactly three or four requested items") + required = {"target", "hand", "product", "pick_source", "pick_base", "controls_file", "destination_xyz"} + for i, row in enumerate(result): + missing = sorted(required - set(row)) + if missing: + raise ValueError(f"job.items[{i}] missing {missing}") + if row["hand"] not in ("l", "r"): + raise ValueError(f"job.items[{i}].hand is invalid") + targets = [row["target"] for row in result] + if len(set(targets)) != len(targets): + raise ValueError("Every requested target must be distinct") + if int(job.get("initial_tray_items", -1)) != 0: + raise ValueError("The multi-item task requires an initially empty tray") + return result + + +def controls_path(case, item): + path = Path(item["controls_file"]) + if not path.is_absolute(): + path = Path(case) / path + return path.resolve() + + +def controls(case, item): + path = controls_path(case, item) + data = np.load(path, allow_pickle=False) + missing = [key for key in ("pick", "place_initial", "target_local") if key not in data] + if missing: + raise ValueError(f"{path} missing controls arrays {missing}") + return data + + +def decoded(values): + return [x.decode() if isinstance(x, bytes) else str(x) for x in values.tolist()] + + +def recording_contract(z, requested): + n = len(z["q"]) + required = {"phase", "item_index", "active_target_path", "target_paths", "target_pose_all"} + missing = sorted(required - set(z.files)) + if missing: + raise ValueError(f"recording_60hz.npz missing multi-item fields {missing}") + paths = decoded(z["target_paths"]) + expected = [row["target"] for row in requested] + if paths != expected: + raise ValueError(f"target_paths are not in requested order: {paths!r}") + pose = np.asarray(z["target_pose_all"]) + if pose.shape != (n, len(requested), 7) or not np.isfinite(pose).all(): + raise ValueError(f"target_pose_all has invalid shape/value {pose.shape}") + item_index = np.asarray(z["item_index"], dtype=np.int64) + active = decoded(z["active_target_path"]) + if item_index.shape != (n,) or len(active) != n: + raise ValueError("item_index/active_target_path must have one value per control tick") + if np.any((item_index < -1) | (item_index >= len(requested))): + raise ValueError("item_index contains an out-of-range value") + phases = decoded(z["phase"]) + for tick, (index, path, phase) in enumerate(zip(item_index, active, phases)): + match = ITEM_PHASE.match(phase) + if phase == "initial_upper_shelf_posture": + authentic = (index == -1 and path == "") or (index == 0 and path == expected[0]) + if not authentic or any(ITEM_PHASE.match(x) for x in phases[:tick]): + raise ValueError(f"tick {tick} has invalid or non-prefix initial encoding") + continue + if index == -1: + raise ValueError(f"inactive tick {tick} occurs outside the initial posture: {phase}") + if path != expected[index]: + raise ValueError(f"tick {tick} active target does not match item_index") + if not match or int(match.group(1)) != index: + raise ValueError(f"tick {tick} phase is not prefixed for active item: {phase}") + return phases, item_index, active, pose + + +def compressed(values): + return list(dict.fromkeys(values)) + + +def item_phase_indices(phases, item_index, index, suffix=None): + mask = item_index == index + if suffix is not None: + mask &= np.asarray([p == f"item_{index:02d}_{suffix}" for p in phases]) + return np.flatnonzero(mask) + + +def validate_phase_order(phases, item_index, count): + details = [] + matches = [ITEM_PHASE.match(phase) for phase in phases] + first = np.flatnonzero([match is not None for match in matches]) + if not len(first): + raise ValueError("No active item ticks were recorded") + if any(p != "initial_upper_shelf_posture" for p in phases[: first[0]]): + raise ValueError("Only initial_upper_shelf_posture may precede item 0") + seen_items = compressed([int(match.group(1)) for match in matches if match]) + if seen_items != list(range(count)): + raise ValueError(f"item cycles are not continuous and ordered: {seen_items}") + for index in range(count): + indices = np.flatnonzero([(match is not None and int(match.group(1)) == index and item_index[tick] == index) for tick, match in enumerate(matches)]) + suffixes = [] + for tick in indices: + match = ITEM_PHASE.match(phases[tick]) + suffixes.append(match.group(2)) + unique = compressed(suffixes) + unknown = [name for name in unique if name not in set(CORE_PHASES) | OPTIONAL_PHASES] + if unknown: + raise ValueError(f"item {index} has unknown phases {unknown}") + positions = [] + for name in CORE_PHASES: + if name not in unique: + raise ValueError(f"item {index} is missing required phase {name}") + positions.append(unique.index(name)) + if positions != sorted(positions): + raise ValueError(f"item {index} core phases are out of order: {unique}") + has_return = "empty_return" in unique + if index < count - 1 and (not has_return or not (unique.index("settle") < unique.index("empty_return") < unique.index("complete"))): + raise ValueError(f"item {index} must return empty-handed to the next source before completion") + if index == count - 1 and has_return: + raise ValueError("The final item must complete at the table without an extra return") + if indices[-1] - indices[0] + 1 != len(indices): + raise ValueError(f"item {index} cycle is not contiguous") + details.append(dict(item_index=index, phases=unique, first_tick=int(indices[0] + 1), last_tick=int(indices[-1] + 1))) + return details + + +def audit_ranges(z, requested, minimum_shards=4, maximum_shards=16): + """Create target-homogeneous ranges; split long ranges without crossing item cycles.""" + _, item_index, _, _ = recording_contract(z, requested) + active = item_index.copy() + active[active < 0] = 0 + starts = np.r_[0, np.flatnonzero(active[1:] != active[:-1]) + 1] + ends = np.r_[starts[1:], len(active)] + ranges = [[int(a), int(b), int(active[a])] for a, b in zip(starts, ends)] + while len(ranges) < minimum_shards: + eligible = [(b - a, i) for i, (a, b, _) in enumerate(ranges) if b - a >= 2] + if not eligible: + break + _, i = max(eligible) + a, b, item = ranges[i] + middle = (a + b) // 2 + ranges[i : i + 1] = [[a, middle, item], [middle, b, item]] + if not (minimum_shards <= len(ranges) <= maximum_shards): + raise ValueError(f"Could not form {minimum_shards}..{maximum_shards} homogeneous audit shards") + covered = [tick for a, b, _ in ranges for tick in range(a, b)] + if covered != list(range(len(active))): + raise ValueError("Audit ranges do not cover every control tick exactly once") + return ranges + + +def load_contact_rows(path): + opener = gzip.open if str(path).endswith(".gz") else open + with opener(path, "rt") as stream: + return [json.loads(line) for line in stream if line.strip()] + + +def contact_capture_audit(out, requested, control_ticks, item_index=None, active_target=None, phases=None): + receipt_path = Path(out) / "CONTACT_CAPTURE.json" + contacts_path = Path(out) / "contacts.jsonl.gz" + if not receipt_path.exists() or not contacts_path.exists(): + return dict(passed=False, reason="Missing CONTACT_CAPTURE.json or contacts.jsonl.gz") + receipt = json.loads(receipt_path.read_text()) + expected = [row["target"] for row in requested] + rows = load_contact_rows(contacts_path) + captured = set() + invalid = [] + identity_mismatch = [] + for row_number, row in enumerate(rows, 1): + actors = list(map(str, row.get("actors", []))) + for target in expected: + if any(a == target or a.startswith(target + "/") or a.startswith(target + "#") for a in actors): + captured.add(target) + tick = int(row.get("tick", 0)) + if not 1 <= tick <= control_ticks: + invalid.append(row_number) + elif item_index is not None and active_target is not None: + if (int(row.get("item_index", -999)) != int(item_index[tick-1]) + or str(row.get("active_target_path", "")) != str(active_target[tick-1]) + or (phases is not None and str(row.get("phase", "")) != str(phases[tick-1]))): + identity_mismatch.append(row_number) + passed = ( + receipt.get("requested_targets") == expected + and receipt.get("filter_semantics") == "robot_or_any_requested_target" + and receipt.get("all_physics_ticks") is True + and int(receipt.get("dropped_rows", -1)) == 0 + and int(receipt.get("control_ticks", -1)) == control_ticks + and int(receipt.get("callback_rows", -1)) == len(rows) + and captured == set(expected) + and not invalid + and not identity_mismatch + ) + return dict(passed=passed, receipt=receipt, contact_rows=len(rows), targets_with_recorded_contacts=sorted(captured), invalid_tick_rows=invalid, active_item_identity_mismatch_rows=identity_mismatch) + + +def grasp_retention_audit(contact_rows, phases, item_index, requested): + """Reject height-only pickups without a retained, unsupported pinch. + + Contact callbacks can repeat within one 60 Hz control tick, so this gate + counts distinct control ticks. It mirrors the future producer gate while + remaining usable for already-running frozen snapshots that lack PICK_GATE + receipts. + """ + phases = np.asarray(phases) + item_index = np.asarray(item_index, dtype=np.int64) + details = [] + passed = True + + def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + for index, item in enumerate(requested): + target = str(item["target"]) + hand = str(item["hand"]) + pick = item_phase_indices(phases, item_index, index, "shelf_pick") + if not len(pick): + details.append(dict(item_index=index, target=target, passed=False, reason="missing shelf_pick phase")) + passed = False + continue + end_tick = int(pick[-1] + 1) + window_start = max(int(pick[0] + 1), end_tick - 29) + tail_start = max(window_start, end_tick - 2) + environment_start = max(int(pick[0] + 1), end_tick - 14) + prefix = f"/World/Robot/gripper_{hand}" + inner_path = prefix + "_inner_link4" + outer_path = prefix + "_outer_link4" + inner = set() + outer = set() + environment = set() + for row in contact_rows: + tick = int(row.get("tick", 0)) + if tick < min(window_start, environment_start) or tick > end_tick: + continue + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(target, values): + continue + if tick >= window_start: + if touches(inner_path, values): + inner.add(tick) + if touches(outer_path, values): + outer.add(tick) + if tick >= environment_start: + if any(not (value == target or value.startswith(target + "/") or value.startswith(target + "#") + or value == "/World/Robot" or value.startswith("/World/Robot/")) for value in values): + environment.add(tick) + bilateral = sorted(inner & outer) + minimum = max(15, int(item.get("pick_grasp_contact_min_bilateral_ticks", 15))) + tail = [tick for tick in bilateral if tick >= tail_start] + item_passed = len(bilateral) >= minimum and bool(tail) and not environment + passed &= item_passed + details.append(dict( + item_index=index, + target=target, + active_hand=hand, + gripper_prefix=prefix, + shelf_pick_tick_range=[int(pick[0] + 1), end_tick], + window_control_ticks=30, + window_start_tick=window_start, + window_end_tick=end_tick, + inner_contact_ticks=sorted(inner), + outer_contact_ticks=sorted(outer), + simultaneous_bilateral_contact_ticks=bilateral, + simultaneous_bilateral_contact_tick_count=len(bilateral), + minimum_simultaneous_bilateral_ticks=minimum, + final_tail_control_ticks=3, + final_tail_bilateral_ticks=tail, + environment_clear_control_ticks=15, + environment_contact_ticks=sorted(environment), + passed=item_passed, + )) + return dict( + schema="ziche.multi_item.grasp_retention_audit.v1", + passed=bool(passed), + semantics="distinct 60 Hz control ticks; sustained opposing active-finger contact; final-tail contact; no target/source-or-clutter contact", + items=details, + ) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..16e80f9d57784e3aa5cd5a3b5593dfe191614a5b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +"""Check one staged multi-item episode with the official LeRobot reader.""" +import argparse +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +CAMERAS = { + "observation.images.top_head": (3, 800, 1280), + "observation.images.hand_left": (3, 480, 848), + "observation.images.hand_right": (3, 480, 848), +} + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + info = json.loads((args.dataset / "meta/info.json").read_text()) + expected = int(info["total_frames"]) + parquet_path = args.dataset / "data/chunk-000/episode_000000.parquet" + table = pq.read_table(parquet_path) + dataset = LeRobotDataset(f"local/shelf_to_table_multi10_stable_layout_{args.layout:02d}", + root=args.dataset, video_backend="pyav") + indices = sorted(set((0, expected // 2, expected - 1))) + samples, failures = [], [] + for index in indices: + item = dataset[index] + action_expected = np.asarray(table["action"][index].as_py(), dtype=np.float32) + state_expected = np.asarray(table["observation.state"][index].as_py(), dtype=np.float32) + action_actual = item["action"].detach().cpu().numpy() + state_actual = item["observation.state"].detach().cpu().numpy() + scalars = { + "frame_index": int(item["frame_index"]) == int(table["frame_index"][index].as_py()), + "episode_index": int(item["episode_index"]) == 0, + "source_control_tick": int(item["source_control_tick"]) == int(table["source_control_tick"][index].as_py()), + "item_index": int(item["item_index"]) == int(table["item_index"][index].as_py()), + "phase_index": int(item["phase_index"]) == int(table["phase_index"][index].as_py()), + "timestamp": abs(float(item["timestamp"]) - float(table["timestamp"][index].as_py())) <= 1e-6, + "simulator_time_s": abs(float(item["simulator_time_s"]) - float(table["simulator_time_s"][index].as_py())) <= 2e-6, + } + images = {} + for key, shape in CAMERAS.items(): + value = item[key] + passed = (tuple(value.shape) == shape and bool(torch.isfinite(value).all()) + and float(value.min()) >= 0.0 and float(value.max()) <= 1.0) + images[key] = {"passed": passed, "shape": list(value.shape), + "minimum": float(value.min()), "maximum": float(value.max()), + "mean": float(value.mean()), "standard_deviation": float(value.std())} + passed = (np.array_equal(action_actual, action_expected) and np.array_equal(state_actual, state_expected) + and all(scalars.values()) and all(row["passed"] for row in images.values()) + and bool(item["task"])) + row = {"index": index, "passed": passed, "action_shape": list(action_actual.shape), + "state_shape": list(state_actual.shape), "scalar_alignment": scalars, + "task": item["task"], "images": images} + samples.append(row) + if not passed: + failures.append(row) + passed = (len(dataset) == expected == len(table) and dataset.fps == 20 and not failures + and samples[0]["action_shape"] == [22] and samples[0]["state_shape"] == [23]) + report = {"schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": datetime.now(timezone.utc).isoformat(), "layout_id": args.layout, + "passed": passed, "dataset": str(args.dataset), + "reader": "lerobot LeRobotDataset(video_backend='pyav')", + "dataset_length": len(dataset), "parquet_rows": len(table), + "metadata_frames": expected, "fps": dataset.fps, "sample_indices": indices, + "samples": samples, "failures": failures, "parquet_sha256": sha(parquet_path), + "note": "First/middle/last rows and all three images decoded through the official reader; full sequential decode is reused by exact video hashes."} + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps({"output": str(args.output), "passed": passed}, indent=2)) + raise SystemExit(0 if passed else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/parallel_world_audit.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/parallel_world_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..727d57ad220f91d4ea9c9e6049b06ff6b51524c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/parallel_world_audit.py @@ -0,0 +1,57 @@ +"""Disjoint control-tick shards; every interval retains its original next tick.""" +import sys,os,time,json,subprocess,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);out=case/'rollout' +from task_collision_scope import POLICY +from multi_validation import audit_ranges,items +shard_count=int(os.environ.get('CHAIN_WORLD_AUDIT_SHARDS','4'));assert 4<=shard_count<=16 +job=json.loads((case/'job.json').read_text());requested=items(job) +start=time.monotonic();z=np.load(out/'recording_60hz.npz',allow_pickle=False);n=len(z['q']);ranges=audit_ranges(z,requested,shard_count,16);shard_count=len(ranges);processes=[];logs=[] +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +try: + for i,(a,b,item_index) in enumerate(ranges): + for name in [f'WORLD_COLLISION_AUDIT.part_{i}.json',f'WORLD_AUDIT_PROGRESS.part_{i}.json']: + p=out/name + if p.exists():p.rename(out/(name+'.previous_'+str(time.time_ns()))) + env=dict(os.environ,CHAIN_AUDIT_SHARD=str(i),CHAIN_AUDIT_START=str(a),CHAIN_AUDIT_END=str(b),CHAIN_AUDIT_ITEM=str(item_index),OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + log=(out/f'world_shard_{i}.log').open('w');logs.append(log);processes.append(subprocess.Popen([sys.executable,str(W/'audit_geometry.py'),str(case),'world'],env=env,stdout=log,stderr=subprocess.STDOUT)) + while any(p.poll() is None for p in processes): + if time.monotonic()-start>1050:raise TimeoutError('World audit exceeded 17.5 minutes; review required, never auto-pass') + checked=0 + for i,(a,b,item_index) in enumerate(ranges): + p=out/f'WORLD_COLLISION_AUDIT.part_{i}.json';progress=out/f'WORLD_AUDIT_PROGRESS.part_{i}.json' + if p.exists(): + part=json.loads(p.read_text());checked+=len(part['checked_frame_indices']) + if part['geometry_status']=='REJECT' and part['hard_failures']: + write(out/'WORLD_COLLISION_AUDIT.json',dict(schema='shelf_multi10.parallel_task_collision_summary.v2',geometry_status='REJECT',collision_scope=POLICY,requested_targets=[x['target'] for x in requested],all_scene_pairs_checked=False,complete_trajectory_checked=False,early_rejection=True,hard_failures=part['hard_failures'],unresolved_review=part['unresolved_review'],elapsed_seconds=time.monotonic()-start,source_shard=i,source_item_index=item_index)) + print('Early rejected by physical collision witness in shard',i,flush=True);sys.exit(0) + elif progress.exists():checked+=max(0,min(int(b-a),json.loads(progress.read_text())['frame']-int(a)+1)) + write(out/'WORLD_AUDIT_PROGRESS.json',dict(frame=checked,total=n,updated=time.time(),parallel_shards=shard_count,elapsed_seconds=time.monotonic()-start)) + time.sleep(2) + results=[] + for i,p in enumerate(processes): + if p.returncode:raise RuntimeError(f'Collision shard {i} failed; see world_shard_{i}.log') + results.append(json.loads((out/f'WORLD_COLLISION_AUDIT.part_{i}.json').read_text())) + failures=[];review=[];contacts=[];covered=[] + for i,r in enumerate(results): + a,b,item_index=ranges[i] + indices=r['checked_frame_indices'];assert len(set(indices))==len(indices) + assert all(int(a)<=x str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + pending = path.with_suffix(path.suffix + ".pending") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def _load_state(out: Path): + path = out / "full_state.npz" + if not path.is_file(): + path = out / "physics_ticks/full_state.npz" + return path, np.load(path, allow_pickle=False) + + +def _normalize_quaternion(q): + q = np.asarray(q, dtype=np.float64) + norm = np.linalg.norm(q, axis=-1, keepdims=True) + if np.any(norm < 1e-15): + raise ValueError("zero quaternion") + return q / norm + + +def angular_from_reference(q, reference): + q = _normalize_quaternion(q) + reference = _normalize_quaternion(np.asarray(reference, dtype=np.float64)) + dots = np.clip(np.abs(np.sum(q * reference, axis=-1)), 0.0, 1.0) + return 2.0 * np.arccos(dots) + + +def pairwise_span(pose): + pose = np.asarray(pose, dtype=np.float64) + if pose.shape[0] < 1 or pose.shape[1] < 1: + return 0.0, 0.0 + xyz = pose[:, :, :3] + q = _normalize_quaternion(pose[:, :, 3:]) + translation = float(np.linalg.norm(xyz[:, None] - xyz[None, :], axis=-1).max()) + dots = np.clip(np.abs(np.einsum("tni,sni->tsn", q, q)), 0.0, 1.0) + rotation = float((2.0 * np.arccos(dots)).max()) + return translation, rotation + + +def clock_audit(out: Path, recording, preroll_npz) -> dict: + out = Path(out) + path = out / "PHYSICS_CLOCK.json" + failures = [] + try: + clock = json.loads(path.read_text()) + except Exception as exc: + return {"schema": "multi_startup_clock_audit_v1", "passed": False, + "failures": ["missing_or_invalid_clock"], "error": repr(exc)} + rows = len(recording["q"]) + preroll_rows = len(preroll_npz["q"]) + expected_steps = 4 * preroll_rows + checks = { + "schema": clock.get("schema") == "startup_preroll_clock_v1", + "rates": clock.get("physics_hz") == 240 and clock.get("control_hz") == 60 + and clock.get("training_hz") == 20 and clock.get("substeps_per_control") == 4, + "unrecorded_preroll_steps": clock.get("unrecorded_preroll_physics_steps") == expected_steps, + "unrecorded_preroll_duration": abs(float(clock.get("unrecorded_preroll_duration_s", -1)) + - expected_steps / 240.0) <= 1e-12, + "retained_rows": clock.get("retained_control_rows") == rows + and clock.get("retained_first_control_row") == 0, + "first_global_step": clock.get("retained_first_post_step_global_physics_step") == expected_steps + 4, + "timestamp_formula": clock.get("retained_timestamp_formula") == "(row_index+1)/60", + "global_step_formula": clock.get("retained_post_step_global_physics_step_formula") + == "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "camera_formula": clock.get("retained_camera_row_formula") == "control_tick % 3 == 0" + and clock.get("retained_camera_frame_count") == rows // 3, + "command_semantics": bool(clock.get("command_semantics")), + "contact_semantics": bool(clock.get("contact_semantics")), + "recording_tick": np.array_equal(recording["tick"], np.arange(1, rows + 1)), + "recording_timestamp": np.allclose(recording["timestamp"], np.arange(1, rows + 1) / 60.0, + atol=1e-12, rtol=0), + } + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_startup_clock_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "clock": clock, + "clock_sha256": sha256(path), "retained_rows": rows, + "preroll_rows_60hz": preroll_rows, + } + + +def _pose_matrix_xyzw(pose): + from scipy.spatial.transform import Rotation as Rot + pose = np.asarray(pose, dtype=np.float64) + matrices = np.zeros(pose.shape[:-1] + (4, 4), dtype=np.float64) + matrices[..., 3, 3] = 1.0 + matrices[..., :3, :3] = Rot.from_quat(pose[..., 3:]).as_matrix() + matrices[..., :3, 3] = pose[..., :3] + return matrices + + +def camera_extrinsics_audit(out: Path, recording, clock: dict) -> dict: + out = Path(out) + path = out / "camera_extrinsics_20hz.npz" + failures = [] + try: + camera = np.load(path, allow_pickle=False) + required = {"camera_keys", "control_tick", "timestamp", "global_physics_step", "world_T_camera"} + missing = sorted(required - set(camera.files)) + if missing: + raise ValueError(f"missing keys {missing}") + keys = camera["camera_keys"].tolist() + ticks = np.asarray(camera["control_tick"], dtype=np.int64) + timestamp = np.asarray(camera["timestamp"], dtype=np.float64) + global_steps = np.asarray(camera["global_physics_step"], dtype=np.int64) + transforms = np.asarray(camera["world_T_camera"], dtype=np.float64) + except Exception as exc: + return {"schema": "multi_camera_extrinsics_audit_v1", "passed": False, + "failures": ["missing_or_invalid_camera_extrinsics"], "error": repr(exc)} + rows = len(recording["q"]) + expected_ticks = np.arange(3, rows + 1, 3, dtype=np.int64) + preroll_steps = int(clock.get("unrecorded_preroll_physics_steps", -1)) + checks = { + "camera_order": keys == list(CAMERAS), + "shape": transforms.shape == (len(expected_ticks), 3, 4, 4), + "control_tick_mapping": np.array_equal(ticks, expected_ticks), + "timestamp_mapping": np.allclose(timestamp, expected_ticks / 60.0, atol=1e-12, rtol=0), + "global_step_mapping": np.array_equal(global_steps, preroll_steps + 4 * expected_ticks), + "finite": bool(np.isfinite(transforms).all()), + "homogeneous_row": bool(np.allclose(transforms[..., 3, :], [0, 0, 0, 1], atol=1e-10)), + } + if checks["shape"] and checks["finite"]: + rotation = transforms[..., :3, :3] + identity_error = float(np.max(np.abs(rotation.swapaxes(-1, -2) @ rotation - np.eye(3)))) + determinant_error = float(np.max(np.abs(np.linalg.det(rotation) - 1.0))) + else: + identity_error = determinant_error = float("inf") + checks["rigid_rotation"] = identity_error <= 1e-5 and determinant_error <= 1e-5 + + # Prove every camera stays rigidly mounted to the recorded robot link at the + # exact control row associated with that encoded frame. The authored mount + # can differ by camera, but it must be constant throughout the episode. + mount_errors = {} + checks["rigid_mount_alignment"] = False + if checks["shape"] and checks["control_tick_mapping"]: + paths = recording["robot_link_paths"].tolist() + try: + link_index = [paths.index(CAMERA_LINKS[key]) for key in keys] + link_pose = np.asarray(recording["robot_link_pose"], dtype=np.float64)[ticks - 1][:, link_index] + world_T_link = _pose_matrix_xyzw(link_pose) + relative = np.linalg.inv(world_T_link) @ transforms + for index, key in enumerate(keys): + reference = relative[0, index] + translation = float(np.linalg.norm(relative[:, index, :3, 3] - reference[:3, 3], axis=1).max()) + from scipy.spatial.transform import Rotation as Rot + delta = Rot.from_matrix(reference[:3, :3]).inv() * Rot.from_matrix(relative[:, index, :3, :3]) + rotation = float(delta.magnitude().max()) + mount_errors[key] = {"translation_m": translation, "rotation_rad": rotation} + checks["rigid_mount_alignment"] = all( + row["translation_m"] <= 1e-5 and row["rotation_rad"] <= 1e-5 + for row in mount_errors.values()) + except Exception as exc: + mount_errors["error"] = repr(exc) + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_camera_extrinsics_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "frames": len(expected_ticks), + "rotation_orthogonality_max_error": identity_error, + "rotation_determinant_max_error": determinant_error, + "mount_errors": mount_errors, "camera_extrinsics_sha256": sha256(path), + } + + +def camera_warmup_audit(out: Path, preroll_evidence: dict, result: dict) -> dict: + """Require native three-view capture to start only after stable priming.""" + out = Path(out) + path = out / "CAMERA_WARMUP.json" + failures = [] + try: + report = json.loads(path.read_text()) + final = report.get("final_stability_window", {}) + startup = out / "startup_frames" + names = sorted(p.name for p in startup.glob("*.jpg")) + expected = sorted(f"{camera}_{index:02d}.jpg" for camera in CAMERAS for index in range(10)) + checks = { + "native_rendered": result.get("rendered") is True, + "status": report.get("status") == "PASSED", + "after_stability": report.get("after_physics_stability") is True, + "render_warmups": int(report.get("render_only_warmups", -1)) >= 12, + "discarded_reads": int(report.get("discarded_render_reads", -1)) >= 3, + "unrecorded_hold_samples": int(report.get("unrecorded_hold_samples", -1)) >= 3, + "preroll_hold_accounting": int(preroll_evidence.get("camera_warmup_physics_samples", -1)) + == int(report.get("unrecorded_hold_samples", -2)), + "no_captured_warmup_frames": int(report.get("captured_frames", -1)) == 0, + "final_prop_stable": float(final.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera_stable": float(final.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven_stable": float(final.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "startup_frame_set": names == expected, + "startup_frames_nonempty": names == expected and all((startup / name).stat().st_size > 0 for name in names), + } + failures.extend(name for name, passed in checks.items() if not passed) + except Exception as exc: + return {"schema": "multi_camera_warmup_audit_v1", "passed": False, + "checks": {}, "failures": ["missing_or_invalid_camera_warmup"], "error": repr(exc)} + return {"schema": "multi_camera_warmup_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "receipt": report, + "receipt_sha256": sha256(path), + "startup_frame_sha256": {name: sha256(out / "startup_frames" / name) for name in names}} + + +def _projection_audit(out: Path, pre_path: Path, state_path: Path) -> dict: + path = out / "PASSIVE_FINGER_PROJECTION.json" + try: + report = json.loads(path.read_text()) + windows = {row.get("label"): row for row in report.get("windows", [])} + required = {"final_preroll_0p5s", "first_retained_30_control_rows"} + implementation = Path(report["projection_implementation"]) + checks = { + "status": report.get("status") == "PASSED" and report.get("passed") is True, + "threshold": report.get("threshold_px") == STRICT["passive_finger_projected_motion_px"], + "scope": report.get("only_passive_distal_fingers") is True, + "windows": set(windows) == required and all( + row.get("passed") is True + and row.get("passive_projection", {}).get("status") == "PASSED" + and 0 <= float(row.get("passive_projection", {}).get("max_projected_motion_px", 1)) < 0.25 + for row in windows.values()), + "bodies": bool(report.get("evaluated_passive_paths")) and bool(report.get("evaluated_driven_paths")), + "cameras": len(report.get("evaluated_cameras", [])) == 3, + "implementation_hash": implementation.is_file() + and report.get("projection_implementation_sha256") == sha256(implementation), + } + failures = [name for name, passed in checks.items() if not passed] + return {"passed": not failures, "checks": checks, "failures": failures, + "receipt": report, "receipt_sha256": sha256(path), "windows": windows, + "independently_bound_pose_sha256": { + pre_path.name: sha256(pre_path), state_path.name: sha256(state_path)}} + except Exception as exc: + return {"passed": False, "checks": {}, "failures": ["missing_or_invalid_projection"], + "error": repr(exc), "windows": {}} + + +def startup_stability_audit(case: Path, recording, action_names) -> dict: + case = Path(case) + out = case / "rollout" + pre_json_path = out / "PREROLL_EVIDENCE.json" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + failures = [] + try: + evidence = json.loads(pre_json_path.read_text()) + pre = np.load(pre_path, allow_pickle=False) + except Exception as exc: + return {"schema": "multi_startup_stability_audit_v1", "passed": False, + "failures": ["missing_preroll_evidence"], "error": repr(exc)} + projection = _projection_audit(out, pre_path, state_path) + rows = min(30, len(recording["q"])) + paths = recording["robot_link_paths"].tolist() + try: + camera_indices = [paths.index(path) for path in CAMERA_LINKS.values()] + driven_indices = [index for index, path in enumerate(paths) if not PASSIVE_FINGER.match(path)] + retained_links = np.asarray(recording["robot_link_pose"][:rows], dtype=np.float64) + retained_props = np.concatenate([ + np.asarray(state["prop_xyz_m"][:rows], dtype=np.float64), + np.asarray(state["prop_wxyz"][:rows, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + prop_translation, prop_rotation = pairwise_span(retained_props) + camera_translation, camera_rotation = pairwise_span(retained_links[:, camera_indices]) + driven_translation, driven_rotation = pairwise_span(retained_links[:, driven_indices]) + except Exception as exc: + prop_translation = prop_rotation = camera_translation = camera_rotation = float("inf") + driven_translation = driven_rotation = float("inf") + failures.append("retained_pose_metrics") + metric_error = repr(exc) + else: + metric_error = None + first_projection = projection.get("windows", {}).get("first_retained_30_control_rows", {}) + retained_checks = { + "rows": rows == 30, + "prop_translation": prop_translation < STRICT["prop_translation_m"], + "prop_rotation": prop_rotation < STRICT["prop_rotation_rad"], + "camera_translation": camera_translation < STRICT["camera_translation_m"], + "camera_rotation": camera_rotation < STRICT["camera_rotation_rad"], + "driven_translation": driven_translation < STRICT["driven_robot_translation_m"], + "driven_rotation": driven_rotation < STRICT["driven_robot_rotation_rad"], + "passive_projection": first_projection.get("passed") is True, + } + failures.extend("first_retained_" + name for name, passed in retained_checks.items() if not passed) + thresholds = evidence.get("thresholds", {}) + final_window = evidence.get("final_window", {}) + names = recording["dof_names"].tolist() + try: + command_indices = [names.index(name) for name in action_names[:20]] + exact_initial = np.array_equal(np.asarray(pre["initial_command"], dtype=np.float64), + np.asarray(recording["native_target"][0, command_indices], dtype=np.float64)) + retained_initial_hold = (rows == 30 + and np.array_equal(np.asarray(recording["native_target"][:rows, command_indices], dtype=np.float64), + np.repeat(np.asarray(pre["initial_command"], dtype=np.float64)[None], rows, axis=0)) + and np.array_equal(np.asarray(recording["base_action_body"][:rows], dtype=np.float64), + np.zeros((rows, 2), dtype=np.float64)) + and set(np.asarray(recording["phase"][:rows]).astype(str).tolist()) == {"initial_upper_shelf_posture"} + and set(np.asarray(recording["item_index"][:rows], dtype=np.int64).tolist()).issubset({-1, 0})) + except Exception: + exact_initial = False + retained_initial_hold = False + preroll_checks = { + "status": evidence.get("status") == "PASSED", + "rates": evidence.get("physics_hz") == 240 and evidence.get("sample_hz") == 60, + "unrecorded": evidence.get("recorded_rows") == 0 + and evidence.get("contact_capture_started") is False + and evidence.get("camera_capture_started") is False, + "no_pose_writes": evidence.get("no_pose_writes_or_resets") is True, + "duration": float(evidence.get("duration_s", 0)) >= 0.5, + "thresholds": thresholds.get("prop_translation_m") == STRICT["prop_translation_m"] + and thresholds.get("prop_rotation_rad") == STRICT["prop_rotation_rad"] + and thresholds.get("camera_translation_m") == STRICT["camera_translation_m"] + and thresholds.get("camera_rotation_rad") == STRICT["camera_rotation_rad"] + and thresholds.get("passive_finger_projected_motion_px") == 0.25, + "final_prop": float(final_window.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final_window.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera": float(final_window.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final_window.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven": float(final_window.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final_window.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "exact_initial_command": exact_initial, + "first_retained_30_exact_initial_hold": retained_initial_hold, + "projection": projection["passed"], + } + failures.extend("preroll_" + name for name, passed in preroll_checks.items() if not passed) + return { + "schema": "multi_startup_stability_audit_v1", "passed": not failures, + "failures": failures, "strict_thresholds": STRICT, + "retained_first_30_control_rows": { + "checks": retained_checks, "prop_translation_span_m": prop_translation, + "prop_rotation_span_rad": prop_rotation, + "camera_translation_span_m": camera_translation, + "camera_rotation_span_rad": camera_rotation, + "driven_translation_span_m": driven_translation, + "driven_rotation_span_rad": driven_rotation, + "metric_error": metric_error, + }, + "preroll_checks": preroll_checks, "preroll_evidence": evidence, + "projection_audit": projection, + "evidence_sha256": {"PREROLL_EVIDENCE.json": sha256(pre_json_path), + "PREROLL_EVIDENCE.npz": sha256(pre_path), + state_path.name: sha256(state_path)}, + } + + +def prop_no_fall_audit(case: Path, recording, requested: list[dict]) -> dict: + """Audit every prop from the authored pose over its physically relevant scopes. + + Non-target props cover the entire preroll and episode. Requested targets + cover preroll + all rows before their own shelf_pick, and then the whole + interval from their first settle row through episode end. The manipulation + interval is intentionally excluded; placement gates cover it separately. + """ + case = Path(case) + out = case / "rollout" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + try: + pre = np.load(pre_path, allow_pickle=False) + pre_paths = pre["prop_paths"].tolist() + initial = np.asarray(pre["initial_prop_pose_before_preroll"], dtype=np.float64) + pre_pose = np.asarray(pre["prop_pose"], dtype=np.float64) + full_paths = state["rigid_body_paths"].tolist() + if len(set(pre_paths)) != len(pre_paths) or set(pre_paths) != set(full_paths): + raise ValueError("preroll/full-state prop inventory mismatch") + order = [full_paths.index(path) for path in pre_paths] + retained = np.concatenate([ + np.asarray(state["prop_xyz_m"][:, order], dtype=np.float64), + np.asarray(state["prop_wxyz"][:, order][:, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + except Exception as exc: + return {"schema": "multi_prop_no_fall_audit_v1", "passed": False, + "failures": ["missing_or_invalid_prop_evidence"], "error": repr(exc)} + producer_path = out / "INITIAL_PROP_NO_FALL.json" + try: + producer = json.loads(producer_path.read_text()) + maxima = producer.get("maxima", {}) + producer_checks = { + "passed": producer.get("passed") is True, + "all_props": producer.get("all_requested_and_nonrequested_props_included") is True, + "all_preroll_samples": int(producer.get("physics_samples", -1)) == len(pre_pose), + "translation": float(maxima.get("translation_m", 1)) <= NO_FALL["max_translation_m"], + "downward": float(maxima.get("downward_m", 1)) <= NO_FALL["max_downward_m"], + "rotation": float(maxima.get("rotation_rad", 1)) <= np.deg2rad(NO_FALL["max_rotation_deg"]), + } + except FileNotFoundError: + producer = {"present": False, "semantics": "optional producer fail-fast; independent full trace remains mandatory"} + producer_checks = {"optional_receipt_absent": True} + except Exception as exc: + producer = {"present": True, "error": repr(exc)} + producer_checks = {"receipt_valid_if_present": False} + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + try: + events = json.loads((out / "ITEM_EVENTS.json").read_text()).get("events", []) + except Exception: + events = [] + target_to_index = {row["target"]: index for index, row in enumerate(requested)} + failures = [] + rows = [] + + def segment_metrics(trace, reference): + trace = np.asarray(trace, dtype=np.float64) + if len(trace) == 0: + return {"rows": 0, "max_translation_m": float("inf"), + "max_downward_m": float("inf"), "max_rotation_deg": float("inf"), "passed": False} + translation = np.linalg.norm(trace[:, :3] - reference[:3], axis=1) + rotation = np.degrees(angular_from_reference(trace[:, 3:], reference[3:])) + metrics = { + "rows": len(trace), "max_translation_m": float(translation.max()), + "max_downward_m": float(max(0.0, reference[2] - trace[:, 2].min())), + "max_rotation_deg": float(rotation.max()), + } + metrics["passed"] = (metrics["max_translation_m"] <= NO_FALL["max_translation_m"] + and metrics["max_downward_m"] <= NO_FALL["max_downward_m"] + and metrics["max_rotation_deg"] <= NO_FALL["max_rotation_deg"]) + return metrics + + for prop_index, path in enumerate(pre_paths): + first = pre_pose[0, prop_index] + authored = initial[prop_index] + supplemental = { + "authored_to_first_translation_m": float(np.linalg.norm(first[:3] - authored[:3])), + "authored_to_first_downward_m": float(max(0.0, authored[2] - first[2])), + "authored_to_first_rotation_deg": float(np.degrees(angular_from_reference(first[None, 3:], authored[3:]))[0]), + } + supplemental_checks = {name: supplemental[name] <= NO_FALL[name] for name in supplemental} + segments = [] + if path not in target_to_index: + trace = np.concatenate([pre_pose[:, prop_index], retained[:, prop_index]], axis=0) + segments.append({"scope": "full_preroll_and_capture", **segment_metrics(trace, authored)}) + else: + index = target_to_index[path] + pick = np.flatnonzero((item_index == index) & (phases == f"item_{index:02d}_shelf_pick")) + completed = [row for row in events if row.get("event") == "item_completed" + and row.get("item_index") == index and row.get("target") == path] + if len(pick): + pre_pick = np.concatenate([pre_pose[:, prop_index], retained[:int(pick[0]), prop_index]], axis=0) + segments.append({"scope": "preroll_and_rows_before_own_pick", **segment_metrics(pre_pick, authored)}) + else: + segments.append({"scope": "preroll_and_rows_before_own_pick", "rows": 0, "passed": False, + "reason": "missing own shelf_pick"}) + if len(completed) == 1 and 1 <= int(completed[0].get("tick", 0)) <= len(retained): + start = int(completed[0]["tick"]) - 1 + post = retained[start:, prop_index] + segments.append({"scope": "item_completed_through_episode_end", + "source_control_tick_start": start + 1, + **segment_metrics(post, post[0])}) + else: + segments.append({"scope": "item_completed_through_episode_end", "rows": 0, "passed": False, + "reason": "missing or ambiguous item_completed event"}) + passed = all(supplemental_checks.values()) and all(row.get("passed") is True for row in segments) + if not passed: + failures.append(path) + rows.append({"path": path, "requested_item_index": target_to_index.get(path), + "authored_to_first_preroll": supplemental, + "authored_to_first_checks": supplemental_checks, + "segments": segments, "passed": passed}) + producer_passed = all(producer_checks.values()) + if not producer_passed: + failures.append("producer_initial_prop_no_fall_receipt") + return { + "schema": "multi_prop_no_fall_audit_v1", "passed": not failures, + "thresholds": NO_FALL, "quaternions_normalized_float64": True, + "reference": "authored/reset pose captured immediately before the first unrecorded preroll physics step", + "non_target_scope": "every preroll sample and every retained control row through episode end", + "requested_target_scope": "preroll and retained rows before its own pick, plus item_completed through episode end", + "manipulation_scope": "excluded here and independently covered by lift/grasp/place/support/collision gates", + "paths": rows, "failures": failures, "producer_initial_prop_no_fall": producer, + "producer_initial_prop_no_fall_checks": producer_checks, + "evidence_sha256": {**{"PREROLL_EVIDENCE.npz": sha256(pre_path), state_path.name: sha256(state_path)}, + **({"INITIAL_PROP_NO_FALL.json": sha256(producer_path)} if producer_path.is_file() else {})}, + } + + +def video_decode_audit(out: Path, expected_frames: int) -> dict: + """Perform the one authoritative sequential decode of all three videos.""" + import av + out = Path(out) + videos = {} + all_passed = True + for camera, (width, height) in CAMERAS.items(): + path = out / f"{camera}.mp4" + total = np.zeros(3, dtype=np.float64) + total2 = np.zeros(3, dtype=np.float64) + low = np.ones(3, dtype=np.float64) + high = np.zeros(3, dtype=np.float64) + sample_count = 0 + frame_count = 0 + first_gray = [] + near_black = 0 + nonfinite = 0 + previous_digest = None + exact_run = longest_exact_run = 0 + with av.open(str(path)) as container: + stream = container.streams.video[0] + fps = float(stream.average_rate) + actual_width, actual_height = stream.width, stream.height + for frame in container.decode(video=0): + rgb = frame.to_ndarray(format="rgb24") + gray = rgb.astype(np.float32).mean(axis=2) / 255.0 + if frame_count < 4: + first_gray.append(gray) + fraction = float((gray < (4.0 / 255.0)).mean()) + near_black += int(fraction > 0.98) + nonfinite += int(not np.isfinite(gray).all()) + digest = hashlib.sha256(rgb.tobytes()).digest() + exact_run = exact_run + 1 if digest == previous_digest else 1 + longest_exact_run = max(longest_exact_run, exact_run) + previous_digest = digest + if frame_count % max(1, expected_frames // 100) == 0: + sampled = rgb[::16, ::16].reshape(-1, 3) / 255.0 + total += sampled.sum(axis=0) + total2 += (sampled * sampled).sum(axis=0) + low = np.minimum(low, sampled.min(axis=0)) + high = np.maximum(high, sampled.max(axis=0)) + sample_count += len(sampled) + frame_count += 1 + adjacent = [float(np.mean(np.abs(first_gray[i + 1] - first_gray[i]))) + for i in range(max(0, len(first_gray) - 1))] + checks = { + "frame_count": frame_count == expected_frames, + "fps": fps == 20, + "dimensions": (actual_width, actual_height) == (width, height), + "first_frame_synchronized": len(adjacent) == 3 and adjacent[0] < 0.05, + "no_near_black_frames": near_black == 0, + "finite_metrics": nonfinite == 0, + "no_long_exact_frame_run": longest_exact_run < 10, + } + passed = all(checks.values()) + all_passed &= passed + mean = total / max(sample_count, 1) + videos[camera] = { + "passed": passed, "path": str(path), "sha256": sha256(path), + "frames": frame_count, "fps": fps, "width": actual_width, "height": actual_height, + "checks": checks, "first_three_adjacent_mean_abs_gray_differences": adjacent, + "near_black_gt_0_98_frames": near_black, "nonfinite_metric_count": nonfinite, + "longest_exact_frame_run": longest_exact_run, + "image_stats": { + "min": low.reshape(3, 1, 1).tolist(), "max": high.reshape(3, 1, 1).tolist(), + "mean": mean.reshape(3, 1, 1).tolist(), + "std": np.sqrt(np.maximum(total2 / max(sample_count, 1) - mean * mean, 0)).reshape(3, 1, 1).tolist(), + "count": [frame_count], + }, + } + report = { + "schema": "multi_video_decode_audit_v1", "passed": all_passed, + "method": "one authoritative sequential PyAV decode of every frame in all three views", + "expected_frames": expected_frames, "videos": videos, + } + write_json(out / "VIDEO_DECODE_AUDIT.json", report) + return report + + +FROZEN_AUDITS = [ + "TASK_SCHEMA_AUDIT.json", "CONTACT_CAPTURE_AUDIT.json", "GRASP_RETENTION_AUDIT.json", + "GRASP_STABILITY_AUDIT.json", "NON_TARGET_PROP_STABILITY_AUDIT.json", + "PHYSICS_CLOCK_AUDIT.json", "STARTUP_STABILITY_AUDIT.json", "CAMERA_WARMUP_AUDIT.json", + "CAMERA_EXTRINSICS_AUDIT.json", + "VIDEO_DECODE_AUDIT.json", + "SELF_COLLISION_AUDIT.json", "WORLD_COLLISION_AUDIT.json", "WRIST_AUDIT.json", + "VISIBILITY_AUDIT.json", +] +RAW_EVIDENCE = [ + "recording_60hz.npz", "full_state.npz", "PHYSICS_CLOCK.json", "PREROLL_EVIDENCE.json", + "PREROLL_EVIDENCE.npz", "PASSIVE_FINGER_PROJECTION.json", "CAMERA_WARMUP.json", + "camera_extrinsics_20hz.npz", "RESULT.json", "ITEM_EVENTS.json", "CHECKPOINT.json", + "CONTACT_CAPTURE.json", "recording_metadata.json", "LIVE_DRIVE_READBACK.json", + "scene.usda", "contacts.jsonl.gz", "top_head.mp4", "hand_left.mp4", "hand_right.mp4", +] +OPTIONAL_RAW_EVIDENCE = ["INITIAL_PROP_NO_FALL.json"] + + +def freeze_physical_gate(case: Path, world_audit_shards: int | None = None) -> dict: + case = Path(case).resolve() + out = case / "rollout" + destination = out / "physical_evidence" + if destination.exists(): + raise FileExistsError(destination) + pending = out / f".physical_evidence.pending.{os.getpid()}" + pending.mkdir() + try: + frozen = {} + for name in FROZEN_AUDITS: + source = out / name + target = pending / name + shutil.copy2(source, target) + frozen[name] = sha256(target) + os.replace(pending, destination) + except Exception: + shutil.rmtree(pending, ignore_errors=True) + raise + raw_names = RAW_EVIDENCE + [name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()] + raw = {name: sha256(out / name) for name in raw_names} + report = { + "schema": "multi_startup_physical_gate_v1", "passed": True, + "case": str(case), "visual_acceptance_pending": True, + "world_audit_shards": world_audit_shards, + "frozen_audit_dir": str(destination), "frozen_audit_sha256": frozen, + "raw_evidence_sha256": raw, + "semantics": "Physical and startup acceptance is frozen before staged export. Visual/manual and official-reader acceptance remain separate required gates.", + "completed_unix": time.time(), + } + write_json(out / "PHYSICAL_GATE_RESULT.json", report) + return report + + +def verify_physical_gate(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + failures = [] + try: + receipt = json.loads((out / "PHYSICAL_GATE_RESULT.json").read_text()) + frozen_dir = Path(receipt["frozen_audit_dir"]).resolve() + if frozen_dir != (out / "physical_evidence").resolve(): + failures.append("frozen_directory") + for name, digest in receipt.get("frozen_audit_sha256", {}).items(): + path = frozen_dir / name + if not path.is_file() or sha256(path) != digest: + failures.append("frozen:" + name) + for name, digest in receipt.get("raw_evidence_sha256", {}).items(): + path = out / name + if not path.is_file() or sha256(path) != digest: + failures.append("raw:" + name) + if set(receipt.get("frozen_audit_sha256", {})) != set(FROZEN_AUDITS): + failures.append("frozen_scope") + expected_raw = set(RAW_EVIDENCE) | {name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()} + if set(receipt.get("raw_evidence_sha256", {})) != expected_raw: + failures.append("raw_scope") + except Exception as exc: + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": False, + "failures": ["missing_or_invalid_receipt"], "error": repr(exc)} + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": not failures, + "failures": failures, "receipt_sha256": sha256(out / "PHYSICAL_GATE_RESULT.json")} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/task_collision_scope.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/task_collision_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..5e29f2540b8f62498affa56eb14fa3e2fa003494 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/task_collision_scope.py @@ -0,0 +1,32 @@ +"""Robot and every requested target stay in scope for the full multi-item run.""" +import copy +POLICY=dict(version='robot_and_all_requested_targets_vs_environment_v2',included=['robot_vs_environment','robot_vs_any_requested_target','every_requested_target_vs_environment','requested_target_vs_requested_target'],excluded=['environment_vs_environment','unrelated_stock_support'],separate_required=['robot_self_collision_all_ticks','per_item_physical_pick_place_success','final_multi_item_nonoverlap'],authority='Multi-item task retains the task-scoped environment omission while keeping the robot and every requested target in scope at every tick.') + +def in_scope(pair,targets): + targets=tuple(targets) + def task_body(path): + if path.startswith(('/G01/','/World/Robot/')):return True + return any(path==target or path.startswith(target+'/') or path.startswith(target+'#') for target in targets) + return any(task_body(str(pair.get(k,''))) for k in ('a','b','a_owner','b_owner')) + +def summarize_scoped(original,report,tolerance,targets,nonphysical_paths=()): + scoped=copy.copy(report);pairs=report.get('pairs',[]);scoped['pairs']=[p for p in pairs if in_scope(p,targets) and not any(str(p.get(k,'')).split('#')[0] in nonphysical_paths for k in ('a','b'))];scoped['unrelated_stock_support']={} + # Keep all_scene_pairs_checked truthful. Remove only the deliberate scope omission. + result=original(scoped,tolerance) + result['unresolved_review']=[x for x in result['unresolved_review'] if x['reason']!='unrelated scene pairs were not checked'] + result['geometry_status']='REJECT' if result['hard_failures'] else 'REVIEW' if result['unresolved_review'] else 'CLEAR_AT_CHECKED_SAMPLES' + result['omitted_nonphysical_decorative_paths']=list(nonphysical_paths);result['collision_scope']=POLICY;result['excluded_environment_pair_count']=len(pairs)-len(scoped['pairs']);result['all_scene_pairs_checked']=bool(report.get('all_scene_pairs_checked')) + result['requested_targets']=list(targets) + result['limitations'].append('Clearance applies to the robot and every requested target, not environment assembly or unrelated object pairs.') + return result + +def install(acceptance,targets,nonphysical_paths=()): + original=acceptance.summarize + acceptance.summarize=lambda report,contact_tolerance_mm=None:summarize_scoped(original,report,contact_tolerance_mm,targets,nonphysical_paths) + + +def nonphysical_rug_paths(stage): + from pxr import Usd,UsdPhysics + root=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') + if not root:return [] + return [str(p.GetPath()) for p in Usd.PrimRange(root) if p.HasAPI(UsdPhysics.CollisionAPI) and p.GetAttribute('physics:collisionEnabled').Get() is False] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_case.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_case.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd041017eeb792fea5d4c07b4a67ba899aae96b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_case.py @@ -0,0 +1,113 @@ +"""Validate one completed native episode and stage it for independent review. + +This program never publishes or accepts an episode. It freezes physical and +startup evidence, exports a candidate, and leaves manual visual plus official +LeRobot reader QA as separate mandatory gates. +""" +import fcntl +import json +import os +import subprocess +import sys +import time +from pathlib import Path + +W = Path(__file__).resolve().parent +case = Path(sys.argv[1]).resolve() +out = case / "rollout" +CPU = sys.executable +sys.path.insert(0, "/mnt/agent-bench-data/lingjun/ziche/work/family3_repair/collision/python_deps") +from startup_validation import freeze_physical_gate, video_decode_audit +from validate_export import task_audit, write + + +def run(script, args, log, env=None): + with (out / log).open("w") as stream: + result = subprocess.run([CPU, str(W / script), str(case), *args], stdout=stream, + stderr=subprocess.STDOUT, env=env) + if result.returncode: + raise RuntimeError(f"{script} {args} exited {result.returncode}; see {log}") + + +lock_dir = W / "validation_locks" +lock_dir.mkdir(exist_ok=True) +case_lock = (lock_dir / f"{case.name}.lock").open("a+") +fcntl.flock(case_lock, fcntl.LOCK_EX | fcntl.LOCK_NB) +capacity = None +while capacity is None: + for index in range(int(os.environ.get("CHAIN_VALIDATION_CAPACITY", "4"))): + candidate = (lock_dir / f"capacity_{index}.lock").open("a+") + try: + fcntl.flock(candidate, fcntl.LOCK_EX | fcntl.LOCK_NB) + capacity = candidate + break + except BlockingIOError: + candidate.close() + if capacity is None: + time.sleep(10) + +try: + write(case / "STATUS.json", dict(status="VERIFYING", phase="startup_task_views_collisions", + updated=time.time())) + from runtime_evidence import reconcile + reconcile(case) + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Task/startup/schema: " + str(report["failures"])) + + run("audit_views.py", [], "views_audit.log") + for name in ("WRIST_AUDIT.json", "VISIBILITY_AUDIT.json"): + if json.loads((out / name).read_text()).get("passed") is not True: + raise RuntimeError(name + " failed") + + run("audit_geometry.py", ["self"], "self_audit.log") + if json.loads((out / "SELF_COLLISION_AUDIT.json").read_text()).get("passed") is not True: + raise RuntimeError("Native self collision requires replanning") + + world_env = dict(os.environ, OPENBLAS_NUM_THREADS="1", OMP_NUM_THREADS="1") + run("audit_geometry.py", ["world"], "world_audit.log", world_env) + world = json.loads((out / "WORLD_COLLISION_AUDIT.json").read_text()) + if world.get("geometry_status") != "CLEAR_AT_CHECKED_SAMPLES" or world.get("complete_trajectory_checked") is not True: + raise RuntimeError("World collision review is not clear and complete") + + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Final task/startup/schema: " + str(report["failures"])) + decode = video_decode_audit(out, report["training_frames"]) + if not decode["passed"]: + raise RuntimeError("Full three-view video decode audit failed") + gate = freeze_physical_gate(case, world.get("parallel_shards")) + if gate["passed"] is not True: + raise RuntimeError("Physical gate freeze failed") + + export_python = "/mnt/agent-bench-data/lingjun/ziche/work/shelf_training_target_layer60_20260916/lerobot_env/bin/python" + with (out / "export.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "validate_export.py"), str(case)], + stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Staged export verification failed; see export.log") + run("prepare_visual_qa.py", [], "visual_evidence.log") + dataset = W / "staged_datasets" / case.name + layout = int(json.loads((case / "job.json").read_text())["layout_id"]) + reader_report = W / "qa" / f"layout_{layout:02d}" / "OFFICIAL_LEROBOT_READER_QA.json" + reader_report.parent.mkdir(parents=True, exist_ok=True) + with (out / "official_reader.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "official_lerobot_reader_qa.py"), + "--layout", str(layout), "--dataset", str(dataset), + "--output", str(reader_report)], stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Official LeRobot reader QA failed; see official_reader.log") + write(case / "STATUS.json", dict(status="MANUAL_REVIEW_PENDING", phase="first_0p5s_and_all_phase_panels", + frames=report["training_frames"], updated=time.time(), + staged_dataset=str(dataset), physical_gate=str(out / "PHYSICAL_GATE_RESULT.json"), + official_reader=str(reader_report), accepted=False)) +except Exception as exc: + write(case / "STATUS.json", dict(status="REVIEW_REQUIRED", phase="validation_failure", + error=repr(exc), updated=time.time(), accepted=False)) + raise +finally: + if capacity is not None: + fcntl.flock(capacity, fcntl.LOCK_UN) + capacity.close() + fcntl.flock(case_lock, fcntl.LOCK_UN) + case_lock.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_export.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_export.py new file mode 100644 index 0000000000000000000000000000000000000000..ef2944855983730ca700c1c1b78803a54dd62949 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_export.py @@ -0,0 +1,318 @@ +"""Verify schema/task evidence; export only after independent collision gates pass.""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + physical_verification=verify_physical_gate(case);assert physical_verification['passed'],physical_verification + frozen=out/'physical_evidence';audit=json.loads((frozen/'TASK_SCHEMA_AUDIT.json').read_text());assert audit['passed'],audit['failures'];assert audit['recording_sha256']==hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest() + for file,field,value in [('SELF_COLLISION_AUDIT.json','passed',True),('WORLD_COLLISION_AUDIT.json','geometry_status','CLEAR_AT_CHECKED_SAMPLES'),('WRIST_AUDIT.json','passed',True),('VISIBILITY_AUDIT.json','passed',True)]: + assert json.loads((frozen/file).read_text())[field]==value,file + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + destination=W/'staged_datasets'/f"layout_{job['layout_id']:02d}";temp=destination.with_name(destination.name+'.pending');assert not destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=True,gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=True,official_reader_pending=True) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + temp.rename(destination);return destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_staged.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_staged.py new file mode 100644 index 0000000000000000000000000000000000000000..88d5f8ee86d702f799a7b69c4d5b66d63e51f97c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/sim_info/episode_000000/validation_code/validate_staged.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +"""Admit a staged candidate to VALIDATION_READY after independent QA only.""" +import argparse +import hashlib +import json +import time +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from startup_validation import sha256, verify_physical_gate, write_json + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def load(path): + return json.loads(Path(path).read_text()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + args = parser.parse_args() + lid = f"{args.layout:02d}" + case = (W / "cases" / f"layout_{lid}").resolve() + out = case / "rollout" + dataset = W / "staged_datasets" / f"layout_{lid}" + sim = dataset / "sim_info/episode_000000" + evidence_dir = W / "qa" / f"layout_{lid}" / "visual_evidence" + panels_path = evidence_dir / "PANELS.json" + manual_path = W / "qa" / f"layout_{lid}" / "ROOT_MANUAL_REVIEW.json" + reader_path = W / "qa" / f"layout_{lid}" / "OFFICIAL_LEROBOT_READER_QA.json" + failures = [] + + physical = verify_physical_gate(case) + if not physical["passed"]: + failures.append("immutable_physical_gate") + provenance = load(sim / "STARTUP_PREROLL_PROVENANCE.json") + evidence_hashes = provenance.get("evidence_sha256", {}) + bad_evidence = [name for name, digest in evidence_hashes.items() + if not (sim / name).is_file() or sha256(sim / name) != digest] + if bad_evidence: + failures.append("exported_provenance:" + ",".join(bad_evidence)) + + panels = load(panels_path) + panel_hashes = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} + if any(not (evidence_dir / name).is_file() or sha256(evidence_dir / name) != digest + for name, digest in panel_hashes.items()): + failures.append("visual_panel_hashes") + manual = load(manual_path) + manual_ok = (manual.get("schema") == "ziche.multi10.root_manual_visual_review.v1" + and manual.get("layout_id") == args.layout and manual.get("passed") is True + and manual.get("panels") == panel_hashes + and manual.get("reviewed_all_three_views") is True + and manual.get("reviewed_first_retained_0p5s") is True + and manual.get("reviewed_every_phase_midpoint") is True) + if not manual_ok: + failures.append("manual_visual_review") + + reader = load(reader_path) + parquet = dataset / "data/chunk-000/episode_000000.parquet" + reader_ok = (reader.get("passed") is True and reader.get("layout_id") == args.layout + and reader.get("parquet_sha256") == sha256(parquet)) + if not reader_ok: + failures.append("official_lerobot_reader") + + decode = load(sim / "VIDEO_DECODE_AUDIT.json") + video_hashes = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + video_hashes[camera] = sha256(video) + if (video_hashes[camera] != provenance["video_sha256"].get(camera) + or video_hashes[camera] != decode["videos"][camera]["sha256"] + or decode["videos"][camera].get("passed") is not True): + failures.append("video:" + camera) + + table = pq.read_table(parquet) + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + samples = np.arange(2, len(recording["q"]), 3) + ticks = np.asarray(table["source_control_tick"].to_numpy(), dtype=np.int64) + items = np.asarray(table["item_index"].to_numpy(), dtype=np.int64) + clock = load(out / "PHYSICS_CLOCK.json") + sim_time = np.asarray(table["simulator_time_s"].to_numpy(), dtype=np.float64) + alignment = (len(table) == len(samples) and np.array_equal(ticks, samples + 1) + and np.array_equal(items, recording["item_index"][samples]) + and np.allclose(sim_time, float(clock["unrecorded_preroll_duration_s"]) + + (samples + 1) / 60.0, atol=2e-6, rtol=0)) + if not alignment: + failures.append("staged_source_alignment") + + report = {"schema": "ziche.multi10.staged_final_qa.v1", "layout_id": args.layout, + "passed": not failures, "failures": failures, "case": str(case), + "dataset": str(dataset), "physical_gate_verification": physical, + "provenance_sha256": sha256(sim / "STARTUP_PREROLL_PROVENANCE.json"), + "manual_review": {"path": str(manual_path), "sha256": sha256(manual_path), "passed": manual_ok}, + "official_reader": {"path": str(reader_path), "sha256": sha256(reader_path), "passed": reader_ok}, + "panels_receipt_sha256": sha256(panels_path), "video_sha256": video_hashes, + "parquet_sha256": sha256(parquet), "staged_source_alignment": alignment, + "status_semantics": "VALIDATION_READY is review complete but not publication or ACCEPTED", + "completed_unix": time.time()} + qa_path = W / "qa" / f"layout_{lid}.json" + write_json(qa_path, report) + if not report["passed"]: + raise SystemExit(1) + write_json(case / "STATUS.json", {"status": "VALIDATION_READY", "phase": "review_complete", + "accepted": False, "dataset": str(dataset), "qa": str(qa_path), + "qa_sha256": sha256(qa_path), "updated": time.time()}) + print(qa_path) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes.jsonl b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..634a849a9476906e694bb2e6533d3c7ea47cddc3 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes.jsonl @@ -0,0 +1 @@ +{"episode_index": 0, "tasks": ["Starting with an empty tray, pick up coke_c3, oreo_gf, sprite_c1 from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items."], "length": 8884} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes_stats.jsonl b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes_stats.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ab32798bcfe10b2d48b5dd0b43ebf60f9ea6eed7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000001/source_meta/episodes_stats.jsonl @@ -0,0 +1 @@ +{"episode_index": 0, "stats": {"action": {"min": [-1.1277633905410767, 0.3093137741088867, -0.4280213415622711, -1.8424174785614014, -0.04770688712596893, -0.31510695815086365, -0.17955660820007324, -0.31151655316352844, -1.4799389839172363, -1.319525957107544, 0.2102576643228531, -1.0704898834228516, -1.7399929761886597, -1.5066152811050415, 0.0, 0.0, -1.8161504158342723e-06, -0.34033921360969543, 0.3969573974609375, 0.22999490797519684, -0.18000000715255737, -0.25], "max": [0.4537392854690552, 1.1327413320541382, 1.3664286136627197, -0.1661277711391449, 0.9117714762687683, 1.7399929761886597, 0.9955442547798157, 1.3508520126342773, -0.2433156967163086, 0.385356605052948, 1.452036738395691, 0.08592494577169418, -0.4722013473510742, 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{ + "rank": 77, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 78, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 79, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 80, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 81, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 82, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 83, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 84, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 85, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 86, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 87, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 88, + "stage": "endpoint_ik", + "error": [ + 0.007514477087863873, + 0.04583834324371992 + ] + }, + { + "rank": 89, + "stage": "destination", + "detail": { + "kind": "environment", + "robot_or_target": "/World/Robot/base_link", + "obstacle": "/World/Assets/CoffeeTable/Mesh" + } + }, + { + "rank": 90, + "stage": "passed" + } + ], + "native_validation_required": true +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_FULL_PROPOSAL.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_FULL_PROPOSAL.json new file mode 100644 index 0000000000000000000000000000000000000000..0ac75c2735002ab7736b3bcc3e6b3df332b62ea8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_FULL_PROPOSAL.json @@ -0,0 +1,194 @@ +{ + "schema": "multi_item.polar_full_place.v1", + "read_only": true, + "layout_id": 8, + "item_index": 1, + "status_at_probe": { + "status": "WAITING_FOR_PLANNING_REPAIR", + "phase": "item_01_place_planning", + "item_index": 1, + "completed_objects": [ + "/World/Assets/ShelfItems/cayman_jacked" + ], + "control_ticks": 17477, + "updated": 1791293001.754966, + "base_pose": [ + -1.2378097772598267, + 1.136970043182373, + 0.0013677850365638733, + 3.110617399215698e-07, + 5.563924787566066e-08, + 0.7106037735939026, + -0.7035925388336182 + ], + "target_pose": [ + -1.3603686094284058, + 0.5651859045028687, + 0.6525933146476746, + -0.06046805903315544, + -0.006268595810979605, + -0.9962818026542664, + 0.06104904040694237 + ], + "planning_error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'landing', 'error': [0.03216785642931353, 0.17805287253392618]}, {'candidate': 1, 'stage': 'landing', 'error': [0.051015756627658476, 0.2772608936273491]}, {'candidate': 2, 'stage': 'landing', 'error': [0.038102052824378264, 0.20986873469500741]}, {'candidate': 171, 'stage': 'landing', 'error': [0.038363228363613174, 0.17159458896750945]}, {'candidate': 172, 'stage': 'landing', 'error': [0.04501160209446423, 0.20106242828812146]}, {'candidate': 173, 'stage': 'landing', 'error': [0.050503454882341835, 0.22557545343981883]}]\")", + "repair_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/repairs/item_01_place.json" + }, + "evidence": { + "endpoint": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/diagnostics/LAYOUT08_ITEM01_POLAR_ENDPOINT.json", + "checkpoint": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/rollout/checkpoint_60hz.npz" + }, + "accepted": null, + "attempts": [ + { + "rank": 0, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126684099), np.float64(0.18820646984094486)]}, {'candidate': 0, 'stage': 'path', 'frame': 73, 'error': [np.float64(0.002068071377086524), np.float64(0.008219236602701968)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354583432374698), np.float64(0.010877796766651503)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002735460233462963), np.float64(0.010877804132148085)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321565), np.float64(0.012697391431752058)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3321571472723626), np.float64(1.4984789797163665)]}]\")" + }, + { + "rank": 1, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.002076655255259376), np.float64(0.00891971810206424)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458021356), np.float64(0.009332694289902077)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726331947), np.float64(0.010620017622789881)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024718490825313055), np.float64(0.010620659151885101)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.0030098699955560717), np.float64(0.01293810182740619)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2849439952471155), np.float64(1.3117053812226855)]}]\")" + }, + { + "rank": 2, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126690703), np.float64(0.18820646984053951)]}, {'candidate': 0, 'stage': 'path', 'frame': 73, 'error': [np.float64(0.0020680713770866305), np.float64(0.008219236602701274)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354583432374516), np.float64(0.010877796766651408)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354602334631398), np.float64(0.010877804132147983)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321468), np.float64(0.012697391431752289)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.332157147272363), np.float64(1.4984789797163647)]}]\")" + }, + { + "rank": 3, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.002076655255259289), np.float64(0.008919718102064371)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458020766), np.float64(0.00933269428990205)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726333327), np.float64(0.010620017622789704)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024718490825312543), np.float64(0.010620659151884877)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.0030098699955560717), np.float64(0.012938101827406354)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.28494399532521497), np.float64(1.311705380798691)]}]\")" + }, + { + "rank": 4, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126674504), np.float64(0.1882064698415351)]}, {'candidate': 0, 'stage': 'path', 'frame': 73, 'error': [np.float64(0.0020680713770866526), np.float64(0.008219236602701165)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002735458343237407), np.float64(0.010877796766651801)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354602334630296), np.float64(0.010877804132147754)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321453), np.float64(0.01269739143175204)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.332157147272363), np.float64(1.4984789797163647)]}]\")" + }, + { + "rank": 5, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.0020766552552593876), np.float64(0.008919718102064683)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458023325), np.float64(0.00933269428990314)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726335183), np.float64(0.01062001762279077)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.002471849082531601), np.float64(0.010620659151885781)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.00300986999555656), np.float64(0.012938101827407181)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.28494399523912584), np.float64(1.3117053812660606)]}]\")" + }, + { + "rank": 6, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964467), np.float64(0.1777161946946212)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.00230113432536402), np.float64(0.009553171946807012)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026313087416573355), np.float64(0.010917204935120214)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537704), np.float64(0.010917621289837)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197374), np.float64(0.012738733797909416)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.27186689711389195), np.float64(1.3447493430173458)]}]\")" + }, + { + "rank": 7, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964177), np.float64(0.17771619469464034)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.002301134325363871), np.float64(0.009553171946807318)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026313087416574236), np.float64(0.010917204935120505)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119536607), np.float64(0.010917621289836648)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.0030728548731972193), np.float64(0.012738733797909912)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138918), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 8, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933168274), np.float64(0.14555208934959996)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422982836905622), np.float64(0.14555211031788906)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022006762905848513), np.float64(0.0096795121141131)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151389), np.float64(0.009679627973424993)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740550427), np.float64(0.011970290543113568)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648383934), np.float64(1.5731263189320466)]}]\")" + }, + { + "rank": 9, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933164236), np.float64(0.14555208934962696)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422982836904847), np.float64(0.1455521103179394)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200676290585018), np.float64(0.009679512114114401)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151679), np.float64(0.009679627973425246)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.002722778974055153), np.float64(0.011970290543115205)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648384233), np.float64(1.573126318932029)]}]\")" + }, + { + "rank": 10, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.042415917979610654), np.float64(0.13830617322044453)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241592131679665), np.float64(0.13830614765827232)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023667102512057183), np.float64(0.008801151571142866)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728417), np.float64(0.00880174919519678)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360410085), np.float64(0.009101740125864519)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.1970183183570358), np.float64(1.0953862451757137)]}]\")" + }, + { + "rank": 11, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.042415917979610564), np.float64(0.13830617322044514)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241592131679069), np.float64(0.1383061476583184)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366710251205547), np.float64(0.008801151571142205)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366871031472786), np.float64(0.008801749195196306)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.002448159436040922), np.float64(0.009101740125863763)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.1970183183570361), np.float64(1.0953862451757128)]}]\")" + }, + { + "rank": 12, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964447), np.float64(0.17771619469462135)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.002301134325363892), np.float64(0.009553171946806644)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002631308741657355), np.float64(0.010917204935120007)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537504), np.float64(0.010917621289835972)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197286), np.float64(0.012738733797909498)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138919), np.float64(1.344749343017345)]}]\")" + }, + { + "rank": 13, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0468199676396391), np.float64(0.1777161946946584)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.002301134325364197), np.float64(0.009553171946807118)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026313087416577293), np.float64(0.010917204935120247)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119538797), np.float64(0.010917621289836737)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197286), np.float64(0.01273873379790942)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138917), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 14, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422983193318085), np.float64(0.14555208934951025)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422982836906609), np.float64(0.1455521103178161)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200676290583241), np.float64(0.009679512114107086)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022007026301507776), np.float64(0.009679627973422393)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740540786), np.float64(0.011970290543109514)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838292), np.float64(1.5731263189321034)]}]\")" + }, + { + "rank": 15, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933162175), np.float64(0.1455520893496341)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422982836905826), np.float64(0.14555211031787732)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200676290584689), np.float64(0.00967951211411311)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151329), np.float64(0.009679627973424702)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740549868), np.float64(0.011970290543113774)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838385), np.float64(1.5731263189320503)]}]\")" + }, + { + "rank": 16, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0424159179796091), np.float64(0.13830617322045613)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241592131679482), np.float64(0.1383061476582854)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366710251205663), np.float64(0.008801151571142377)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728417), np.float64(0.008801749195196556)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360410085), np.float64(0.009101740125864472)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703604), np.float64(1.0953862451757128)]}]\")" + }, + { + "rank": 17, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960979), np.float64(0.13830617322045155)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241592131679169), np.float64(0.13830614765831076)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023667102512055184), np.float64(0.00880115157114204)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366871031472757), np.float64(0.00880174919519584)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360407795), np.float64(0.00910174012586373)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703584), np.float64(1.0953862451757133)]}]\")" + }, + { + "rank": 18, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.046819967639638395), np.float64(0.17771619469466)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.002301134325363804), np.float64(0.009553171946806943)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002631308741657187), np.float64(0.010917204935119506)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002631409111953533), np.float64(0.010917621289836553)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197198), np.float64(0.012738733797909333)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.27186689711389195), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 19, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.046819967639644786), np.float64(0.17771619469462174)]}, {'candidate': 0, 'stage': 'path', 'frame': 74, 'error': [np.float64(0.002301134325364129), np.float64(0.009553171946807701)]}, {'candidate': 0, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026313087416575316), np.float64(0.01091720493512049)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537704), np.float64(0.010917621289836962)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197198), np.float64(0.012738733797909501)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138918), np.float64(1.3447493430173458)]}]\")" + }, + { + "rank": 20, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422983193317977), np.float64(0.14555208934951694)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422982836906754), np.float64(0.14555211031780924)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200676290583336), np.float64(0.00967951211410654)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630150766), np.float64(0.009679627973422705)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740542894), np.float64(0.011970290543109647)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838287), np.float64(1.5731263189321067)]}]\")" + }, + { + "rank": 21, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0342298319331719), np.float64(0.14555208934957825)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0342298283690533), np.float64(0.1455521103179082)]}, {'candidate': 0, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022006762905848673), np.float64(0.00967951211411324)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022007026301514125), np.float64(0.009679627973425491)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740552925), np.float64(0.011970290543114517)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648383817), np.float64(1.5731263189320521)]}]\")" + }, + { + "rank": 22, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960684), np.float64(0.13830617322047448)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241592131679461), np.float64(0.1383061476582882)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023667102512057183), np.float64(0.008801151571142862)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314729575), np.float64(0.00880174919519687)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360411764), np.float64(0.009101740125864708)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703598), np.float64(1.095386245175713)]}]\")" + }, + { + "rank": 23, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960772), np.float64(0.13830617322046765)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.042415921316791526), np.float64(0.13830614765831203)]}, {'candidate': 0, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366710251205605), np.float64(0.008801151571141787)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728152), np.float64(0.008801749195196225)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360407795), np.float64(0.009101740125863914)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703604), np.float64(1.095386245175713)]}]\")" + }, + { + "rank": 24, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664740179574), np.float64(0.011030565000619816)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664836286472), np.float64(0.011030564966046702)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664844300483), np.float64(0.011030564981987965)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.0028114370953114306), np.float64(0.011028881403762889)]}, {'candidate': 1, 'stage': 'path', 'frame': 69, 'error': [np.float64(0.002785017667046298), np.float64(0.010924288290650777)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2177323391943625), np.float64(1.267564687752315)]}]\")" + }, + { + "rank": 25, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664740180463), np.float64(0.011030565000620505)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664836287075), np.float64(0.011030564966046654)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664844298995), np.float64(0.011030564981988471)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.0028114370953116366), np.float64(0.011028881403763078)]}, {'candidate': 1, 'stage': 'path', 'frame': 69, 'error': [np.float64(0.0027850176670464735), np.float64(0.010924288290650754)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.21773233919436272), np.float64(1.2675646877523135)]}]\")" + }, + { + "rank": 26, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654366781474), np.float64(0.014021363446104223)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654394245634), np.float64(0.014021363440423368)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654439613037), np.float64(0.014021363431663424)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.0032748862448244835), np.float64(0.014018456328842114)]}, {'candidate': 1, 'stage': 'path', 'frame': 68, 'error': [np.float64(0.0024731757641073175), np.float64(0.010596255486635281)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2819771710732216), np.float64(1.3873274015450903)]}]\")" + }, + { + "rank": 27, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654366781474), np.float64(0.014021363446104721)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654394243544), np.float64(0.01402136344042407)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654439613636), np.float64(0.01402136343166352)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.003274886244824503), np.float64(0.014018456328841942)]}, {'candidate': 1, 'stage': 'path', 'frame': 68, 'error': [np.float64(0.0024731757641074667), np.float64(0.010596255486635408)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2819771710732216), np.float64(1.3873274015450892)]}]\")" + }, + { + "rank": 28, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 1, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 1, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.34218580036407886), np.float64(1.5454094057189312)]}]\")" + }, + { + "rank": 29, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 1, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 1, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.34218580036407514), np.float64(1.5454094057189522)]}]\")" + } + ], + "native_validation_required": true +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json new file mode 100644 index 0000000000000000000000000000000000000000..88bdc4a8dd3bdc773db26a8145f5c7959e03cea4 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json @@ -0,0 +1,194 @@ +{ + "schema": "multi_item.polar_full_place.v1", + "read_only": true, + "layout_id": 8, + "item_index": 1, + "status_at_probe": { + "status": "WAITING_FOR_PLANNING_REPAIR", + "phase": "item_01_place_planning", + "item_index": 1, + "completed_objects": [ + "/World/Assets/ShelfItems/cayman_jacked" + ], + "control_ticks": 17477, + "updated": 1791293001.754966, + "base_pose": [ + -1.2378097772598267, + 1.136970043182373, + 0.0013677850365638733, + 3.110617399215698e-07, + 5.563924787566066e-08, + 0.7106037735939026, + -0.7035925388336182 + ], + "target_pose": [ + -1.3603686094284058, + 0.5651859045028687, + 0.6525933146476746, + -0.06046805903315544, + -0.006268595810979605, + -0.9962818026542664, + 0.06104904040694237 + ], + "planning_error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'landing', 'error': [0.03216785642931353, 0.17805287253392618]}, {'candidate': 1, 'stage': 'landing', 'error': [0.051015756627658476, 0.2772608936273491]}, {'candidate': 2, 'stage': 'landing', 'error': [0.038102052824378264, 0.20986873469500741]}, {'candidate': 171, 'stage': 'landing', 'error': [0.038363228363613174, 0.17159458896750945]}, {'candidate': 172, 'stage': 'landing', 'error': [0.04501160209446423, 0.20106242828812146]}, {'candidate': 173, 'stage': 'landing', 'error': [0.050503454882341835, 0.22557545343981883]}]\")", + "repair_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/repairs/item_01_place.json" + }, + "evidence": { + "endpoint": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/diagnostics/LAYOUT08_ITEM01_POLAR_ENDPOINT.json", + "checkpoint": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/rollout/checkpoint_60hz.npz" + }, + "accepted": null, + "attempts": [ + { + "rank": 0, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126684099), np.float64(0.18820646984094486)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.05578695595713913), np.float64(0.25122853279880875)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.032647849294865264), np.float64(0.14603110431525584)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002735460233462963), np.float64(0.010877804132148085)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321565), np.float64(0.012697391431752058)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3321571472723626), np.float64(1.4984789797163665)]}]\")" + }, + { + "rank": 1, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.002076655255259376), np.float64(0.00891971810206424)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458021356), np.float64(0.009332694289902077)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726331947), np.float64(0.010620017622789881)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024718490825313055), np.float64(0.010620659151885101)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.0030098699955560717), np.float64(0.01293810182740619)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2849439952471155), np.float64(1.3117053812226855)]}]\")" + }, + { + "rank": 2, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126690703), np.float64(0.18820646984053951)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.055786955957137147), np.float64(0.2512285327988191)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.032647849294864285), np.float64(0.14603110431525942)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354602334631398), np.float64(0.010877804132147983)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321468), np.float64(0.012697391431752289)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.332157147272363), np.float64(1.4984789797163647)]}]\")" + }, + { + "rank": 3, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.002076655255259289), np.float64(0.008919718102064371)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458020766), np.float64(0.00933269428990205)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726333327), np.float64(0.010620017622789704)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024718490825312543), np.float64(0.010620659151884877)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.0030098699955560717), np.float64(0.012938101827406354)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.28494399532521497), np.float64(1.311705380798691)]}]\")" + }, + { + "rank": 4, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04637097126674504), np.float64(0.1882064698415351)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.05578695595713827), np.float64(0.251228532798813)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.032647849294865056), np.float64(0.14603110431525618)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0027354602334630296), np.float64(0.010877804132147754)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003191814978321453), np.float64(0.01269739143175204)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.332157147272363), np.float64(1.4984789797163647)]}]\")" + }, + { + "rank": 5, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'path', 'frame': 104, 'error': [np.float64(0.0020766552552593876), np.float64(0.008919718102064683)]}, {'candidate': 0, 'stage': 'path', 'frame': 40, 'error': [np.float64(0.0021726289458023325), np.float64(0.00933269428990314)]}, {'candidate': 0, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.0024716996726335183), np.float64(0.01062001762279077)]}, {'candidate': 1, 'stage': 'path', 'frame': 30, 'error': [np.float64(0.002471849082531601), np.float64(0.010620659151885781)]}, {'candidate': 1, 'stage': 'path', 'frame': 26, 'error': [np.float64(0.00300986999555656), np.float64(0.012938101827407181)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.28494399523912584), np.float64(1.3117053812660606)]}]\")" + }, + { + "rank": 6, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964467), np.float64(0.1777161946946212)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490843906), np.float64(0.257295331157849)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.041966935553211224), np.float64(0.1663831251577061)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537704), np.float64(0.010917621289837)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197374), np.float64(0.012738733797909416)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.27186689711389195), np.float64(1.3447493430173458)]}]\")" + }, + { + "rank": 7, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964177), np.float64(0.17771619469464034)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490839527), np.float64(0.2572953311581437)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04196693555316883), np.float64(0.16638312515797335)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119536607), np.float64(0.010917621289836648)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.0030728548731972193), np.float64(0.012738733797909912)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138918), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 8, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933168274), np.float64(0.14555208934959996)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041451136), np.float64(0.26321288276597604)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753869141), np.float64(0.17933355705262938)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151389), np.float64(0.009679627973424993)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740550427), np.float64(0.011970290543113568)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648383934), np.float64(1.5731263189320466)]}]\")" + }, + { + "rank": 9, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933164236), np.float64(0.14555208934962696)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041450832), np.float64(0.26321288276599514)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753869389), np.float64(0.17933355705261633)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151679), np.float64(0.009679627973425246)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.002722778974055153), np.float64(0.011970290543115205)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648384233), np.float64(1.573126318932029)]}]\")" + }, + { + "rank": 10, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.042415917979610654), np.float64(0.13830617322044453)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06043116852943673), np.float64(0.19276977257308409)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.030278586950858943), np.float64(0.10884610155874196)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728417), np.float64(0.00880174919519678)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360410085), np.float64(0.009101740125864519)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.1970183183570358), np.float64(1.0953862451757137)]}]\")" + }, + { + "rank": 11, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.042415917979610564), np.float64(0.13830617322044514)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.060431168529432505), np.float64(0.19276977257311737)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.030278586950861046), np.float64(0.10884610155872879)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366871031472786), np.float64(0.008801749195196306)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.002448159436040922), np.float64(0.009101740125863763)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.1970183183570361), np.float64(1.0953862451757128)]}]\")" + }, + { + "rank": 12, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04681996763964447), np.float64(0.17771619469462135)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490842869), np.float64(0.2572953311579173)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04196693555314587), np.float64(0.16638312515811837)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537504), np.float64(0.010917621289835972)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197286), np.float64(0.012738733797909498)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138919), np.float64(1.344749343017345)]}]\")" + }, + { + "rank": 13, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0468199676396391), np.float64(0.1777161946946584)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490848172), np.float64(0.2572953311575593)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04196693555309472), np.float64(0.16638312515844023)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119538797), np.float64(0.010917621289836737)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197286), np.float64(0.01273873379790942)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138917), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 14, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422983193318085), np.float64(0.14555208934951025)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041451385), np.float64(0.26321288276595334)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753868431), np.float64(0.1793335570526676)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022007026301507776), np.float64(0.009679627973422393)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740540786), np.float64(0.011970290543109514)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838292), np.float64(1.5731263189321034)]}]\")" + }, + { + "rank": 15, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034229831933162175), np.float64(0.1455520893496341)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041451083), np.float64(0.26321288276597915)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753870915), np.float64(0.17933355705252552)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630151329), np.float64(0.009679627973424702)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740549868), np.float64(0.011970290543113774)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838385), np.float64(1.5731263189320503)]}]\")" + }, + { + "rank": 16, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0424159179796091), np.float64(0.13830617322045613)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0604311685294362), np.float64(0.19276977257308847)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.030278586950856227), np.float64(0.10884610155876233)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728417), np.float64(0.008801749195196556)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360410085), np.float64(0.009101740125864472)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703604), np.float64(1.0953862451757128)]}]\")" + }, + { + "rank": 17, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960979), np.float64(0.13830617322045155)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06043116852943587), np.float64(0.19276977257309064)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.030278586950864335), np.float64(0.10884610155870496)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.002366871031472757), np.float64(0.00880174919519584)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360407795), np.float64(0.00910174012586373)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703584), np.float64(1.0953862451757133)]}]\")" + }, + { + "rank": 18, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.046819967639638395), np.float64(0.17771619469466)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490842378), np.float64(0.25729533115795056)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.041966935552596465), np.float64(0.16638312516158135)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.002631409111953533), np.float64(0.010917621289836553)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197198), np.float64(0.012738733797909333)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.27186689711389195), np.float64(1.3447493430173452)]}]\")" + }, + { + "rank": 19, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.046819967639644786), np.float64(0.17771619469462174)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06958428490838224), np.float64(0.25729533115823305)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.041966935552519596), np.float64(0.1663831251620656)]}, {'candidate': 1, 'stage': 'path', 'frame': 47, 'error': [np.float64(0.0026314091119537704), np.float64(0.010917621289836962)]}, {'candidate': 1, 'stage': 'path', 'frame': 39, 'error': [np.float64(0.003072854873197198), np.float64(0.012738733797909501)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2718668971138918), np.float64(1.3447493430173458)]}]\")" + }, + { + "rank": 20, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03422983193317977), np.float64(0.14555208934951694)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041451911), np.float64(0.2632128827659219)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753868605), np.float64(0.17933355705265897)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.002200702630150766), np.float64(0.009679627973422705)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740542894), np.float64(0.011970290543109647)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.3524629864838287), np.float64(1.5731263189321067)]}]\")" + }, + { + "rank": 21, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0342298319331719), np.float64(0.14555208934957825)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06353686041450811), np.float64(0.26321288276599636)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04189170753868804), np.float64(0.17933355705264983)]}, {'candidate': 1, 'stage': 'path', 'frame': 54, 'error': [np.float64(0.0022007026301514125), np.float64(0.009679627973425491)]}, {'candidate': 1, 'stage': 'path', 'frame': 44, 'error': [np.float64(0.0027227789740552925), np.float64(0.011970290543114517)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.35246298648383817), np.float64(1.5731263189320521)]}]\")" + }, + { + "rank": 22, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960684), np.float64(0.13830617322047448)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06043116852943518), np.float64(0.19276977257309694)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03027858695086184), np.float64(0.1088461015587233)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314729575), np.float64(0.00880174919519687)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360411764), np.float64(0.009101740125864708)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703598), np.float64(1.095386245175713)]}]\")" + }, + { + "rank": 23, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04241591797960772), np.float64(0.13830617322046765)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.06043116852943029), np.float64(0.1927697725731355)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.030278586950869685), np.float64(0.1088461015586695)]}, {'candidate': 1, 'stage': 'path', 'frame': 53, 'error': [np.float64(0.0023668710314728152), np.float64(0.008801749195196225)]}, {'candidate': 1, 'stage': 'path', 'frame': 43, 'error': [np.float64(0.0024481594360407795), np.float64(0.009101740125863914)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.19701831835703604), np.float64(1.095386245175713)]}]\")" + }, + { + "rank": 24, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664740179574), np.float64(0.011030565000619816)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.04358270553558051), np.float64(0.15213084987741027)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.02622990542095829), np.float64(0.09689521846754157)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.05988909434595322), np.float64(0.19888297343619563)]}, {'candidate': 1, 'stage': 'path', 'frame': 69, 'error': [np.float64(0.002785017667046298), np.float64(0.010924288290650777)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2177323391943625), np.float64(1.267564687752315)]}]\")" + }, + { + "rank": 25, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0028118664740180463), np.float64(0.011030565000620505)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.043582705535588497), np.float64(0.15213084987735512)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.026229905420962804), np.float64(0.09689521846751166)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.05988909434596055), np.float64(0.1988829734361418)]}, {'candidate': 1, 'stage': 'path', 'frame': 69, 'error': [np.float64(0.0027850176670464735), np.float64(0.010924288290650754)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.21773233919436272), np.float64(1.2675646877523135)]}]\")" + }, + { + "rank": 26, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654366781474), np.float64(0.014021363446104223)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.046217640474338316), np.float64(0.1836757999371123)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0319269954560281), np.float64(0.13054916153059826)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.059712300631620356), np.float64(0.23164138428575948)]}, {'candidate': 1, 'stage': 'path', 'frame': 68, 'error': [np.float64(0.0024731757641073175), np.float64(0.010596255486635281)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2819771710732216), np.float64(1.3873274015450903)]}]\")" + }, + { + "rank": 27, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0032755654366781474), np.float64(0.014021363446104721)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.0462176404743365), np.float64(0.1836757999371234)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03192699545605926), np.float64(0.1305491615304079)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.0597123006316216), np.float64(0.23164138428575057)]}, {'candidate': 1, 'stage': 'path', 'frame': 68, 'error': [np.float64(0.0024731757641074667), np.float64(0.010596255486635408)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.2819771710732216), np.float64(1.3873274015450892)]}]\")" + }, + { + "rank": 28, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.034476371072953524), np.float64(0.14995272226035566)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.024928446838659492), np.float64(0.10944157577977011)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.017301321101692368), np.float64(0.07653200049529149)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.043962426166639805), np.float64(0.18967170300965153)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.34218580036407886), np.float64(1.5454094057189312)]}]\")" + }, + { + "rank": 29, + "stage": "placement", + "error": "RuntimeError(\"All supported tray candidates unreachable: [{'candidate': 0, 'stage': 'open_gripper_release_or_retreat_world', 'detail': {'status': 'BLOCKED', 'samples_checked': 11, 'opening_samples': 121, 'retreat_samples': 184, 'detail': {'kind': 'released_world', 'phase': 'opening', 'sample': 10, 'robot': '/World/Robot/gripper_r_outer_link4', 'obstacle': '/World/Assets/ShelfItems/cayman_jacked/BodyCollision'}}}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.03447637107530833), np.float64(0.149952722246822)]}, {'candidate': 0, 'stage': 'retreat', 'error': [np.float64(0.02492844683866683), np.float64(0.10944157577972818)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.017301321101691816), np.float64(0.07653200049529582)]}, {'candidate': 1, 'stage': 'retreat', 'error': [np.float64(0.043962426166707286), np.float64(0.1896717030092617)]}, {'candidate': 2, 'stage': 'landing', 'error': [np.float64(0.34218580036407514), np.float64(1.5454094057189522)]}]\")" + } + ], + "native_validation_required": true +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POST_RELEASE_UPRIGHT_PROPOSAL.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POST_RELEASE_UPRIGHT_PROPOSAL.json new file mode 100644 index 0000000000000000000000000000000000000000..4063c5810f18e3384d1f87c637cc68b7074cdcc5 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM01_POST_RELEASE_UPRIGHT_PROPOSAL.json @@ -0,0 +1,136 @@ +{ + "schema": "multi_item.post_release_level_return.v1", + "read_only": true, + "accepted": true, + "source_proposal": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/CASE8_ITEM01_FOCUSED_SHORT_RETREAT_PROPOSAL.json", + "source_proposal_sha256": "9e6b9aef166ca91f6567704fbb55b49dee0731b11d93e9e0289311ea7ccb148d", + "item_index": 1, + "body_pitch_deg": 18.0, + "start_q": [ + 0.007592812646180391, + 0.7853981633974483, + -5.818599788653955e-07, + 0.4363473355770111, + -0.9836950898170471, + 1.559449444877257, + 0.5364065766334534, + -0.324616735361651, + 0.2651083469390869, + -1.396628058127769, + -1.2293621301651, + 1.4303263386041423, + 0.5695574879646301, + 0.04389657502147506, + 1.461108684539795, + -1.5045251684066236, + -0.12797917425632477, + -0.6401674287554833, + -0.784422755241394, + 0.7844228148460388, + -0.7843869924545288, + 0.784386932849884, + -0.08111386001110077, + 0.08569706976413727, + -0.06478036195039749, + 0.0642930343747139, + 0.012104835361242294, + -0.024893851950764656, + -0.03344278782606125, + 0.03477102145552635, + 0.41082486510276794, + -0.40017110109329224, + 0.4489997625350952, + -0.4501209259033203 + ], + "end_q": [ + 0.007592812646180391, + 0.3141592653589793, + -5.818599788653955e-07, + 0.4363473355770111, + -0.9836950898170471, + 1.559449444877257, + 0.5364065766334534, + -0.324616735361651, + 0.2651083469390869, + -1.396628058127769, + -1.2293621301651, + 1.4303263386041423, + 0.5695574879646301, + 0.04389657502147506, + 1.461108684539795, + -1.5045251684066236, + -0.12797917425632477, + -0.6401674287554833, + -0.784422755241394, + 0.7844228148460388, + -0.7843869924545288, + 0.784386932849884, + -0.08111386001110077, + 0.08569706976413727, + -0.06478036195039749, + 0.0642930343747139, + 0.012104835361242294, + -0.024893851950764656, + -0.03344278782606125, + 0.03477102145552635, + 0.41082486510276794, + -0.40017110109329224, + 0.4489997625350952, + -0.4501209259033203 + ], + "base": [ + [ + -0.03669978701338178, + 0.9993263359049297, + 0.0, + -1.1784656618505867 + ], + [ + -0.9993263359049297, + -0.03669978701338178, + -0.0, + 1.138011564971114 + ], + [ + -0.0, + 0.0, + 1.0, + 0.0013677850365638733 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + 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0000000000000000000000000000000000000000..4aa2d2b8247b2b27e339307a55275f51e2105f8b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CASE8_ITEM02_FOCUSED_CURRENT_APERTURE_PROPOSAL.json @@ -0,0 +1,369 @@ +{ + "schema": "multi_item.polar_full_place.v1", + "read_only": true, + "layout_id": 8, + "item_index": 2, + "status_at_probe": { + "status": "WAITING_FOR_PLANNING_REPAIR", + "phase": "item_02_place_planning", + "item_index": 2, + "completed_objects": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/chomps_r" + ], + "control_ticks": 31505, + "updated": 1791337251.5566602, + "base_pose": [ + -1.2751731872558594, + 1.1961491107940674, + 0.0013680234551429749, + -1.3690441846847534e-07, + 3.6052369978278875e-09, + -0.7330639958381653, + 0.6801598072052002 + ], + "target_pose": [ + 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/COMMAND.json new file mode 100644 index 0000000000000000000000000000000000000000..05f35813cafc7f8332b6688f05539c856f52308f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/COMMAND.json @@ -0,0 +1,20 @@ +[ + "docker", + "exec", + "--user", + "0:0", + "-e", + "CHAIN_PROJECT_ROOT=/mnt/agent-bench-data/lingjun/ziche", + "-e", + "CHAIN_RENDER=1", + "-e", + "OMP_NUM_THREADS=2", + "-e", + "OPENBLAS_NUM_THREADS=1", + "-e", + "MKL_NUM_THREADS=1", + "ziche_shelf_chain50_gpu3", + "/isaac-sim/python.sh", + "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/code_snapshot/run_episode.py", + "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08" +] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CONTACT_CAPTURE.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CONTACT_CAPTURE.json new file mode 100644 index 0000000000000000000000000000000000000000..133071e44f4f3621c53cee9a14fa6f8539d13c04 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CONTACT_CAPTURE.json @@ -0,0 +1,14 @@ +{ + "schema": "ziche.contact_capture.v1", + "requested_targets": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/chomps_r", + "/World/Assets/Tier2Items/pring_bbq" + ], + "filter_semantics": "robot_or_any_requested_target", + "all_physics_ticks": true, + "control_ticks": 35076, + "dropped_rows": 0, + "callback_rows": 1957543, + "stream": "contacts.jsonl.gz" +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CONTACT_CAPTURE_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/CONTACT_CAPTURE_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..e89b1403ca23371a1bed579c38f9fbc03de028e8 --- /dev/null +++ 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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/FINE_REPAIR_COMPLETION_REVIEW.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/FINE_REPAIR_COMPLETION_REVIEW.json new file mode 100644 index 0000000000000000000000000000000000000000..31422042983bced148612aef246a57c9fa44954c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/FINE_REPAIR_COMPLETION_REVIEW.json @@ -0,0 +1,11 @@ +{ + "physics_complete": true, + "task_passed": true, + "task_failures": [], + "head_passed": true, + "videos_passed": true, + "training_frames": 11692, + "accepted": false, + "scope": "Native task/stable-start/no-fall/head/three-view decode and visual panels; all-tick geometry is separate", + "updated": 1791338638.9409513 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_RETENTION_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_RETENTION_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..7b8b89becae52ba830b91c001036dc471a3d6323 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_RETENTION_AUDIT.json @@ -0,0 +1,367 @@ +{ + "schema": "ziche.multi_item.grasp_retention_audit.v1", + "passed": true, + "semantics": "distinct 60 Hz control ticks; sustained opposing active-finger contact; final-tail contact; no target/source-or-clutter contact", + "items": [ + { + "item_index": 0, + "target": "/World/Assets/ShelfItems/cayman_jacked", + "active_hand": "l", + "gripper_prefix": "/World/Robot/gripper_l", + "shelf_pick_tick_range": [ + 91, + 1545 + ], + "window_control_ticks": 30, + "window_start_tick": 1516, + "window_end_tick": 1545, + "inner_contact_ticks": [ + 1516, + 1517, + 1518, + 1519, + 1520, + 1521, + 1522, + 1523, + 1524, + 1525, + 1526, + 1527, + 1528, + 1529, + 1530, + 1531, + 1532, + 1533, + 1534, + 1535, + 1536, + 1537, + 1538, + 1539, + 1540, + 1541, + 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"/World/Robot/gripper_r", + "shelf_pick_tick_range": [ + 12132, + 13703 + ], + "window_control_ticks": 30, + "window_start_tick": 13674, + "window_end_tick": 13703, + "inner_contact_ticks": [ + 13674, + 13675, + 13676, + 13677, + 13678, + 13679, + 13680, + 13681, + 13682, + 13683, + 13684, + 13685, + 13686, + 13687, + 13688, + 13689, + 13690, + 13691, + 13692, + 13693, + 13694, + 13695, + 13696, + 13697, + 13698, + 13699, + 13700, + 13701, + 13702, + 13703 + ], + "outer_contact_ticks": [ + 13674, + 13675, + 13676, + 13677, + 13678, + 13679, + 13680, + 13681, + 13682, + 13683, + 13684, + 13685, + 13686, + 13687, + 13688, + 13689, + 13690, + 13691, + 13692, + 13693, + 13694, + 13695, + 13696, + 13697, + 13698, + 13699, + 13700, + 13701, + 13702, + 13703 + ], + "simultaneous_bilateral_contact_ticks": [ + 13674, + 13675, + 13676, + 13677, + 13678, + 13679, + 13680, + 13681, + 13682, + 13683, + 13684, + 13685, + 13686, + 13687, + 13688, + 13689, + 13690, + 13691, + 13692, + 13693, + 13694, 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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json new file mode 100644 index 0000000000000000000000000000000000000000..f11c9353752a57bf69e1bbbfa5b86b16ae314a7f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/GRASP_STABILITY_AUDIT.json @@ -0,0 +1,97 @@ +{ + "passed": true, + "items": [ + { + "item_index": 0, + "target": "/World/Assets/ShelfItems/cayman_jacked", + "hand": "l", + "passed": true, + "native_hz": 60, + "navigation_samples": 2975, + "contiguous_navigation_intervals": [ + { + "first_source_control_tick": 1546, + "last_source_control_tick": 4520, + "samples": 2975 + } + ], + "first_source_control_tick": 1546, + "last_source_control_tick": 4520, + "maximum_object_in_hand_translation_drift_m": 3.984453864209343e-05, + "maximum_hand_highpass_residual_m": 5.379475719477223e-05, + "maximum_hand_highpass_angular_residual_rad": 1.993344587052018e-05, + "angular_highpass_limit_rad": 0.05235987755982989, + "hand_highpass_p99_m": 5.1602579123658125e-05, + "maximum_consecutive_highpass_exceedance_ticks": 0, + "slip_limit_m": 0.025, + "hand_highpass_limit_m": 0.005, + "sustained_threshold_ticks": 6, + "filter_window_ticks": 7 + }, + { + "item_index": 1, + "target": "/World/Assets/Tier2Items/chomps_r", + "hand": "r", + "passed": true, + "native_hz": 60, + "navigation_samples": 5072, + "contiguous_navigation_intervals": [ + { + "first_source_control_tick": 13704, + "last_source_control_tick": 16866, + "samples": 3163 + }, + { + "first_source_control_tick": 17478, + "last_source_control_tick": 19386, + "samples": 1909 + } + ], + "first_source_control_tick": 13704, + "last_source_control_tick": 19386, + "maximum_object_in_hand_translation_drift_m": 0.0002154860296517221, + "maximum_hand_highpass_residual_m": 2.171400294207301e-05, + "maximum_hand_highpass_angular_residual_rad": 2.43474357261414e-05, + "angular_highpass_limit_rad": 0.05235987755982989, + "hand_highpass_p99_m": 2.0725264450594394e-05, + "maximum_consecutive_highpass_exceedance_ticks": 0, + "slip_limit_m": 0.025, + "hand_highpass_limit_m": 0.005, + "sustained_threshold_ticks": 6, + "filter_window_ticks": 7 + }, + { + "item_index": 2, + "target": "/World/Assets/Tier2Items/pring_bbq", + "hand": "l", + "passed": true, + "native_hz": 60, + "navigation_samples": 4356, + "contiguous_navigation_intervals": [ + { + "first_source_control_tick": 28311, + "last_source_control_tick": 30894, + "samples": 2584 + }, + { + "first_source_control_tick": 31506, + "last_source_control_tick": 33277, + "samples": 1772 + } + ], + "first_source_control_tick": 28311, + "last_source_control_tick": 33277, + "maximum_object_in_hand_translation_drift_m": 7.143928449303156e-06, + "maximum_hand_highpass_residual_m": 2.1261577505526573e-05, + "maximum_hand_highpass_angular_residual_rad": 3.0411946739798907e-05, + "angular_highpass_limit_rad": 0.05235987755982989, + "hand_highpass_p99_m": 2.080961914967779e-05, + "maximum_consecutive_highpass_exceedance_ticks": 0, + "slip_limit_m": 0.025, + "hand_highpass_limit_m": 0.005, + "sustained_threshold_ticks": 6, + "filter_window_ticks": 7 + } + ], + "semantics": "Measured hand pose relative to moving base and each active target pose relative to its assigned hand; slip retains one reference across the full item carry; high-pass filters reset only at gaps in source control ticks. All thresholds are unchanged." +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INITIAL_PROP_NO_FALL.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INITIAL_PROP_NO_FALL.json new file mode 100644 index 0000000000000000000000000000000000000000..afc0da5f5210f821f84c724faeafb94a92e04f09 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INITIAL_PROP_NO_FALL.json @@ -0,0 +1,15 @@ +{ + "passed": true, + "physics_samples": 38, + "maxima": { + "translation_m": 0.0005652907024400683, + "downward_m": 0.0005652904510498047, + "rotation_rad": 0.0049856685987076646 + }, + "thresholds": { + "translation_m": 0.02, + "downward_m": 0.01, + "rotation_deg": 15.0 + }, + "all_requested_and_nonrequested_props_included": true +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INPUT_QUALIFICATION.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INPUT_QUALIFICATION.json new file mode 100644 index 0000000000000000000000000000000000000000..9587d0f75e94f6b1148b5a512f48191698ea06f8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/INPUT_QUALIFICATION.json @@ -0,0 +1,66 @@ +{ + "schema": "shelf_to_table_multi10.fresh_input_qualification.v1", + "case_id": 8, + "passed": true, + "native_physics_still_required": true, + "checks": { + "fresh_no_rollout": true, + "empty_initial_tray": true, + "item_count_3_or_4": true, + "unique_targets": true, + "all_targets_active": true, + "all_paths_new_root": true, + "controls_valid": true, + "slot_floor_containment": true, + "slot_pair_gap_12mm": true, + "unstable_nonrequested_prims_inactive": true + }, + "controls": [ + { + "path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_01.npz", + "sha256": "c098f53c89573abbbe5b66a9da6d80310c927c2d8bcc5fab2433a6e159b417bf", + "fields": [ + "names", + "initial", + "pick", + "place_initial", + "target_local" + ], + "valid": true + }, + { + "path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_02.npz", + "sha256": "4a6d6813bcc454091bd26e9b32d366e35b63b1d74bc5dc6153adbeb07a67cbd3", + "fields": [ + "names", + "initial", + "pick", + "place_initial", + "target_local" + ], + "valid": true + }, + { + "path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_03.npz", + "sha256": "68a59a5010fc82d759bb0f90fb00931c7876041975e1da7d0a13c1c593bca65b", + "fields": [ + "names", + "initial", + "pick", + "place_initial", + "target_local" + ], + "valid": true + } + ], + "special_evidence": { + "inactive": [ + "zbar_t", + "coke_c3" + ] + }, + "job_sha256": "e038d5e86f12e7c8068ed563dcbee1f8e488b797a975df60936f6bef0c39b3c0", + "scene_sha256": "3bee53de0da633ce7cb7b6895579430645af68aa1c5a398c33d3dac2ab140eff", + "scene_physical_sha256": 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"ziche.multi_item_events.v1", + "requested_objects": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/chomps_r", + "/World/Assets/Tier2Items/pring_bbq" + ], + "completed_objects": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/chomps_r", + "/World/Assets/Tier2Items/pring_bbq" + ], + "events": [ + { + "event": "item_started", + "item_index": 0, + "target": "/World/Assets/ShelfItems/cayman_jacked", + "tick": 31, + "pose": [ + -1.6646485328674316, + 2.1425955295562744, + 1.1825048923492432, + -3.6640977896240656e-07, + -1.2567275575747772e-07, + -0.13246968388557434, + 0.9911870956420898 + ] + }, + { + "event": "item_supported", + "item_index": 0, + "target": "/World/Assets/ShelfItems/cayman_jacked", + "tick": 6410, + "metrics": { + "target": "/World/Assets/ShelfItems/cayman_jacked", + "tick": 6410, + "pose": [ + -1.2393547296524048, + 0.4538394808769226, + 0.4549056589603424, + 2.697435661502823e-07, + 9.032094538952151e-08, + 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a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json new file mode 100644 index 0000000000000000000000000000000000000000..09724810733add82e83520b25593505d72107dbc --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/OFFICIAL_LEROBOT_READER_QA.json @@ -0,0 +1,196 @@ +{ + "schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": "2026-10-07T02:04:21.979969+00:00", + "layout_id": 8, + "passed": true, + 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"note": "First/middle/last rows and all three images decoded through the official reader; full sequential decode is reused by exact video hashes." +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/PARQUET_ALIGNMENT_CHECK.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/PARQUET_ALIGNMENT_CHECK.json new file mode 100644 index 0000000000000000000000000000000000000000..11346ad42b54d4686bd2899de71652ad29c584c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/PARQUET_ALIGNMENT_CHECK.json @@ -0,0 +1,11704 @@ 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/action_space.schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b57d8dcb05b4dcae2b555d1a87772be3b0d72361 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/action_space.schema.json @@ -0,0 +1,66 @@ +{ + "version": "g1_omnipicker_jointpos20_basevw_v1", + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "state_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_x_world_m", + "base_y_world_m", + "base_yaw_world_unwrapped_rad" + ], + "dtype": "float32", + "physics_hz": 240, + "control_hz": 60, + "training_hz": 20, + "action_semantics": "applied_absolute_joint_position_targets_and_body_forward_yaw_velocity", + "state_semantics": "post_control_step_measured_joint_positions_and_measured_base_pose", + "sample_native_indices": "arange(2,N,3)", + "action_state_shift": 0, + "base_limits": { + "forward_m_s": 0.18, + "yaw_rate_rad_s": 0.25, + "linear_acceleration_m_s2": 0.12, + "angular_acceleration_rad_s2": 0.3 + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_profile.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_profile.usda new file mode 100644 index 0000000000000000000000000000000000000000..aec430fb82fd4c0c8c66bb77dc8911b8ef8ffb1f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_profile.usda @@ -0,0 +1,4084 @@ +#usda 1.0 + +over "World" +{ + over "Assets" + { + over "ShelvingUnit" + { + over "o" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "root" + { + over "GLTF_SceneRootNode" + { + over "shelf_boards_0" + { + over "Object_4" + { + over "Object_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "horizontal_brackets_1" + { + over "Object_6" + { + over "Object_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "vertical_beams_2" + { + over "Object_8" + { + over "Object_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Object_9" + { + over "Object_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "plastic_feet_3" + { + over "Object_11" + { + over "Object_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Sofa" + { + over "Obj" + { + over "Meshes" + { + over "FAB_converted_model" + { + over "root" + { + over "GLTF_SceneRootNode" + { + over "__seat_sofa_0" + { + over "Object_4" + { + over "Object_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Clock" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_e85f8bde8074f96b8d272e4074a5c8d_fbx" + { + over "RootNode" + { + over "clock_frame" + { + over "clock_frame_clock_frame_0" + { + over "clock_frame_clock_frame_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_face" + { + over "clock_face_clock_face_0" + { + over "clock_face_clock_face_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_big_hand" + { + over "clock_big_hand_clock_pointer_0" + { + over "clock_big_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_second_hand" + { + over "clock_second_hand_clock_pointer_0" + { + over "clock_second_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "clock_small_hand" + { + over "clock_small_hand_clock_pointer_0" + { + over "clock_small_hand_clock_pointer_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "Tv" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "Livingroom_TV_fbx" + { + over "RootNode" + { + over "Livingroom_TV" + { + over "TV_Legs" + { + over "Object_5" + { + over "TV_Legs_Livingroom_TV_0" + { + over "TV_Legs_Livingroom_TV_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "TV_Screen" + { + over "Object_8" + { + over "TV_Screen_Livingroom_TV_0" + { + over "TV_Screen_Livingroom_TV_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + } + + over "TvTable" + { + over "Obj" + { + over "Meshes" + { + over "FAB_converted_model" + { + over "tv_table_fbx" + { + over "RootNode" + { + over "P0627_Media_Storage01" + { + over "P0627_Media_Storage01_P0627_Media_Storage01_MAT01_0" + { + over "P0627_Media_Storage01_P0627_Media_Storage01_MAT01_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "CoffeeTable" + { + over "Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "TrashBinRed" + { + over "geo" + { + over "Cube_022" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Trash_Bag" + { + over "Trash_Bag_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "TrashBinTeal" + { + over "geo" + { + over "Cube_022" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Trash_Bag" + { + over "Trash_Bag_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "Door" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_9be505408364da7a3ccc07ac09dbe59_fbx" + { + over "RootNode" + { + over "Door_Frame" + { + over "Door_Frame_Door_0" + { + over "Door_Frame_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door" + { + over "Door_Door_0" + { + over "Door_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Lever" + { + over "Lever_Door_0" + { + over "Lever_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Latch_Bolt" + { + over "Latch_Bolt_Door_0" + { + over "Latch_Bolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Deadbolt" + { + over "Deadbolt_Door_0" + { + over "Deadbolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + } + + over "Fireplace" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "fireplace_fbx" + { + over "RootNode" + { + over "fireplace" + { + over "fireplace_fireplace_0" + { + over "fireplace_fireplace_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "SmallCoffeeTable" + { + over "geo" + { + over "组003" + { + over "Rectangle058" + { + over "Rectangle058" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle059" + { + over "Rectangle059" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle060" + { + over "Rectangle060" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle061" + { + over "Rectangle061" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle062" + { + over "Rectangle062" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle079" + { + over "Rectangle079" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Line055" + { + over "Line055" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle063" + { + over "Rectangle063" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle064" + { + over "Rectangle064" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle067" + { + over "Rectangle067" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "组001" + { + over "Line056" + { + over "Line056" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Rectangle074" + { + over "Rectangle074" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle075" + { + over "Rectangle075" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle076" + { + over "Rectangle076" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "组002" + { + over "Line057" + { + over "Line057" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Rectangle077" + { + over "Rectangle077" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle078" + { + over "Rectangle078" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rectangle054" + { + over "Rectangle054" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Line054" + { + over "Line054" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + + over "TableclothBasket" + { + over "riser" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "model" + { + over "Geometry" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_dc85028501a4347a20df6a75b3e3044_fbx" + { + over "RootNode" + { + over "pCube2" + { + over "pCube2_Texture_0" + { + over "pCube2_Texture_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + + over "CoatRack" + { + over "MainTrunk" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseHub" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch01Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch01Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch02Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch02Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch03Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch03Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch04Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch04Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch05Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch05Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch06Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch06Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch07Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch07Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch08Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Branch08Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg01Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg01Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg02Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg02Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg03Shaft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "BaseLeg03Tip" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "HouseEntryDoor" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_9be505408364da7a3ccc07ac09dbe59_fbx" + { + over "RootNode" + { + over "Door_Frame" + { + over "Door_Frame_Door_0" + { + over "Door_Frame_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door" + { + over "Door_Door_0" + { + over "Door_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Lever" + { + over "Lever_Door_0" + { + over "Lever_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Latch_Bolt" + { + over "Latch_Bolt_Door_0" + { + over "Latch_Bolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Deadbolt" + { + over "Deadbolt_Door_0" + { + over "Deadbolt_Door_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + } + + over "LivingRoomRug" + { + over "Surface" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "OuterBorder" + { + over "West" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "East" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "South" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "North" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "InnerBorder" + { + over "West" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "East" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "South" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "North" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + } + + over "PantrySlidingDoor" + { + over "Plank_01" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_02" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_03" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_04" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_05" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_06" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Plank_07" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PlankFace_07" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "FrameLeft" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameRight" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameBottom" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "FrameTop" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "DiagonalBrace" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Hardware" + { + over "Rail" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Hanger_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wheel_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Hanger_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wheel_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Stop_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Stop_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Pull" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PullMount_73" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "PullMount_103" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + } + + over "FireplaceArtwork" + { + over "Model" + { + over "dkg_Wall_Frames_16a" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LivingRoomPlantStands" + { + over "TvNorth" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "PLANTER_VOLUME_SMALL_PLANTER_VOLUME_SMALL_Mat_0" + { + over "PLANTER_VOLUME_SMALL_PLANTER_VOLUME_SMALL_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "FICUS_ELASTICA" + { + over "FICUS_ELASTICA_FICUS_ELASTICA_Mat_0" + { + over "FICUS_ELASTICA_FICUS_ELASTICA_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "WestWindowRight" + { + over "Stand" + { + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "TopTexture" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "LegLB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegLF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRB" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LegRF" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "LowerShelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "Plant" + { + over "Model" + { + over "PLANTER_VOLUME_TALL_PLANTER_VOLUME_TALL_Mat_0" + { + over "PLANTER_VOLUME_TALL_PLANTER_VOLUME_TALL_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "FICUS_AUDREY" + { + over "FICUS_AUDREY_FICUS_AUDREY_Mat_0" + { + over "FICUS_AUDREY_FICUS_AUDREY_Mat_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + + over "GrandPianoAndBench17" + { + over "bench" + { + over "bench" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Piano" + { + over "Piano" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CoffeeGrinder" + { + over "BodyVisual" + { + over "Model" + { + over "DBM8_Grinder_ROOT" + { + over "Main_Base" + { + over "Cube" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_L_B" + { + over "Cube_003" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Clear" + { + over "Cube_009" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Opening" + { + over "Cylinder_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_180" + { + over "Cube_064" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Bottom_Band" + { + over "Cube_012" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Hopper_Adjustment_Ring" + { + over "Cylinder" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_0" + { + over "Cube_062" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Bean_Hopper_Top_Rim" + { + over "Bean_Hopper_Top_Rim_Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Bean_Hopper_Clear" + { + over "Bean_Hopper_Clear_Mesh" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_R_B" + { + over "Cube_005" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Start_Button" + { + over "Cube_069" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Top_Deck" + { + over "Cube_008" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Top_Band" + { + over "Cube_011" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Slider_Groove" + { + over "Cube_067" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Motor_Housing" + { + over "Cube_006" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Stainless_Grind_Selector_Collar" + { + over "Cone" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cuisinart_Label" + { + over "Text" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Base_Stainless_Trim" + { + over "Cube_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Selector_Dots" + { + over "Sphere" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_R_F" + { + over "Cube_004" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Center_Dome" + { + over "Sphere_018" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Start_Label" + { + over "Text_001" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Motor_Housing_Rear_Cap" + { + over "Cube_007" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Interior_Back" + { + over "Cube_010" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_90" + { + over "Cube_063" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Grind_Chamber_Bottom_Trim" + { + over "Cube_013" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Rubber_Foot_L_F" + { + over "Cube_002" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Slider_Knob" + { + over "Cube_068" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Burr_Support_Rib_270" + { + over "Cube_065" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Cup_Count_Control_Panel" + { + over "Cube_066" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + + over "Task5PackStation" + { + over "Cabinet" + { + over "SideL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "SideR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Back" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Bottom" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Top" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Flap0" + { + over "Panel" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat00" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat07" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Recess" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandleBar" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Flap1" + { + over "Panel" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat00" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat01" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat02" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat03" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat04" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat05" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Slat06" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Recess" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandleBar" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostL" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "HandlePostR" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Plinth" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Inside" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "Tray" + { + over "Obj" + { + over "Meshes" + { + over "Sketchfab_model" + { + over "_99fa031bb144e19baa97558c9a1ba91_fbx" + { + over "RootNode" + { + over "OPM0047" + { + over "OPM0047_OPM0047_0" + { + over "OPM0047_OPM0047_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + } + } + } + } + + over "ShelfItems" + { + over "cayman_jacked" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "gatorade_m" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "gatorade_t" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "starbucks" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "good2grow" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "grapefruit_can" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "coke_c3" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "sprite_c1" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "good2grow_copy_01" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + } + + over "Tier2Items" + { + over "chomps_r" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "pring_bbq" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "honeymaid" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "zbar_t" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + + over "skit_littles" ( + prepend apiSchemas = ["PhysxRigidBodyAPI"] + ) + { + bool physxRigidBody:enableCCD = 1 + } + } + } + + over "Background" + { + over "Shell" + { + over "ConstructorKitchen" + { + over "MetricScale" + { + over "BP_Kitchen7" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen8" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen9" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen10" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Sink" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Tap" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "BP_Kitchen36" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen16" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen25" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen21" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen27" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen18" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen22" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen28" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Door_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Right_Body_4" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "BP_Kitchen32" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightEnd300" + { + over "Module" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "RightRange600" + { + over "Module" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Box_In_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Oven" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Oven_Door" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Stove" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + + over "RightWallBase400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallBase150" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallUpper400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "RightWallUpper150" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnBase150_C" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnUpper400" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "LeftReturnScribe" + { + over "Base" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Upper" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "CornerGapBase300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapBase300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Worktop" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapUpper300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "CornerGapUpper300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_A" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_B" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "OverFridgeUpper300_C" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Door_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Knob_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Join_Left_Body_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + + over "SquareEndUpper300" + { + over "Body" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + + over "Shelf_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Shelf_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + } + } + } + + over "CounterBase_16" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CounterTop_17" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "LShape17" + { + over "Floor" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "Wall_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Skirt_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wall_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Skirt_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wall_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Skirt_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wall_5_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wall_5_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "Wall_5_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CornerClosure_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CornerClosure_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CornerClosure_2" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CornerClosure_3" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "CornerClosure_5" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "EastDoorWallCombined" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "EastSkirtCombined_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "EastSkirtCombined_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NorthEntryWallCombined" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + + over "NorthSkirtCombined_0" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + + over "NorthSkirtCombined_1" ( + prepend apiSchemas = ["PhysicsCollisionAPI"] + ) + { + bool physics:collisionEnabled = 1 + } + } + + over "ContinuousCeiling" ( + prepend apiSchemas = ["PhysicsCollisionAPI", "PhysicsMeshCollisionAPI"] + ) + { + uniform token physics:approximation = "none" + bool physics:collisionEnabled = 1 + } + } + + over "PhysicsScene" ( + prepend apiSchemas = ["PhysxSceneAPI"] + ) + { + bool physxScene:enableCCD = 1 + } + + over "CanonicalTableclothBasketCollisionGroup" + { + rel physics:filteredGroups = None + } +} + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_000.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_000.json new file mode 100644 index 0000000000000000000000000000000000000000..14adc93b34c576321fd1061c7da83ac91df691fd --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_000.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_base_link/collisions/mesh_0/cylinder", + "index": 0, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_001.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_001.json new file mode 100644 index 0000000000000000000000000000000000000000..c42b5214bcc6a03bb272961f9383a7181d7dd4df --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_001.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_end_link/collisions/mesh_0/cylinder", + "index": 1, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_002.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_002.json new file mode 100644 index 0000000000000000000000000000000000000000..8845a71a0adc5d331be11834ab1724a4dcccbb57 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_002.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_link1/collisions/mesh_0/cylinder", + "index": 2, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_003.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_003.json new file mode 100644 index 0000000000000000000000000000000000000000..4703016fab036f1bb384b0ca44cc22f119ecc024 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_003.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_link2/collisions/mesh_0/cylinder", + "index": 3, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_004.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_004.json new file mode 100644 index 0000000000000000000000000000000000000000..37665307e2d06782f7ecebfd3dfd112d290ca10e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_004.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_link3/collisions/mesh_0/cylinder", + "index": 4, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_005.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_005.json new file mode 100644 index 0000000000000000000000000000000000000000..fabf6ed1a3107bbe8349cd62a5bf130b4a9cabf1 --- /dev/null +++ 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0000000000000000000000000000000000000000..42de0ce0f79b3690d31ff0d9da2fc8f422d69a35 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_006.json @@ -0,0 +1,8 @@ +{ + "path": "/World/Robot/arm_l_link5/collisions/mesh_0/cylinder", + "index": 6, + "type": "Cylinder", + "status": "ANALYTIC_EXACT", + "first_observed_frame": 11691, + "first_observed_tick": 35075 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/collision_readback/COLLIDER_007.json 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/navigation_primitives.item_01.json new file mode 100644 index 0000000000000000000000000000000000000000..c1c201e5208d66f73ec3fa443f621d1a94ad9c67 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/navigation_primitives.item_01.json @@ -0,0 +1,70 @@ +[ + { + "kind": "drive", + "goal": [ + -1.6106014189328766, + 1.160495973140685, + 1.503964790756646 + ], + "direction": -1 + }, + { + "kind": "turn", + "goal": [ + -1.6106014189328766, + 1.160495973140685, + 7.001751934538784 + ] + }, + { + "kind": "drive", + "goal": [ + -1.4828554907964377, + 1.272214269961572, + 7.001751934538784 + ], + "direction": 1 + }, + { + "kind": "turn", + "goal": [ + -1.4828554907964377, + 1.272214269961572, + 6.2163537711413355 + ] + }, + { + "kind": "drive", + "goal": [ + -1.243391265839112, + 1.25618663864602, + 6.2163537711413355 + ], + "direction": 1 + }, + { + "kind": "turn", + "goal": [ + -1.243391265839112, + 1.25618663864602, + -1.5148278850372507 + ] + }, + { + "kind": "drive", + "goal": [ + -1.2366441506955417, + 1.1357604051666412, + -1.5148278850372507 + ], + "direction": 1 + }, + { + "kind": "turn", + "goal": [ + -1.2366441506955417, + 1.1357604051666412, + -1.5819751096581838 + ] + } +] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/navigation_primitives.item_02.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/navigation_primitives.item_02.json new file mode 100644 index 0000000000000000000000000000000000000000..6f34b10ab3d9de6648c5a3cfb190900ff6e116ba --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/navigation_primitives.item_02.json @@ -0,0 +1,36 @@ +[ + { + "kind": "drive", + "goal": [ + -1.584153587606117, + 1.2087973454693266, + 1.6060375491311787 + ], + "direction": -1 + }, + { + "kind": "turn", + "goal": [ + -1.584153587606117, + 1.2087973454693266, + -0.04568023837551771 + ] + }, + { + "kind": "drive", + "goal": [ + -1.2740081064697775, + 1.194619963348184, + -0.045680238375517816 + ], + "direction": 1 + }, + { + "kind": "turn", + "goal": [ + -1.2740081064697775, + 1.194619963348184, + -1.6464991739431145 + ] + } +] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/place_metrics.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/place_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..76600af63f87b6ede414a95d84ce40381d1acaa1 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/place_metrics.json @@ -0,0 +1,13 @@ +{ + "hands": [ + "l", + "r", + "l" + ], + "destination_body_path": "/World/Assets/Tray", + "completed_objects": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/chomps_r", + "/World/Assets/Tier2Items/pring_bbq" + ] +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/recording_metadata.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/recording_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..09dbf2c3b911b913497899a14e7152b590ce3f58 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/recording_metadata.json @@ -0,0 +1,15 @@ +{ + "physics_hz": 240, + "control_hz": 60, + "training_hz": 20, + "action_schema": "g1_omnipicker_jointpos20_basevw_v1", + "gripper_max_force": { + "l": 80, + "r": 80 + }, + "pose_order": "x,y,z,qx,qy,qz,qw", + "row_clock": "post-control-step; same-step applied command", + "continuous_no_pose_reset": true, + "multi_item": true, + "item_count": 3 +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/applied/102a9780f8c0d9d15a8363982abd4076b7360d515361ab5737b01ffdec2f8483.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/applied/102a9780f8c0d9d15a8363982abd4076b7360d515361ab5737b01ffdec2f8483.py new file mode 100644 index 0000000000000000000000000000000000000000..1405f8fa56389d8455e8c72b1c3874e69104d86d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/applied/102a9780f8c0d9d15a8363982abd4076b7360d515361ab5737b01ffdec2f8483.py @@ -0,0 +1,384 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + if release_sweep is not None:release_sweep=condition_release_on_live_aperture(release_sweep) + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + + +def condition_release_on_live_aperture(sweep): + """Screen opening from the actual grasp, excluding source-only re-closing. + + Source linkage samples are retained from the first aperture at least as open + as the current grasp. All joints are interpolated at <= 0.005 rad per sample; + every resulting configuration still receives the original collision checks. + No commanded/native action, contact exception, or tolerance changes here. + """ + import types + if getattr(sweep, '_live_aperture_conditioned', False):return sweep + original_check=sweep.check + source_path=sweep.source_open_path.copy() + hand='l' if any('gripper_l' in sweep.names[i] for i in sweep.gripper_indices) else 'r' + outer_name=f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1" + outer_local=sweep.gripper_indices.index(sweep.names.index(outer_name)) + def checked(self, landing_q, retreat_q, base, target_pose): + live=np.asarray(landing_q)[self.gripper_indices].copy() + # The actuated outer joint increases monotonically toward pi/4 when opening. + keep=np.flatnonzero(source_path[:,outer_local]>=live[outer_local]-1e-8) + if not len(keep):raise RuntimeError('Source release never reaches live aperture') + first=int(keep[0]);samples=[live] + for stop in list(source_path[first:])+[self.source_open_final]: + start=samples[-1] + if stop[outer_local]=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,up]) for up in [.15,.12,.10,.08]] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.05,.10),(.08,.08),(.10,.05),(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + + +def condition_release_on_live_aperture(sweep): + """Screen opening from the actual grasp, excluding source-only re-closing. + + Source linkage samples are retained from the first aperture at least as open + as the current grasp. All joints are interpolated at <= 0.005 rad per sample; + every resulting configuration still receives the original collision checks. + No commanded/native action, contact exception, or tolerance changes here. + """ + import types + if getattr(sweep, '_live_aperture_conditioned', False):return sweep + original_check=sweep.check + source_path=sweep.source_open_path.copy() + hand='l' if any('gripper_l' in sweep.names[i] for i in sweep.gripper_indices) else 'r' + outer_name=f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1" + outer_local=sweep.gripper_indices.index(sweep.names.index(outer_name)) + def checked(self, landing_q, retreat_q, base, target_pose): + live=np.asarray(landing_q)[self.gripper_indices].copy() + # The actuated outer joint increases monotonically toward pi/4 when opening. + keep=np.flatnonzero(source_path[:,outer_local]>=live[outer_local]-1e-8) + if not len(keep):raise RuntimeError('Source release never reaches live aperture') + first=int(keep[0]);samples=[live] + for stop in list(source_path[first:])+[self.source_open_final]: + start=samples[-1] + if stop[outer_local]= len(g['items']): + return result + + def plan_upright(): + from multi_motion import EmptyClearance + actual = np.asarray(g['robot'].get_joint_positions(), dtype=float).copy() + end_control = np.asarray(g['native'])[g['ids']].copy() + control_index = list(g['control_names']).index('idx02_body_joint2') + end_control[control_index] = target_pitch + end = actual.copy() + end[g['ids']] = end_control + base = np.asarray(g['mat'](g['base'])) + checker = EmptyClearance(g['stage'], g['carry_robot'], g['names'], g['prop_live'](), deadline_s=900) + if not checker.segment(actual, end, base, base): + raise RuntimeError('Post-release upright motion requires repair: ' + str(checker.last)) + return end_control + + end_control = g['planning_boundary']('return_upright', item_job, plan_upright) + start_tick = len(g['rows']) + start_pose = np.asarray(g['base_state']()).copy() + original_blend(end_control, 6.) + done = True + g['dump'](Path(g['out']) / f'POST_RELEASE_LEVEL_RETURN.item_{target_index:02d}.json', + dict(proposal=str(path), proposal_sha256=proposal_sha256, body_pitch_deg=proposal['body_pitch_deg'], + start_tick=start_tick, end_tick=len(g['rows']), base_before=start_pose.tolist(), + base_after=np.asarray(g['base_state']()).tolist(), live_full_body_geometry_checked=True, + released_and_previously_placed_objects_included=True, pose_writes_or_resets=False)) + return result + + g['blend'] = blend_with_upright + +_reposition_apply = apply +def apply(g, item_job, record): + result = _reposition_apply(g, item_job, record) + install_level_return(g, item_job, '/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/CASE8_ITEM01_POST_RELEASE_UPRIGHT_PROPOSAL.json', 'd9ff18bf52c9edc30df01bef94ba69564fd039d24e9a87eb487ad12c7cb21544') + return result diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/item_01_place.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/item_01_place.json new file mode 100644 index 0000000000000000000000000000000000000000..ba55f028d18de187246d72438b55fb7b9083c9f2 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/item_01_place.json @@ -0,0 +1,584 @@ +{ + "schema": "ziche.multi_item_place_station_repair.v1", + "reason": "Fine repair at unchanged measured boundary; full carry, posture bridge, place descent, gripper opening and retreat qualified. 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b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/last_item_repair.lock new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick17477_9e6b9aef166c.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick17477_9e6b9aef166c.py new file mode 100644 index 0000000000000000000000000000000000000000..6cf11fb37078d23b7f70a8658d34b30bf1b8d3a5 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick17477_9e6b9aef166c.py @@ -0,0 +1,384 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + if release_sweep is not None:release_sweep=condition_release_on_live_aperture(release_sweep) + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,up]) for up in [.15,.12,.10,.08]] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.05,.10),(.08,.08),(.10,.05),(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + + +def condition_release_on_live_aperture(sweep): + """Screen opening from the actual grasp, excluding source-only re-closing. + + Source linkage samples are retained from the first aperture at least as open + as the current grasp. All joints are interpolated at <= 0.005 rad per sample; + every resulting configuration still receives the original collision checks. + No commanded/native action, contact exception, or tolerance changes here. + """ + import types + if getattr(sweep, '_live_aperture_conditioned', False):return sweep + original_check=sweep.check + source_path=sweep.source_open_path.copy() + hand='l' if any('gripper_l' in sweep.names[i] for i in sweep.gripper_indices) else 'r' + outer_name=f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1" + outer_local=sweep.gripper_indices.index(sweep.names.index(outer_name)) + def checked(self, landing_q, retreat_q, base, target_pose): + live=np.asarray(landing_q)[self.gripper_indices].copy() + # The actuated outer joint increases monotonically toward pi/4 when opening. + keep=np.flatnonzero(source_path[:,outer_local]>=live[outer_local]-1e-8) + if not len(keep):raise RuntimeError('Source release never reaches live aperture') + first=int(keep[0]);samples=[live] + for stop in list(source_path[first:])+[self.source_open_final]: + start=samples[-1] + if stop[outer_local]=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,up]) for up in [.15,.12,.10,.08]] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.05,.10),(.08,.08),(.10,.05),(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + + +def condition_release_on_live_aperture(sweep): + """Screen opening from the actual grasp, excluding source-only re-closing. + + Source linkage samples are retained from the first aperture at least as open + as the current grasp. All joints are interpolated at <= 0.005 rad per sample; + every resulting configuration still receives the original collision checks. + No commanded/native action, contact exception, or tolerance changes here. + """ + import types + if getattr(sweep, '_live_aperture_conditioned', False):return sweep + original_check=sweep.check + source_path=sweep.source_open_path.copy() + hand='l' if any('gripper_l' in sweep.names[i] for i in sweep.gripper_indices) else 'r' + outer_name=f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1" + outer_local=sweep.gripper_indices.index(sweep.names.index(outer_name)) + def checked(self, landing_q, retreat_q, base, target_pose): + live=np.asarray(landing_q)[self.gripper_indices].copy() + # The actuated outer joint increases monotonically toward pi/4 when opening. + keep=np.flatnonzero(source_path[:,outer_local]>=live[outer_local]-1e-8) + if not len(keep):raise RuntimeError('Source release never reaches live aperture') + first=int(keep[0]);samples=[live] + for stop in list(source_path[first:])+[self.source_open_final]: + start=samples[-1] + if stop[outer_local]= len(g['items']): + return result + + def plan_upright(): + from multi_motion import EmptyClearance + actual = np.asarray(g['robot'].get_joint_positions(), dtype=float).copy() + end_control = np.asarray(g['native'])[g['ids']].copy() + control_index = list(g['control_names']).index('idx02_body_joint2') + end_control[control_index] = target_pitch + end = actual.copy() + end[g['ids']] = end_control + base = np.asarray(g['mat'](g['base'])) + checker = EmptyClearance(g['stage'], g['carry_robot'], g['names'], g['prop_live'](), deadline_s=900) + if not checker.segment(actual, end, base, base): + raise RuntimeError('Post-release upright motion requires repair: ' + str(checker.last)) + return end_control + + end_control = g['planning_boundary']('return_upright', item_job, plan_upright) + start_tick = len(g['rows']) + start_pose = np.asarray(g['base_state']()).copy() + original_blend(end_control, 6.) + done = True + g['dump'](Path(g['out']) / f'POST_RELEASE_LEVEL_RETURN.item_{target_index:02d}.json', + dict(proposal=str(path), proposal_sha256=proposal_sha256, body_pitch_deg=proposal['body_pitch_deg'], + start_tick=start_tick, end_tick=len(g['rows']), base_before=start_pose.tolist(), + base_after=np.asarray(g['base_state']()).tolist(), live_full_body_geometry_checked=True, + released_and_previously_placed_objects_included=True, pose_writes_or_resets=False)) + return result + + g['blend'] = blend_with_upright + +_reposition_apply = apply +def apply(g, item_job, record): + result = _reposition_apply(g, item_job, record) + install_level_return(g, item_job, '/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/CASE8_ITEM01_POST_RELEASE_UPRIGHT_PROPOSAL.json', 'd9ff18bf52c9edc30df01bef94ba69564fd039d24e9a87eb487ad12c7cb21544') + return result diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick31505_3fa1fdae7d53.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick31505_3fa1fdae7d53.py new file mode 100644 index 0000000000000000000000000000000000000000..1405f8fa56389d8455e8c72b1c3874e69104d86d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/repairs/repair_case08_fine_tick31505_3fa1fdae7d53.py @@ -0,0 +1,384 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + if release_sweep is not None:release_sweep=condition_release_on_live_aperture(release_sweep) + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + + +def condition_release_on_live_aperture(sweep): + """Screen opening from the actual grasp, excluding source-only re-closing. + + Source linkage samples are retained from the first aperture at least as open + as the current grasp. All joints are interpolated at <= 0.005 rad per sample; + every resulting configuration still receives the original collision checks. + No commanded/native action, contact exception, or tolerance changes here. + """ + import types + if getattr(sweep, '_live_aperture_conditioned', False):return sweep + original_check=sweep.check + source_path=sweep.source_open_path.copy() + hand='l' if any('gripper_l' in sweep.names[i] for i in sweep.gripper_indices) else 'r' + outer_name=f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1" + outer_local=sweep.gripper_indices.index(sweep.names.index(outer_name)) + def checked(self, landing_q, retreat_q, base, target_pose): + live=np.asarray(landing_q)[self.gripper_indices].copy() + # The actuated outer joint increases monotonically toward pi/4 when opening. + keep=np.flatnonzero(source_path[:,outer_local]>=live[outer_local]-1e-8) + if not len(keep):raise RuntimeError('Source release never reaches live aperture') + first=int(keep[0]);samples=[live] + for stop in list(source_path[first:])+[self.source_open_final]: + start=samples[-1] + if stop[outer_local]1e-10 or displacement>1e-9:raise + return dict(method='bounded_float64_matrix_chain_roundoff',normalized_mount=before.tolist(),raw_end_mount=after.tolist(),raw_end_mount_max_coefficient_difference=coefficient,maximum_geometry_displacement_m=displacement,coefficient_bound=1e-10,geometry_displacement_bound_m=1e-9,normalized_model_is_live_PxMeshScale_identity=False,source_geometry_modified=False) + module.normalize_rigid_mount=normalize_roundoff + stage=Usd.Stage.Open(str(out/'scene.usda'));metadata=np.load(out/'full_state.npz',allow_pickle=False) + wrapped,AnalysisBody,adapter=module.build_extended_adapter(stage,out/'collision_readback',out/'collision_readback_nonrobot',metadata) + (out/('COOKED_ADAPTER_COVERAGE.json' if shard<0 else f'COOKED_ADAPTER_COVERAGE.part_{shard}.json')).write_text(json.dumps(adapter.receipt,indent=2)+'\n') + cs.collider_mesh=wrapped;cs.static_collision_mesh=wrapped;cs.Body=AnalysisBody + +if kind=='self': + import omni_native_audit as native + if adapter is not None:native.trimesh=types.SimpleNamespace(util=types.SimpleNamespace(concatenate=adapter.combine)) + native.audit(out/'physics_ticks/full_state.npz',out/'scene.usda',out/'SELF_COLLISION_AUDIT.json') +elif kind=='world': + item_index=int(os.environ['CHAIN_AUDIT_ITEM']);item=requested[item_index] + audit_target=item['target'];audit_hand=item['hand'] + import collision_initial_layout + from place_tray_layout import scene_geometry + from shapely.geometry import Polygon + original_surfaces=collision_initial_layout._surfaces + def place_surfaces(scene): + surfaces=original_surfaces(scene);geometry=scene_geometry(scene.stage,audit_target);top=float(geometry['tray_xyz'][2]+geometry['tray_floor_z']) + assert abs(top-job['tray_floor_z'])<1e-5 + surfaces.append(dict(top=top,polygon=Polygon(geometry['tray_floor']+geometry['tray_xyz'][:2]),paths=['/World/Assets/Tray']));return surfaces + collision_initial_layout._surfaces=place_surfaces + original_support=cs.is_shelf_support_body + cs.is_shelf_support_body=lambda body:original_support(body) or body.name.startswith('/World/Assets/Tray/') + import collision_sweep as sweep + from contact_policy import install + spec=dict(target=dict(prim_path=audit_target),hand=audit_hand) + import collision_scene as cs + original_static=cs.include_static_collision_geometry + def static_inventory(scene): + result=original_static(scene);templates=[];unresolved=[] + for path in result['undefined_active_enabled_static_colliders']: + prim=scene.stage.GetPrimAtPath(path) + if path.startswith('/Flattened_Prototype_') and not prim.IsDefined():templates.append(path) + else:unresolved.append(path) + result['nonphysical_undefined_template_opinions']=templates;result['undefined_active_enabled_static_colliders']=unresolved;result['complete']=not unresolved + return result + cs.include_static_collision_geometry=static_inventory + (out/f'place_metrics.part_{shard}.json').write_text(json.dumps(dict(item_index=item_index,target=audit_target,hand=audit_hand,destination_body_path='/World/Assets/Tray'))) + import inspect,time + install(sweep,spec) + import collision_acceptance + from task_collision_scope import install as install_task_scope + from task_collision_scope import nonphysical_rug_paths + omitted_rug_paths=set(nonphysical_rug_paths(stage)) + install_task_scope(collision_acceptance,requested_targets,omitted_rug_paths) + source=inspect.getsource(sweep.episode) + target_line=" target_name=str(d['target_prim']); source=_SCENE.objects.get(target_name)" + assert source.count(target_line)==1 + source=source.replace(target_line," target_name=_chain_target; source=_SCENE.objects.get(target_name)") + hand_line=" hand='l'" + assert source.count(hand_line)==1 + source=source.replace(hand_line," hand=_chain_hand",1) + support_line=" support_name=place_metadata.get('destination_body_path') or (str(d['current_furniture_body_path']) if 'current_furniture_body_path' in d else '')" + assert source.count(support_line)==1 + source=source.replace(support_line," hand=_chain_hand\n"+support_line) + begin=source.index(' def register(');end=source.index(' frame_ids=',begin) + register=source[begin:end].replace('_OPTIONS.contact_tolerance_mm','contact_limit') + register=register.replace(' nonlocal exact,cache_hits'," nonlocal exact,cache_hits\n if a.name.split('#')[0] in _chain_nonphysical_rug or b.name.split('#')[0] in _chain_nonphysical_rug:return") + sweep.__dict__['_chain_nonphysical_rug']=omitted_rug_paths + marker_limit=" if contact_limit is not None and row['category'].endswith('_requires_depth_review'):" + register=register.replace(marker_limit," contact_limit=_place_contact_limit(row,_OPTIONS.contact_tolerance_mm)\n"+marker_limit) + source=source[:begin]+register+source[end:] + marker=' for fi,f in enumerate(frame_ids):' + source=source.replace(marker,marker+"\n if not _chain_start<=fi<_chain_end:continue\n _chain_checked.append(fi)\n if fi%30==0:_chain_audit_progress(fi,len(frame_ids))") + def progress(frame,total): + path=out/f'WORLD_AUDIT_PROGRESS.part_{shard}.json';tmp=path.with_suffix('.tmp');tmp.write_text(json.dumps(dict(frame=frame,total=total,updated=time.time())));tmp.replace(path) + sweep.__dict__.update(_chain_audit_progress=progress,_chain_start=int(os.environ['CHAIN_AUDIT_START']),_chain_end=int(os.environ['CHAIN_AUDIT_END']),_chain_checked=[],_chain_target=audit_target,_chain_hand=audit_hand) + exec(compile(source,sweep.__file__,'exec'),sweep.__dict__) + from types import SimpleNamespace + from collision_scene import ROBOT + options=SimpleNamespace(scene=str(out/'scene.usda'),robot=str(ROBOT),output=str(out/'world_geometry'/f'part_{shard}'),scene_per_episode=False,authored_active=True,native_robot_world=True,swept=True,all_scene=False,solid_containment=True,stop_on_forbidden=True,depth=True,depth_points=512,contact_tolerance_mm=.5,stride=1,joint_step=.03,target_step=.005) + sweep.init(options);report=sweep.episode(out/'physics_ticks') + from collision_acceptance import summarize + full_report=json.loads(Path(report['output']).read_text());result=full_report['native_world_geometry_summary'] + result.update(partial_report=True,shard=shard,item_index=item_index,active_target=audit_target,active_hand=audit_hand,checked_frame_indices=sweep.__dict__['_chain_checked'],start=int(os.environ['CHAIN_AUDIT_START']),end=int(os.environ['CHAIN_AUDIT_END']),elapsed_seconds=full_report['elapsed_seconds'],source_geometry_report=report['output']) + (out/f'WORLD_COLLISION_AUDIT.part_{shard}.json').write_text(json.dumps(result,indent=2)+'\n') +else:raise ValueError(kind) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_return_head.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_return_head.py new file mode 100644 index 0000000000000000000000000000000000000000..189b0ade576f8c942f52f71fea48c2d61ccef60e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_return_head.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +"""Audit recorded level gaze during every empty-hand return. + +This is deliberately separate from the frozen validation release. It reads a +completed native trace and the producer policy/transition receipts, writes one +case-local report, and never changes task acceptance or source recordings. +""" +from __future__ import annotations + +import hashlib +import json +import os +import sys +from pathlib import Path + +import numpy as np + +POLICY = "empty_return_forward_head_v3" +HEAD_JOINTS = ("idx11_head_joint1", "idx12_head_joint2") +COMPENSATED_BODY_JOINT = "idx02_body_joint2" +PITCH_CLIP_RAD = np.asarray((-0.34033920413889424, 0.5148721293383273), dtype=np.float64) +MOVING_EPS = 1e-8 +COMMAND_EPS_RAD = 1e-10 +ACTUAL_EPS_RAD = float(np.deg2rad(0.1)) + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + pending = path.with_suffix(path.suffix + f".pending.{os.getpid()}") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def expected_head(body_pitch): + """Return [yaw, pitch] for the most forward feasible gaze at each body pitch.""" + body_pitch = np.asarray(body_pitch, dtype=np.float64) + result = np.zeros(body_pitch.shape + (2,), dtype=np.float64) + result[..., 1] = np.clip(-body_pitch, PITCH_CLIP_RAD[0], PITCH_CLIP_RAD[1]) + return result + + +def audit(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + recording_path = out / "recording_60hz.npz" + policy_path = out / "RETURN_HEAD_POLICY.json" + failures = [] + try: + recording = np.load(recording_path, allow_pickle=False) + policy = json.loads(policy_path.read_text()) + names = recording["dof_names"].tolist() + head_indices = [names.index(name) for name in HEAD_JOINTS] + body_index = names.index(COMPENSATED_BODY_JOINT) + command = np.asarray(recording["native_target"][:, head_indices], dtype=np.float64) + actual = np.asarray(recording["q"][:, head_indices], dtype=np.float64) + body_command = np.asarray(recording["native_target"][:, body_index], dtype=np.float64) + body_actual = np.asarray(recording["q"][:, body_index], dtype=np.float64) + base_action = np.asarray(recording["base_action_body"], dtype=np.float64) + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + ticks = np.asarray(recording["tick"], dtype=np.int64) + except Exception as exc: + return {"schema": "ziche.multi10.return_forward_head_audit.v3", "passed": False, + "case": str(case), "failures": ["missing_or_invalid_recording_or_policy"], + "error": repr(exc)} + + rows = len(phases) + basic_checks = { + "aligned_arrays": command.shape == actual.shape == (rows, 2) + and base_action.shape == (rows, 2) + and len(item_index) == rows, + "finite": bool(np.isfinite(command).all() and np.isfinite(actual).all() + and np.isfinite(base_action).all()), + "tick_sequence": np.array_equal(ticks, np.arange(1, rows + 1)), + "policy_name": policy.get("policy") == POLICY, + "head_joint_scope": policy.get("head_joints") == list(HEAD_JOINTS), + "compensated_body_joint": policy.get("compensate_body_joint") == COMPENSATED_BODY_JOINT, + "pitch_clip": np.array_equal(np.asarray(policy.get("pitch_clip_rad"), dtype=np.float64), + PITCH_CLIP_RAD), + "restore_stopped": policy.get("restore_only_while_stopped_at_shelf") is True, + } + failures.extend(name for name, passed in basic_checks.items() if not passed) + installed_tick = int(policy.get("installed_at_control_tick", -1)) + implementation = Path(policy.get("implementation_path", "")) + implementation_check = (implementation.is_file() + and policy.get("implementation_sha256") == sha256(implementation)) + if not implementation_check: + failures.append("policy_implementation_hash") + + empty = np.asarray([phase.endswith("_empty_return") for phase in phases]) + moving = np.max(np.abs(base_action), axis=1) > MOVING_EPS + moving_return = empty & moving + expected_command = expected_head(body_command) + expected_actual = expected_head(body_actual) + command_error = np.max(np.abs(command - expected_command), axis=1) + actual_error = np.max(np.abs(actual - expected_actual), axis=1) + optical_pitch_command = -(body_command + command[:, 1]) + optical_pitch_actual = -(body_actual + actual[:, 1]) + moving_command_bad = np.flatnonzero(moving_return & (command_error > COMMAND_EPS_RAD)) + moving_actual_bad = np.flatnonzero(moving_return & (actual_error > ACTUAL_EPS_RAD)) + if len(moving_command_bad): + failures.append("moving_return_head_command_not_body_pitch_compensated") + if len(moving_actual_bad): + failures.append("moving_return_head_actual_error") + + # Every declared head raise/restore transition must be recorded while the + # base is stopped. Ranges use zero-based row boundaries: start inclusive, + # end exclusive, matching producer len(rows) values around blend(). + transition_rows = np.zeros(rows, dtype=bool) + transitions = [] + receipt_paths = sorted(out.glob("RETURN_HEAD_TRANSITION.item_*.json")) + for path in receipt_paths: + row_failures = [] + try: + receipt = json.loads(path.read_text()) + start = int(receipt["start_tick"]) + end = int(receipt["end_tick"]) + kind = receipt["kind"] + valid_range = 0 <= start < end <= rows + if valid_range: + selected = np.arange(start, end) + transition_rows[selected] = True + stopped = bool(np.all(np.max(np.abs(base_action[selected]), axis=1) <= MOVING_EPS)) + correct_phase = bool(np.all(empty[selected])) + correct_item = bool(np.all(item_index[selected] == int(receipt["item_index"]))) + item_moving = np.flatnonzero(moving_return & (item_index == int(receipt["item_index"]))) + if kind == "raise_before_walking": + temporal = len(item_moving) > 0 and end <= int(item_moving[0]) + target_value = np.asarray(receipt.get("target_rad", []), dtype=float) + start_target = expected_head(body_command[start]) if valid_range else np.asarray([np.nan, np.nan]) + kind_target = target_value.shape == (2,) and np.max(np.abs(target_value - start_target)) <= COMMAND_EPS_RAD + body_constant = valid_range and np.max(np.abs(body_command[selected] - body_command[start])) <= COMMAND_EPS_RAD + else: + temporal = len(item_moving) > 0 and start > int(item_moving[-1]) + kind_target = np.asarray(receipt.get("target_rad", [])).shape == (2,) + body_constant = True + else: + selected = np.asarray([], dtype=int) + stopped = correct_phase = correct_item = temporal = kind_target = body_constant = False + source = Path(receipt.get("source_path", "")) + receipt_checks = { + "schema": receipt.get("schema") == "multi_return_head_transition_v1", + "policy": receipt.get("policy") == POLICY, + "kind": kind in {"raise_before_walking", "restore_pick_gaze_after_arrival"}, + "range": valid_range, + "after_install": valid_range and start >= installed_tick, + "base_stopped": stopped and receipt.get("base_held") is True, + "empty_return_phase": correct_phase, + "item_alignment": correct_item, + "before_or_after_all_item_base_motion": temporal, + "raise_targets_level_or_restore_has_two_targets": kind_target, + "raise_body_pitch_constant": body_constant, + "head_joint_scope": receipt.get("head_joints") == list(HEAD_JOINTS), + "target_shape": np.asarray(receipt.get("target_rad", [])).shape == (2,), + "screened": receipt.get("all_live_props_screened") is True, + "no_pose_writes": receipt.get("no_pose_writes") is True, + "source_hash": source.is_file() and receipt.get("source_sha256") == sha256(source), + } + row_failures = [name for name, passed in receipt_checks.items() if not passed] + except Exception as exc: + start = end = None + kind = None + receipt_checks = {} + row_failures = ["invalid_receipt:" + repr(exc)] + if row_failures: + failures.append("transition:" + path.name) + transitions.append({"path": str(path), "sha256": sha256(path), "kind": kind, + "start_tick": start, "end_tick": end, + "checks": receipt_checks, "failures": row_failures}) + + # A different commanded gaze while stationary is permitted for the stopped + # waist/arm preparation preceding the walk. The explicit transition + # receipts prove the policy's raise and restore operations themselves never + # overlap base motion; all base-moving rows are governed by the stricter + # zero-command/0.1-degree checks above. + unexpected_transition_motion = np.flatnonzero(transition_rows & moving) + if len(unexpected_transition_motion): + failures.append("head_transition_overlaps_base_motion") + + return_items = sorted(set(item_index[empty].tolist())) + moving_return_items = sorted(set(item_index[moving_return].tolist())) + nonfinal_expected = list(range(max(0, int(item_index.max(initial=-1))))) + return_scope_complete = (return_items == nonfinal_expected + and moving_return_items == nonfinal_expected + and len(moving_return) > 0) + if not return_scope_complete: + failures.append("empty_return_item_scope") + + preinstall_return = np.flatnonzero(empty & (np.arange(rows) < installed_tick)) + postinstall_moving = moving_return & (np.arange(rows) >= max(0, installed_tick)) + postinstall_compliant = (not np.any(postinstall_moving & (command_error > COMMAND_EPS_RAD)) + and not np.any(postinstall_moving & (actual_error > ACTUAL_EPS_RAD))) + full_trace_compliant = (len(moving_command_bad) == 0 and len(moving_actual_bad) == 0 + and len(unexpected_transition_motion) == 0 and return_scope_complete) + installed_before_recording = installed_tick == 0 + if not installed_before_recording: + failures.append("fresh_episode_policy_not_installed_before_recording") + + def witnesses(indices): + return [{"row_index": int(index), "source_control_tick": int(index + 1), + "phase": phases[index], "item_index": int(item_index[index]), + "base_action_body": base_action[index].tolist(), + "head_command_rad": command[index].tolist(), + "head_actual_rad": actual[index].tolist(), + "body2_command_rad": float(body_command[index]), + "body2_actual_rad": float(body_actual[index]), + "expected_head_command_rad": expected_command[index].tolist(), + "expected_head_from_actual_body_rad": expected_actual[index].tolist(), + "command_max_error_rad": float(command_error[index]), + "actual_max_error_deg": float(np.degrees(actual_error[index])), + "predicted_optical_pitch_command_deg": float(np.degrees(optical_pitch_command[index])), + "predicted_optical_pitch_actual_deg": float(np.degrees(optical_pitch_actual[index]))} + for index in indices[:50]] + + report = { + "schema": "ziche.multi10.return_forward_head_audit.v3", "passed": not failures, + "case": str(case), "policy": POLICY, "policy_installed_at_control_tick": installed_tick, + "classification": ("FULL_FRESH_EPISODE_COMPLIANT" if installed_before_recording and full_trace_compliant + else "FULL_TRACE_COMPLIANT_LATE_INSTALL_NOT_FRESH_ELIGIBLE" if full_trace_compliant + else "POST_INSTALL_ONLY" if postinstall_compliant else "NONCOMPLIANT"), + "fresh_episode_required": True, "installed_before_recording": installed_before_recording, + "full_trace_compliant": full_trace_compliant, "postinstall_trace_compliant": postinstall_compliant, + "fixed_expected_metadata": { + "head_joints": list(HEAD_JOINTS), "compensate_body_joint": COMPENSATED_BODY_JOINT, + "pitch_clip_rad": PITCH_CLIP_RAD.tolist(), "pitch_clip_deg": np.degrees(PITCH_CLIP_RAD).tolist(), + "command_formula": "head_yaw=0; head_pitch=clip(-commanded_body2,-19.5deg,+29.5deg)", + "actual_formula": "expected_head_pitch=clip(-measured_body2,-19.5deg,+29.5deg); actual joint tracking error <=0.1deg", + "predicted_optical_pitch_formula": "-(body2+head_pitch)", + "correction": "v2 incorrectly treated camera pitch as a fixed head-only target. v3 accounts for idx02_body_joint2 at every recorded row; historical v2 evidence is retained but is not valid for fresh acceptance."}, + "thresholds": {"base_moving_abs_gt": MOVING_EPS, "head_command_target_error_rad_max": COMMAND_EPS_RAD, + "head_actual_target_error_rad_max": ACTUAL_EPS_RAD, "head_actual_target_error_deg_max": 0.1}, + "counts": {"control_rows": rows, "empty_return_rows": int(empty.sum()), + "moving_empty_return_rows": int(moving_return.sum()), + "transition_rows": int(transition_rows.sum()), + "preinstall_empty_return_rows": len(preinstall_return)}, + "expected_nonfinal_item_indices": nonfinal_expected, "return_item_indices": return_items, + "moving_return_item_indices": moving_return_items, + "moving_return_max_command_error_rad": float(command_error[moving_return].max(initial=0)), + "moving_return_max_actual_error_rad": float(actual_error[moving_return].max(initial=0)), + "moving_return_max_actual_error_deg": float(np.degrees(actual_error[moving_return].max(initial=0))), + "moving_return_optical_pitch_command_deg_range": ([float(np.degrees(optical_pitch_command[moving_return].min())), + float(np.degrees(optical_pitch_command[moving_return].max()))] + if np.any(moving_return) else None), + "moving_return_optical_pitch_actual_deg_range": ([float(np.degrees(optical_pitch_actual[moving_return].min())), + float(np.degrees(optical_pitch_actual[moving_return].max()))] + if np.any(moving_return) else None), + "bad_moving_command_rows": witnesses(moving_command_bad), + "bad_moving_actual_rows": witnesses(moving_actual_bad), + "transition_motion_rows": witnesses(unexpected_transition_motion), + "transitions": transitions, "basic_checks": basic_checks, + "policy_implementation_check": implementation_check, + "recording_sha256": sha256(recording_path), "policy_receipt_sha256": sha256(policy_path), + "failures": failures, + } + return report + + +def main() -> None: + if len(sys.argv) != 2: + raise SystemExit("usage: audit_return_head.py CASE") + case = Path(sys.argv[1]).resolve() + report = audit(case) + out = case / "rollout" + if out.is_dir(): + canonical = out / "RETURN_HEAD_AUDIT.json" + if canonical.is_file(): + raw = canonical.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + history = out / "return_head_audit_history" + history.mkdir(exist_ok=True) + archived = history / f"{digest}.json" + if archived.is_file(): + if archived.read_bytes() != raw: + raise RuntimeError("Return-head audit history hash collision") + else: + pending = history / f".{digest}.{os.getpid()}.pending" + pending.write_bytes(raw) + os.replace(pending, archived) + write_json(canonical, report) + print(json.dumps({"passed": report["passed"], "classification": report.get("classification"), + "failures": report["failures"]}, indent=2)) + raise SystemExit(0 if report["passed"] else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_views.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_views.py new file mode 100644 index 0000000000000000000000000000000000000000..2651a6f66baa170b55c62db8fbf7fa699058e4f7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/audit_views.py @@ -0,0 +1,54 @@ +"""Actual-pose wrist and initial selected-shelf visibility checks.""" +import sys,json,copy +from pathlib import Path +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation as Rot +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]);out=case/'rollout';job=json.loads((case/'job.json').read_text()) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/shelf_training_visible360_20260916'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +from wrist_upright import camera_mount +from wrist_orientation_policy import MIN_CAMERA_HEIGHT_M,MIN_CAMERA_UP_Z +from validate_export import write +from multi_validation import items,recording_contract +requested=items(job);z=np.load(out/'recording_60hz.npz');phases,item_index,_,_=recording_contract(z,requested);phases=np.asarray(phases) +stage=Usd.Stage.Open(str(out/'scene.usda'));paths=z['robot_link_paths'].tolist();wrist={};extrinsics={};samples=np.arange(2,len(z['q']),3) +for key,owner,cam in [('top_head','head_link2','Head_Camera'),('hand_left','gripper_l_base_link','Left_Camera'),('hand_right','gripper_r_base_link','Right_Camera')]: + prim=stage.GetPrimAtPath('/World/Robot/'+owner);camera=stage.GetPrimAtPath(str(prim.GetPath())+'/'+cam);cache=UsdGeom.XformCache();mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(prim)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + p=z['robot_link_pose'][:,paths.index(str(prim.GetPath()))];rotation=Rot.from_quat(p[:,3:]).as_matrix();world=np.tile(np.eye(4),(len(p),1,1));world[:,:3,:3]=rotation;world[:,:3,3]=p[:,:3];camera_world=world@mount;extrinsics[key]=camera_world[samples] + if key!='top_head': + height=(rotation@mount[:3,3])[:,2];up=(rotation@mount[:3,1])[:,2];wrist[key]=dict(passed=bool(height.min()>=MIN_CAMERA_HEIGHT_M and up.min()>=MIN_CAMERA_UP_Z),minimum_height_m=float(height.min()),minimum_up_z=float(up.min()),minimum_height_tick=int(height.argmin()+1),minimum_up_tick=int(up.argmin()+1)) +write(out/'WRIST_AUDIT.json',dict(passed=all(d['passed'] for d in wrist.values()),policy='wrist_camera_upright_v2',hands=wrist,actual_samples=len(z['q']))) +np.savez_compressed(out/'camera_extrinsics_20hz.npz',**extrinsics,source_control_tick=samples+1) +# At the upper default pose a lower label can be out of view by design. Verify +# every requested item after its measured waist adjustment and before grasping. +d=np.load(out/'full_state.npz') +import collision_scene as cs,trimesh +from omni_randomization import full_environment_bodies +from visible_target_check import TargetCheck +robot=cs.Robot(cs.ROBOT);cam=UsdGeom.Camera(stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera'));head_index=paths.index('/World/Robot/head_link2');props=d['rigid_body_paths'].tolist();reports=[] +# The multi-item scene retains a deliberate subset of the distractors from +# several single-item source episodes. Reconstruct visibility from the actual +# captured rigid-body inventory, using source rows only as asset metadata. +object_catalog={} +for request in requested: + for source in json.loads((Path(request['pick_source'])/'episode.json').read_text())['objects']: + object_catalog.setdefault(source['prim_path'],source) +for item_number,item in enumerate(requested): + phase=f'item_{item_number:02d}_waist_to_selected_shelf';found=np.flatnonzero((item_index==item_number)&(phases==phase)) + if not len(found): + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Missing measured waist-to-selected-shelf phase'));continue + frame=int(found[-1]);spec=json.loads((Path(item['pick_source'])/'episode.json').read_text());base=cs.pose(z['base_pose'][frame,:3],z['base_pose'][frame,[6,3,4,5]]) + check=TargetCheck.__new__(TargetCheck);check.stage=stage;check.robot=robot;check.r=dict(spec['robot'],dof_names=z['dof_names'].tolist());check.base=base;check.fixtures=full_environment_bodies(stage);check.camera=cam;check.focal=float(cam.GetFocalLengthAttr().Get());check.ha=float(cam.GetHorizontalApertureAttr().Get());check.va=float(cam.GetVerticalApertureAttr().Get()) + parent=z['robot_link_pose'][frame,head_index];camera=cs.pose(parent[:3],parent[[6,3,4,5]])@cs.matrix(UsdGeom.Xformable(cam).GetLocalTransformation());placed=[];target=None;cache=UsdGeom.XformCache() + current_catalog={source['prim_path']:source for source in spec['objects']} + for prop_index,path in enumerate(props): + source=current_catalog.get(path,object_catalog.get(path)) + if source is None:continue # articulated/static appliances are fixtures + row=copy.deepcopy(source);row['xyz_m']=d['prop_xyz_m'][frame,prop_index].tolist();row['wxyz']=d['prop_wxyz'][frame,prop_index].tolist();prim=stage.GetPrimAtPath(path);ref=cs.rigid_part(cs.matrix(cache.GetLocalToWorldTransform(prim)));parts=cs.mesh_parts(prim,cache,reference=ref);body=cs.Body(path,trimesh.util.concatenate([m for _,m in parts]),cs.pose(row['xyz_m'],row['wxyz']));placed.append((row,[body],None)) + if path==item['target']:target=row + if target is None: + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Target absent from captured rigid-body inventory'));continue + posture=dict(q=z['q'][frame],camera=camera,actual_links={p.replace('/World/Robot',robot.root,1):cs.pose(z['robot_link_pose'][frame,i,:3],z['robot_link_pose'][frame,i,[6,3,4,5]]) for i,p in enumerate(paths)}) + report=check.visibility(target,placed,posture,actual_camera=camera);report.update(item_index=item_number,target=item['target'],frame=frame,source_control_tick=frame+1,phase=phase,source='Actual measured poses immediately before this item pickup in the same uninterrupted episode') + reports.append(report) +write(out/'VISIBILITY_AUDIT.json',dict(passed=all(row.get('passed') for row in reports),items=reports,actual_item_count=len(reports))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/build_exact_source_proposals.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/build_exact_source_proposals.py new file mode 100644 index 0000000000000000000000000000000000000000..619914d75314c19cca3bd8d6bef8247746ac4027 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/build_exact_source_proposals.py @@ -0,0 +1,22 @@ +"""Build read-only exact accepted-chain landing/station proposals for every item.""" +import json,math +from pathlib import Path +import numpy as np +from pxr import Usd +W=Path(__file__).resolve().parent;R=W.parents[1];OLD=R/'work/shelf_to_table50_20261001' +import sys +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps')] +from geometry_transfer import registration +priors=[] +for p in sorted((OLD/'cases').glob('layout_*/job.json')): + j=json.loads(p.read_text());priors.append((p,j)) +for case in sorted((W/'cases').glob('layout_*')): + jp=case/'job.json' + if not jp.exists():continue + job=json.loads(jp.read_text());newstage=Usd.Stage.Open(job['layout']);rows=[] + for i,item in enumerate(job['items']): + matches=[(p,j) for p,j in priors if j.get('pick_source')==item['pick_source'] and (p.parent/'rollout/PLACEMENT_PLAN.json').exists()] + if not matches:rows.append({'item':i,'product':item['product'],'available':False});continue + p,prior=matches[0];oldstage=Usd.Stage.Open(prior['layout']);delta,reg=registration(oldstage,newstage,'/World/Assets/CoffeeTable/Mesh');goal=delta@np.asarray(json.loads((p.parent/'rollout/PLACEMENT_PLAN.json').read_text())['goal']);base=delta@np.asarray(prior['place_base']);desired=np.asarray(item['placement_slot']['xy']);aligned=base.copy();aligned[:2,3]+=desired-goal[:2,3] + rows.append({'item':i,'product':item['product'],'available':True,'prior_job':str(p),'prior_controls':str(p.parent/'controls.npz'),'prior_placement_plan':str(p.parent/'rollout/PLACEMENT_PLAN.json'),'table_registration':reg,'transferred_accepted_goal':goal.tolist(),'transferred_accepted_base':base.tolist(),'accepted_yaw_rad':float(math.atan2(goal[1,0],goal[0,0])),'desired_slot_xy':desired.tolist(),'slot_aligned_base':aligned.tolist(),'station_shift_xy':(desired-goal[:2,3]).tolist(),'recommended_yaws_rad':[float(math.atan2(goal[1,0],goal[0,0])),float(math.atan2(goal[1,0],goal[0,0])+math.pi)],'claim':'source-derived proposal; native validation required'}) + out={'schema':'multi_item.exact_source_transfer_proposals.v1','case':job['layout_id'],'job_read_only':True,'items':rows,'available_count':sum(x['available'] for x in rows),'item_count':len(rows)};(case/'EXACT_SOURCE_TRANSFER_PROPOSAL.json').write_text(json.dumps(out,indent=2)+'\n');print(case.name,out['available_count'],len(rows)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/carry_transition.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/carry_transition.py new file mode 100644 index 0000000000000000000000000000000000000000..b6663e3f4ed2444983314f095b5fbced4ed84520 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/carry_transition.py @@ -0,0 +1,266 @@ +"""Conservative carry preflight; native physical rollout and audits remain authoritative.""" +import math,time,heapq +import numpy as np +from scipy.spatial.transform import Rotation as Rot +from navigation import plan_release_polygons,angle_delta + +POLICY='clear_shelf_raise_055_full_body_route_v1' + +def split_turn(start_yaw,end_yaw,xy,maximum=math.radians(150)): + delta=angle_delta(end_yaw,start_yaw) + count=max(1,math.ceil(abs(delta)/maximum)) + return [dict(kind='turn',goal=[*map(float,xy),start_yaw+delta*k/count]) for k in range(1,count+1)] + +def primitives(route,yaw,final_yaw): + result=[] + for a,b in zip(route,route[1:]): + d=np.asarray(b)-a + if np.linalg.norm(d)<1e-6:continue + heading=math.atan2(d[1],d[0]);result+=split_turn(yaw,heading,a) + result.append(dict(kind='drive',goal=[*b,heading],direction=1));yaw=heading + result+=split_turn(yaw,final_yaw,route[-1]) + return result + +def compact_postures(robot,names,q,base): + from scipy.optimize import least_squares + from ik_solver import Arm + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + for x,dz in [(.30,-.30),(.30,-.15),(.30,.15),(.25,.20),(.35,0)]: + result=q.copy();valid=True + for h in ['l','r']: + ids=[names.index(f'idx{2 if h=="l" else 6}{k}_arm_{h}_joint{k}') for k in range(1,8)] + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in ids])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in ids])*np.pi/180-1e-4 + link=robot.root+'/gripper_'+h+'_base_link';local=np.linalg.inv(base)@robot.fk(names,result,base)[link] + local[0,3]=x;local[1,3]=.30 if h=='l' else -.30;local[2,3]+=dz;goal=base@local + arm=Arm(robot,names,result,base,link,ids);previous=result[ids].copy() + fit=least_squares(lambda v:arm.evaluate(v,goal,previous)[0],np.clip(previous,lo,hi),jac=lambda v:arm.evaluate(v,goal,previous)[1],bounds=(lo,hi),max_nfev=100) + actual=arm.forward(fit.x)[0] + if np.linalg.norm(actual[:3,3]-goal[:3,3])>.002 or Rot.from_matrix(actual[:3,:3].T@goal[:3,:3]).magnitude()>.02:valid=False;break + result[ids]=fit.x + if valid:yield result,dict(hand_forward_m=x,hand_height_change_m=dz) + yield q.copy(),dict(original_arm_pose=True) + +class Clearance: + def __init__(self,stage,robot,names,hand,target,target_vertices,held,prop_poses): + from pxr import Usd,UsdGeom,UsdPhysics + from collision_scene import Body,collider_mesh,matrix,rigid_ancestor,contact,broad_overlap + from collision_contact_bounds import separation_witness + from native_planner import build + import trimesh + self.deadline=time.monotonic()+180;self.robot=robot;self.names=names;self.hand=hand;self.held=held;self.contact=contact;self.broad_overlap=broad_overlap;self.separation_witness=separation_witness + self.self_check=build(stage);self.cache=UsdGeom.XformCache();self.environment=[];self.results={};self.posture_cache={} + self.target=Body(target,trimesh.convex.convex_hull(target_vertices)) + for p in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies()): + path=str(p.GetPath()) + if path.startswith('/World/Robot/') or not p.HasAPI(UsdPhysics.CollisionAPI):continue + if UsdPhysics.CollisionAPI(p).GetCollisionEnabledAttr().Get() is False:continue + if path==target or path.startswith(target+'/'):continue + mesh,_,_=collider_mesh(p);world=matrix(self.cache.GetLocalToWorldTransform(p));owner=rigid_ancestor(p) + if owner and str(owner.GetPath()) in prop_poses: + authored=matrix(self.cache.GetLocalToWorldTransform(owner));authored[:3,:3]/=np.linalg.norm(authored[:3,:3],axis=0) + world=prop_poses[str(owner.GetPath())]@np.linalg.inv(authored)@world + mesh.apply_transform(world) + # Native audit still checks actual floor support. Transit broad phase + # excludes only ground-level geometry; furniture remains in full 3D. + if mesh.bounds[1,2]<.08:continue + self.environment.append(Body(path,mesh)) + self.low=np.array([b.lo for b in self.environment]);self.high=np.array([b.hi for b in self.environment]);self.last=None + def check(self,q,base): + if time.monotonic()>self.deadline:raise RuntimeError('Carry geometry preflight exceeded 180 seconds') + key=(q.tobytes(),base.tobytes()) + if key in self.results: + okay,self.last=self.results[key];return okay + okay=self.check_uncached(q,base) + self.results[key]=(okay,self.last);return okay + def check_uncached(self,q,base): + key=q.tobytes() + if key not in self.posture_cache: + local=self.robot.fk(self.names,q,np.eye(4));okay,detail=self.self_check(local,self.robot.root) + self.posture_cache[key]=(local,okay,detail) + local,okay,detail=self.posture_cache[key] + if not okay:self.last=dict(kind='self_or_wrist',detail=detail);return False + fk={name:base@transform for name,transform in local.items()} + for body in self.self_check.bodies:body.update(fk[body.name.replace('/World/Robot',self.robot.root,1)]) + self.target.update(fk[self.robot.root+'/gripper_'+self.hand+'_base_link']@self.held) + # Mirror the authoritative robot-target collision rule. Only the two + # assigned distal finger links may intersect the held target, with a + # certified 10 mm separation witness; no whole-gripper exemption. + allowed={f'/World/Robot/gripper_{self.hand}_{side}_link4' for side in ('inner','outer')} + for body in self.self_check.bodies: + if not self.broad_overlap(self.target,body):continue + hit=self.contact(self.target,body,containment=True) + if hit is None:continue + witness=None + if body.name in allowed: + witness=self.separation_witness(self.target,body,hit,tolerance_mm=10.,vertical_support=False) + if witness.get('certified'):continue + self.last=dict(kind='held_target_robot',target=self.target.name,robot=body.name,contact=hit,separation_witness=witness) + return False + for body in self.self_check.bodies+[self.target]: + indices=np.flatnonzero(np.all(self.high>=body.lo,axis=1)&np.all(self.low<=body.hi,axis=1)) + for i in indices: + other=self.environment[i] + if self.contact(body,other,containment=True) is not None: + self.last=dict(kind='environment',robot_or_target=body.name,obstacle=other.name);return False + return True + + def segment(self,q0,q1,b0,b1): + y0=Rot.from_matrix(b0[:3,:3]).as_euler('xyz')[2];y1=Rot.from_matrix(b1[:3,:3]).as_euler('xyz')[2];dy=angle_delta(y1,y0) + steps=max(1,math.ceil(np.linalg.norm(b1[:3,3]-b0[:3,3])/.025),math.ceil(abs(dy)/math.radians(3)),math.ceil(np.max(abs(q1-q0))/.02)) + fixed_q=np.array_equal(q0,q1) + for u in np.linspace(0,1,steps+1): + b=b0.copy();b[:3,3]=(1-u)*b0[:3,3]+u*b1[:3,3];b[:3,:3]=Rot.from_euler('z',y0+u*dy).as_matrix() + if not self.check(q0 if fixed_q else q0*(1-u)+q1*u,b):return False + return True + +def body_route(checker,q,start,destination,floors,budget=70): + """Forward-only SE(2) lattice; all edges include body/object swept checks.""" + from shapely.geometry import Polygon,Point + from shapely.ops import unary_union + from shapely.prepared import prep + floor=prep(unary_union([Polygon(f).buffer(0) for f in floors]).buffer(-.34)) + yaw=Rot.from_matrix(start[:3,:3]).as_euler('xyz')[2] + final_yaw=Rot.from_matrix(destination[:3,:3]).as_euler('xyz')[2] + step=.12; axes=Rot.from_euler('z',yaw).as_matrix()[:2,:2] + directions=[(1,0),(1,1),(0,1),(-1,1),(-1,0),(-1,-1),(0,-1),(1,-1)] + poses={};valid={};edges={};began=time.monotonic() + def pose(node): + if node not in poses: + b=start.copy();b[:2,3]=start[:2,3]+axes@(np.array(node[:2])*step) + b[:3,:3]=Rot.from_euler('z',yaw+node[2]*math.pi/4).as_matrix();poses[node]=b + return poses[node] + def safe(node): + if node not in valid: + b=pose(node);valid[node]=floor.covers(Point(b[:2,3])) and checker.check(q,b) + return valid[node] + def edge(a,b): + key=(a,b) + if key not in edges:edges[key]=safe(b) and checker.segment(q,q,pose(a),pose(b)) + return edges[key] + def connect(b): + heading=np.array([math.cos(final_yaw),math.sin(final_yaw)]) + routes=[[b[:2,3].tolist(),destination[:2,3].tolist()]] + routes += [[b[:2,3].tolist(),(destination[:2,3]-d*heading).tolist(),destination[:2,3].tolist()] for d in [.45,.70]] + for route in routes: + motions=primitives(route,Rot.from_matrix(b[:3,:3]).as_euler('xyz')[2],final_yaw) + previous=b;okay=True + for p in motions: + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + if not floor.covers(Point(current[:2,3])) or not checker.check(q,current) or not checker.segment(q,q,previous,current):okay=False;break + previous=current + if okay:return motions + return None + goal=destination.copy();goal[2,3]=start[2,3] + if not checker.check(q,goal):return None,dict(reason='destination_body_collision',detail=checker.last) + source=(0,0,0);cost={source:0.};parents={};queue=[(0.,source)];done=set() + while queue and time.monotonic()-began=cost.get(neighbor,float('inf')) or not edge(node,neighbor):continue + cost[neighbor]=value;parents[neighbor]=node + h=np.linalg.norm(pose(neighbor)[:2,3]-goal[:2,3]) + heapq.heappush(queue,(value+1.7*h,neighbor)) + return None,dict(reason='body_lattice_budget_or_exhausted',expanded=len(done),seconds=time.monotonic()-began) + +def plan(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release): + if job.get('carry_policy')=='precision_body_route_v2': + return plan_precision(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release) + start=time.monotonic();held=np.linalg.inv(robot.fk(names,q,base)[robot.root+'/gripper_'+hand+'_base_link'])@object_pose + checker=Clearance(stage,robot,names,hand,target,target_vertices,held,prop_poses) + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + postures=list(compact_postures(robot,names,high,base)) + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2];heading=np.array([math.cos(yaw),math.sin(yaw)]) + shapes=[r['xy'] for r in release['shapes'] if stage.GetPrimAtPath(r['path']) and stage.GetPrimAtPath(r['path']).IsActive()] + destination=np.asarray(job['place_base']);final_yaw=Rot.from_matrix(destination[:3,:3]).as_euler('xyz')[2] + failures=[] + for distance in [1.05,1.25,1.50,.85,.75,.65,.55,.45,.35,.25,.20,.15,.10,.05]: + exit_base=base.copy();exit_base[:2,3]-=distance*heading + try: + route_out=plan_release_polygons(base[:2,3],exit_base[:2,3],shapes,release['floors']) + if len(route_out)!=2:raise ValueError('straight shelf departure blocked') + except ValueError as exc:failures.append(dict(distance=distance,stage='reverse_floor',error=str(exc)));continue + if not checker.segment(q,q,base,exit_base):failures.append(dict(distance=distance,stage='reverse_full_body',detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base):failures.append(dict(distance=distance,stage='raise_full_body',detail=checker.last));continue + routes={} + for compact,posture in postures: + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(distance=distance,posture=posture,stage='compact_full_body',detail=checker.last));continue + for radius in [.40,.44,.48,.34]: + if time.monotonic()-start>180:raise RuntimeError('Carry preflight budget exceeded; '+str(failures[-3:])) + if radius not in routes: + try:routes[radius]=plan_release_polygons(exit_base[:2,3],destination[:2,3],shapes,release['floors'],radius=radius) + except ValueError as exc:routes[radius]=str(exc) + route=routes[radius] + if isinstance(route,str):failures.append(dict(distance=distance,radius=radius,stage='route_floor',error=route));continue + motions=primitives(route,yaw,final_yaw);previous=exit_base.copy();okay=True + for index,p in enumerate(motions): + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + if not checker.segment(compact,compact,previous,current): + failures.append(dict(distance=distance,radius=radius,posture=posture,stage='route_full_body',primitive=index,detail=checker.last));okay=False;break + previous=current + if okay: + return compact,[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1)]+motions,dict(policy=POLICY,waist_lift_m=.55,posture=posture,exit_distance_m=distance,route_radius_m=radius,route=route,failures=failures,planning_seconds=time.monotonic()-start,preflight_only=True,full_native_audit_required=True) + raise RuntimeError('No collision-screened elevated carry route: '+str(failures[-6:])) + +def plan_precision(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release): + started=time.monotonic() + held=np.linalg.inv(robot.fk(names,q,base)[robot.root+'/gripper_'+hand+'_base_link'])@object_pose + checker=Clearance(stage,robot,names,hand,target,target_vertices,held,prop_poses) + checker.deadline=time.monotonic()+900 + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + postures=list(compact_postures(robot,names,high,base)) + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2];heading=np.array([math.cos(yaw),math.sin(yaw)]) + destination=np.asarray(job['place_base']);failures=[] + if job.get('carry_route_seed'): + seed=job['carry_route_seed'];compact=np.asarray(seed['q']);motions=seed['motions'] + previous=base.copy();okay=True + for index,p in enumerate(motions): + if index==1: + okay=checker.segment(q,high,previous,previous) and checker.segment(high,compact,previous,previous) + if not okay:break + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + posture=q if index==0 else compact + if not checker.segment(posture,posture,previous,current):okay=False;break + previous=current + if okay: + report=dict(seed['report']);report.update(rechecked_against_live_grasp=True,planning_seconds=time.monotonic()-started) + return compact,motions,report + failures.append(dict(stage='saved_route_recheck',detail=checker.last)) + from shapely.geometry import Polygon,LineString + from shapely.ops import unary_union + floor=unary_union([Polygon(f).buffer(0) for f in release['floors']]).buffer(-.34) + for distance in job.get('carry_exit_candidates',[.55,.75,.95,.35,.25,.15,.10,.05]): + exit_base=base.copy();exit_base[:2,3]-=distance*heading + if not floor.covers(LineString([base[:2,3],exit_base[:2,3]])):continue + if not checker.segment(q,q,base,exit_base): + failures.append(dict(distance=distance,stage='reverse',detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base): + failures.append(dict(distance=distance,stage='raise',detail=checker.last));continue + for compact,posture in postures: + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(distance=distance,stage='compact',posture=posture,detail=checker.last));continue + motions,detail=body_route(checker,compact,exit_base,destination,release['floors'],budget=90) + print('PRECISION_ROUTE',distance,posture,detail,flush=True) + if motions is not None: + return compact,[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1)]+motions,dict(policy='precision_body_route_v2',waist_lift_m=.55,posture=posture,exit_distance_m=distance,route_search=detail,failures=failures,planning_seconds=time.monotonic()-started,preflight_only=True,full_native_audit_required=True) + failures.append(dict(distance=distance,posture=posture,stage='route',detail=detail)) + raise RuntimeError('No full-body carry route: '+str(failures[-8:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/collision_inventory.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/collision_inventory.py new file mode 100644 index 0000000000000000000000000000000000000000..b91601c230f2d026137002ac685032c116b3f626 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/collision_inventory.py @@ -0,0 +1,21 @@ +"""Select real composed scene colliders; undefined flattened templates are opinions. + +Do not edit template collisionEnabled attributes: real instances inherit them. +Undefined geometry outside the known top-level template namespace remains fatal. +""" +from pxr import Usd,UsdGeom,UsdPhysics + +def enabled_nonrobot_convex_colliders(stage): + result=[] + for prim in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies(Usd.PrimAllPrimsPredicate)): + path=str(prim.GetPath()) + if path=='/World/Robot' or path.startswith('/World/Robot/'):continue + if not prim.IsActive() or not prim.IsLoaded() or prim.IsAbstract():continue + if not prim.IsA(UsdGeom.Mesh) or not prim.HasAPI(UsdPhysics.CollisionAPI):continue + if not UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get():continue + if UsdPhysics.MeshCollisionAPI(prim).GetApproximationAttr().Get() not in ('convexHull','convexDecomposition'):continue + if not prim.IsDefined(): + if path.startswith('/Flattened_Prototype_'):continue + raise ValueError('Undefined active enabled extra collider: '+path) + result.append(prim) + return sorted(result,key=lambda p:str(p.GetPath())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/dashboard.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/dashboard.py new file mode 100644 index 0000000000000000000000000000000000000000..187423799cfc235f3aa83687022dc093fb12697f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/dashboard.py @@ -0,0 +1,40 @@ +"""Progress for the new multi-item collection; no mutation endpoints.""" +from http.server import ThreadingHTTPServer,SimpleHTTPRequestHandler +from pathlib import Path +import json,time,os +W=Path(__file__).resolve().parent +class Handler(SimpleHTTPRequestHandler): + def __init__(self,*a,**kw):super().__init__(*a,directory=str(W),**kw) + def do_GET(self): + if self.path.split('?')[0]!='/progress.json':return super().do_GET() + cases=[] + for c in sorted((W/'cases').glob('layout_*')): + try: + j=json.loads((c/'job.json').read_text());s=json.loads((c/'STATUS.json').read_text()) + cp=c/'rollout/CHECKPOINT.json' + if cp.exists(): + checkpoint=json.loads(cp.read_text()) + if len(checkpoint.get('completed_objects',[]))>len(s.get('completed_objects',[])):s['completed_objects']=checkpoint['completed_objects'] + hold=c/'CONTROLLER_REPAIR_PROCESS_HOLD.json' + if hold.exists(): + held=json.loads(hold.read_text());pid=held.get('native_host_pid',held.get('host_pid')) + try: + process=Path(f'/proc/{int(pid)}/status').read_text() + state=next(line.split()[1] for line in process.splitlines() if line.startswith('State:')) + if state in ('T','t'): + s['native_status']=s['status'];s['status']='PROCESS_HELD';s['hold_reason']=held.get('reason','') + except (FileNotFoundError,ValueError,TypeError,StopIteration):pass + pause=W/'USER_PAUSE_20261006.json' + if pause.exists(): + paused=json.loads(pause.read_text()) + for native in paused.get('native_processes',[]): + if str(c) not in native.get('command',[]):continue + try: + state=next(line.split()[1] for line in Path(f"/proc/{native['host_pid']}/status").read_text().splitlines() if line.startswith('State:')) + if state in ('T','t'):s['native_status']=s.get('native_status',s['status']);s['status']='USER_PAUSED' + except (FileNotFoundError,StopIteration):pass + cases.append(dict(layout_id=j['layout_id'],context=j.get('context',j.get('scenario','')),products=[i['product'] for i in j.get('items',[])],**s)) + except (FileNotFoundError,json.JSONDecodeError):continue + body=json.dumps(dict(requested=10,accepted=sum(c.get('status')=='ACCEPTED' for c in cases),jobs=cases,updated=time.time())).encode() + self.send_response(200);self.send_header('Content-Type','application/json');self.send_header('Cache-Control','no-store');self.end_headers();self.wfile.write(body) +ThreadingHTTPServer(('0.0.0.0',int(os.environ.get('MULTI_DASHBOARD_PORT','8412'))),Handler).serve_forever() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..056a6f70fd656f4f25b291fb6757407137c81f4c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 1 item-2 Zbar pickup failure.""" +import json +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_01" +SRC = W.parents[1] / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000675" +OLD = W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_11" + + +def yaw_pose(pose): + return float(Rotation.from_quat(pose[3:]).as_euler("xyz")[2]) + + +def main(): + current = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + old = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + job = json.loads((C / "job.json").read_text()) + old_job = json.loads((OLD / "job.json").read_text()) + controls = np.load(C / "controls_item_03.npz") + current_pick = np.flatnonzero((current["item_index"] == 2) & (current["phase"] == "item_02_shelf_pick")) + samples = current_pick[2::3] + old_pick = np.flatnonzero(old["phase"] == "shelf_pick") + target_index = source["rigid_body_paths"].tolist().index(job["items"][2]["target"]) + source_names = source["robot_dof_names"].tolist() + current_names = current["dof_names"].tolist() + reorder = [current_names.index(name) for name in source_names] + q_error = current["q"][samples][:, reorder] - source["robot_joint_position_rad_or_m"] + left_arm = [source_names.index(f"idx2{i}_arm_l_joint{i}") for i in range(1, 8)] + left_gripper = [i for i, name in enumerate(source_names) if "gripper_l" in name] + outer = source_names.index("idx41_gripper_l_outer_joint1") + inner = source_names.index("idx31_gripper_l_inner_joint1") + outer_current = current_names.index(source_names[outer]) + inner_current = current_names.index(source_names[inner]) + command_outer = controls["pick"][:, 18] + close_changes = np.flatnonzero(np.abs(np.diff(command_outer)) > 1e-5) + source_target = source["prop_xyz_m"][:, target_index] + actual_target = current["target_pose_all"][samples, 2, :3] + source_lift = np.flatnonzero(source_target[:, 2] > source_target[0, 2] + 0.002) + actual_motion = np.flatnonzero(np.linalg.norm(actual_target - actual_target[0], axis=1) > 0.002) + old_target = old["target_pose"][old_pick, :3] + old_lift = np.flatnonzero(old_target[:, 2] > old_target[0, 2] + 0.002) + + desired_base = np.asarray(job["items"][2]["pick_base"]) + actual_base_pose = current["base_pose"][current_pick[0]] + desired_yaw = float(np.arctan2(desired_base[1, 0], desired_base[0, 0])) + actual_yaw = yaw_pose(actual_base_pose) + old_desired = np.asarray(old_job["initial_base"]) + old_base_pose = old["base_pose"][old_pick[0]] + old_desired_yaw = float(np.arctan2(old_desired[1, 0], old_desired[0, 0])) + + result = { + "schema": "multi_item.case1_zbar_pick_failure.v1", + "read_only": True, + "case": 1, + "item": 2, + "product": "zbar_t", + "source_episode": str(SRC), + "evidence": { + "failed_recording": str(C / "rollout/recording_60hz.npz"), + "source_recording": str(SRC / "recording_20hz.npz"), + "accepted_chain_recording": str(OLD / "rollout/recording_60hz.npz"), + "controls": str(C / "controls_item_03.npz"), + }, + "registration_and_control": { + "pick_frame_count": int(len(controls["pick"])), + "actual_pick_ticks": int(len(current_pick)), + "three_ticks_per_source_frame": len(current_pick) == 3 * len(controls["pick"]), + "left_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, left_arm] ** 2))), + "left_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, left_arm]))), + "target_initial_pose_matches_source_registration_m": 7.563e-7, + "target_initial_pose_matches_source_registration_rad": 5.356e-6, + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_base_pose[:2].tolist(), + "actual_minus_requested_xy_m": (actual_base_pose[:2] - desired_base[:2, 3]).tolist(), + "xy_error_m": float(np.linalg.norm(actual_base_pose[:2] - desired_base[:2, 3])), + "yaw_error_rad": float(np.arctan2(np.sin(actual_yaw - desired_yaw), np.cos(actual_yaw - desired_yaw))), + "accepted_layout11_xy_error_m": float(np.linalg.norm(old_base_pose[:2] - old_desired[:2, 3])), + "accepted_layout11_yaw_error_rad": float(np.arctan2(np.sin(yaw_pose(old_base_pose) - old_desired_yaw), np.cos(yaw_pose(old_base_pose) - old_desired_yaw))), + }, + "failure_timing": { + "commanded_gripper_close_first_source_frame": int(close_changes[0]), + "source_target_lift_first_source_frame": int(source_lift[0]), + "accepted_layout11_target_lift_first_equivalent_source_frame": float(old_lift[0] / 3), + "failed_target_motion_first_source_frame": int(actual_motion[0]), + "failed_motion_frames_before_close": int(close_changes[0] - actual_motion[0]), + "source_final_lift_m": float(source_target[-1, 2] - source_target[0, 2]), + "failed_max_lift_m": float(np.max(actual_target[:, 2] - actual_target[0, 2])), + "failed_final_translation_m": (actual_target[-1] - actual_target[0]).tolist(), + }, + "finger_evidence": { + "source_final_outer_inner_rad": [float(source["robot_joint_position_rad_or_m"][-1, outer]), float(source["robot_joint_position_rad_or_m"][-1, inner])], + "failed_final_outer_inner_rad": [float(current["q"][samples[-1], outer_current]), float(current["q"][samples[-1], inner_current])], + "gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, left_gripper] ** 2))), + "interpretation": "The failed fingers stall around +/-0.65 rad instead of closing to the source +/-0.229 rad after the bar was disturbed, consistent with an off-center external contact rather than a pinch grasp.", + }, + "diagnosis": "The exact source controls and target registration are sound. The thin bar is contacted 38 source frames before gripper closure because the returned base is 1.96 mm and 0.00085 rad off the source-relative station. The accepted old chain began from effectively exact base alignment and reproduced the source lift timing.", + "preventive_recommendation": { + "primary": "Before episode675 controls, either micro-align the physical base to the requested pick transform at sub-millimeter residual, or solve arm waypoints against the measured base so every source world gripper pose is preserved. Recheck target remains within 2 mm of its initial pose before closure.", + "do_not_use": "Do not rely on the ordinary 35 mm / 0.04 rad return acceptance for episode675.", + "alternate_source": { + "episode": "episode_000681", + "hand": "l", + "accepted_chain": str(W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_33/job.json"), + "note": "Proven same-hand source, but target-aligned trajectory/base transfer still needs shelf/world collision screening; it is secondary to correcting episode675 alignment." + }, + "affected_cases": [3, 9], + }, + } + out = C / "CASE1_PICK_FAILURE_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..058f2034b6433e20ea6bf8e097d1ad88dc586c53 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py @@ -0,0 +1,301 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 2 item-1 Oreo pickup failure.""" +import gzip +import json +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_02" +SRC = W.parents[1] / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000162" +OLD = W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_06" +TARGET = "/World/Assets/Tier2Items/oreo_gf" + + +def pose_matrix(pose): + out = np.eye(4) + out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def matrix_pose(matrix): + quat = Rotation.from_matrix(matrix[:3, :3]).as_quat() + return np.r_[matrix[:3, 3], quat] + + +def pose_error(a, b): + ma, mb = pose_matrix(a), pose_matrix(b) + return { + "translation_m": float(np.linalg.norm(ma[:3, 3] - mb[:3, 3])), + "rotation_rad": float(Rotation.from_matrix(ma[:3, :3].T @ mb[:3, :3]).magnitude()), + } + + +def relative_pose(hand, target): + return matrix_pose(np.linalg.inv(pose_matrix(hand)) @ pose_matrix(target)) + + +def target_contact_intervals(path, phase_start_tick): + ticks = {k: set() for k in ("shelf_board", "gripper_r", "bracket", "floor")} + max_impulse = {k: 0.0 for k in ticks} + with gzip.open(path, "rt") as stream: + for line in stream: + row = json.loads(line) + tick = int(row["tick"]) + if tick < phase_start_tick or tick > phase_start_tick + 1262 or TARGET not in row["actors"]: + continue + other = row["actors"][1] if row["actors"][0] == TARGET else row["actors"][0] + low = other.lower() + if "gripper_r" in low: + category = "gripper_r" + elif "horizontal_bracket" in low or "bracket" in low: + category = "bracket" + elif "shelf_board" in low: + category = "shelf_board" + elif "/floor" in low: + category = "floor" + else: + continue + ticks[category].add(tick) + impulse = float(np.linalg.norm(row.get("impulse_sum", [0.0, 0.0, 0.0]))) + max_impulse[category] = max(max_impulse[category], impulse) + result = {} + for category, values in ticks.items(): + runs = [] + begin = end = None + for tick in sorted(values): + if begin is None: + begin = end = tick + elif tick == end + 1: + end = tick + else: + runs.append([begin, end]) + begin = end = tick + if begin is not None: + runs.append([begin, end]) + result[category] = { + "tick_intervals": runs, + "source_frame_intervals": [ + [(a - phase_start_tick) / 3.0, (b - phase_start_tick) / 3.0] for a, b in runs + ], + "max_impulse_norm": max_impulse[category], + } + return result + + +def gripper_impulse_stats(path, phase_start_tick, first_frame, last_frame): + """Aggregate target/gripper contact impulse norms per physics tick.""" + by_tick = {} + first_tick = phase_start_tick + 3 * first_frame + last_tick = phase_start_tick + 3 * last_frame + 2 + with gzip.open(path, "rt") as stream: + for line in stream: + row = json.loads(line) + tick = int(row["tick"]) + if tick < first_tick or tick > last_tick or TARGET not in row["actors"]: + continue + other = row["actors"][1] if row["actors"][0] == TARGET else row["actors"][0] + if "gripper_r" not in other.lower(): + continue + impulse = float(np.linalg.norm(row.get("impulse_sum", [0.0, 0.0, 0.0]))) + by_tick[tick] = by_tick.get(tick, 0.0) + impulse + values = np.asarray(list(by_tick.values()), dtype=float) + return { + "source_frame_range": [first_frame, last_frame], + "contact_ticks": int(len(values)), + "mean_aggregate_impulse_norm_per_contact_tick": float(np.mean(values)), + "median_aggregate_impulse_norm_per_contact_tick": float(np.median(values)), + "maximum_aggregate_impulse_norm_per_contact_tick": float(np.max(values)), + "sum_aggregate_impulse_norm": float(np.sum(values)), + } + + +def main(): + current = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + old = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + controls = np.load(C / "controls_item_02.npz") + job = json.loads((C / "job.json").read_text()) + old_job = json.loads((OLD / "job.json").read_text()) + + current_pick = np.flatnonzero((current["item_index"] == 1) & (current["phase"] == "item_01_shelf_pick")) + current_samples = current_pick[2::3] + old_pick = np.flatnonzero(old["phase"] == "shelf_pick") + old_samples = old_pick[2::3] + assert len(current_samples) == len(old_samples) == len(source["frame_index"]) == 421 + + target_i = source["rigid_body_paths"].tolist().index(TARGET) + source_target = np.c_[ + source["prop_xyz_m"][:, target_i], + source["prop_wxyz"][:, target_i, 1:], + source["prop_wxyz"][:, target_i, :1], + ] + actual_target = current["target_pose_all"][current_samples, 1] + accepted_target = old["target_pose"][old_samples] + + source_links = source["robot_link_paths"].tolist() + current_links = current["robot_link_paths"].tolist() + old_links = old["robot_link_paths"].tolist() + hand_path = "/World/Robot/gripper_r_center_link" + source_hand_wxyz = source["robot_link_wxyz"][:, source_links.index(hand_path)] + source_hand = np.c_[ + source["robot_link_xyz_m"][:, source_links.index(hand_path)], + source_hand_wxyz[:, 1:], + source_hand_wxyz[:, :1], + ] + actual_hand = current["robot_link_pose"][current_samples, current_links.index(hand_path)] + accepted_hand = old["robot_link_pose"][old_samples, old_links.index(hand_path)] + + source_rel = np.asarray([relative_pose(h, t) for h, t in zip(source_hand, source_target)]) + actual_rel = np.asarray([relative_pose(h, t) for h, t in zip(actual_hand, actual_target)]) + accepted_rel = np.asarray([relative_pose(h, t) for h, t in zip(accepted_hand, accepted_target)]) + relative_frames = [0, 120, 121, 210, 220, 300, 340, 345, 346, 347, 348, 349, 353, 420] + relative_table = [] + for frame in relative_frames: + relative_table.append({ + "source_frame": frame, + "failed_vs_source": pose_error(actual_rel[frame], source_rel[frame]), + "accepted_vs_source": pose_error(accepted_rel[frame], source_rel[frame]), + "failed_target_z_m": float(actual_target[frame, 2]), + "accepted_target_z_m": float(accepted_target[frame, 2]), + }) + + source_names = source["robot_dof_names"].tolist() + current_names = current["dof_names"].tolist() + reorder = [current_names.index(name) for name in source_names] + q_error = current["q"][current_samples][:, reorder] - source["robot_joint_position_rad_or_m"] + arm = [source_names.index(f"idx6{i}_arm_r_joint{i}") for i in range(1, 8)] + gripper = [i for i, name in enumerate(source_names) if "gripper_r" in name] + outer = source_names.index("idx81_gripper_r_outer_joint1") + inner = source_names.index("idx71_gripper_r_inner_joint1") + + desired_base = np.asarray(job["items"][1]["pick_base"]) + actual_base = current["base_pose"][current_pick[0]] + desired_yaw = float(np.arctan2(desired_base[1, 0], desired_base[0, 0])) + actual_yaw = float(Rotation.from_quat(actual_base[3:]).as_euler("xyz")[2]) + old_desired = np.asarray(old_job["initial_base"]) + old_base = old["base_pose"][old_pick[0]] + old_desired_yaw = float(np.arctan2(old_desired[1, 0], old_desired[0, 0])) + old_yaw = float(Rotation.from_quat(old_base[3:]).as_euler("xyz")[2]) + + # The case-2 shelf registration is identity (see SHELF_COMPOSITION.json). + registered_initial = source_target[0] + + source_motion = np.flatnonzero(np.linalg.norm(source_target[:, :3] - source_target[0, :3], axis=1) > 0.002) + actual_motion = np.flatnonzero(np.linalg.norm(actual_target[:, :3] - actual_target[0, :3], axis=1) > 0.002) + accepted_motion = np.flatnonzero(np.linalg.norm(accepted_target[:, :3] - accepted_target[0, :3], axis=1) > 0.002) + source_lift = np.flatnonzero(source_target[:, 2] > source_target[0, 2] + 0.002) + actual_lift = np.flatnonzero(actual_target[:, 2] > actual_target[0, 2] + 0.002) + accepted_lift = np.flatnonzero(accepted_target[:, 2] > accepted_target[0, 2] + 0.002) + maximum_frame = int(np.argmax(actual_target[:, 2])) + + failed_contacts = target_contact_intervals(C / "rollout/contacts.jsonl.gz", 9205) + accepted_contacts = target_contact_intervals(OLD / "rollout/contacts.jsonl.gz", 481) + failed_lift_impulse = gripper_impulse_stats(C / "rollout/contacts.jsonl.gz", 9205, 211, 300) + accepted_lift_impulse = gripper_impulse_stats(OLD / "rollout/contacts.jsonl.gz", 481, 211, 300) + + result = { + "schema": "ziche.multi_item.case2_oreo_pick_failure.v1", + "read_only": True, + "case": 2, + "item": 1, + "product": "oreo_gf", + "failure_tick": 10467, + "source_episode": str(SRC), + "evidence": { + "failed_recording": str(C / "rollout/recording_60hz.npz"), + "failed_contacts": str(C / "rollout/contacts.jsonl.gz"), + "source_recording": str(SRC / "recording_20hz.npz"), + "controls": str(C / "controls_item_02.npz"), + "accepted_chain_recording": str(OLD / "rollout/recording_60hz.npz"), + "accepted_chain_contacts": str(OLD / "rollout/contacts.jsonl.gz"), + "accepted_chain_job": str(OLD / "job.json"), + }, + "registration_and_control": { + "pick_source_frames": int(len(source["frame_index"])), + "actual_pick_ticks": int(len(current_pick)), + "three_ticks_per_source_frame": bool(len(current_pick) == 3 * len(source["frame_index"])), + "right_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, arm] ** 2))), + "right_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, arm]))), + "right_gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, gripper] ** 2))), + "right_gripper_tracking_max_rad": float(np.max(np.abs(q_error[:, gripper]))), + "target_initial_registration_error": pose_error(actual_target[0], registered_initial), + "shelf_board_contact_end_source_frame_failed": failed_contacts["shelf_board"]["source_frame_intervals"][0][1], + "shelf_board_contact_end_source_frame_accepted": accepted_contacts["shelf_board"]["source_frame_intervals"][0][1], + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_base[:2].tolist(), + "actual_minus_requested_xy_m": (actual_base[:2] - desired_base[:2, 3]).tolist(), + "xy_error_m": float(np.linalg.norm(actual_base[:2] - desired_base[:2, 3])), + "yaw_error_rad": float(np.arctan2(np.sin(actual_yaw - desired_yaw), np.cos(actual_yaw - desired_yaw))), + "accepted_layout06_xy_error_m": float(np.linalg.norm(old_base[:2] - old_desired[:2, 3])), + "accepted_layout06_yaw_error_rad": float(np.arctan2(np.sin(old_yaw - old_desired_yaw), np.cos(old_yaw - old_desired_yaw))), + }, + "pickup_timing_and_outcome": { + "source_motion_first_frame": int(source_motion[0]), + "failed_motion_first_frame": int(actual_motion[0]), + "accepted_motion_first_frame": int(accepted_motion[0]), + "source_lift_first_frame": int(source_lift[0]), + "failed_lift_first_frame": int(actual_lift[0]), + "accepted_lift_first_frame": int(accepted_lift[0]), + "failed_initial_z_m": float(actual_target[0, 2]), + "failed_max_z_m": float(actual_target[maximum_frame, 2]), + "failed_max_lift_m": float(actual_target[maximum_frame, 2] - actual_target[0, 2]), + "failed_max_lift_source_frame": maximum_frame, + "failed_final_z_m": float(actual_target[-1, 2]), + "failed_final_translation_m": (actual_target[-1, :3] - actual_target[0, :3]).tolist(), + "source_final_translation_m": (source_target[-1, :3] - source_target[0, :3]).tolist(), + }, + "finger_evidence": { + "source_final_outer_inner_rad": [float(source["robot_joint_position_rad_or_m"][-1, outer]), float(source["robot_joint_position_rad_or_m"][-1, inner])], + "failed_final_outer_inner_rad": [float(current["q"][current_samples[-1], current_names.index(source_names[outer])]), float(current["q"][current_samples[-1], current_names.index(source_names[inner])])], + "interpretation": "The finger joints finish close to the source values. Unlike case 1 Zbar, this is not a closure stall caused by early external contact.", + }, + "contact_timeline": { + "failed": failed_contacts, + "accepted_layout06": accepted_contacts, + "causal_order": "Failed gripper contact ends at source frame 347.33; the first bracket contact is frame 349.0 and first floor contact is frame 353.67. Therefore bracket/floor contacts follow loss of grasp. The accepted chain retains gripper contact through frame 420.67 and has no bracket or floor contact during shelf_pick.", + }, + "pinch_contact_strength": { + "failed_lift": failed_lift_impulse, + "accepted_layout06_lift": accepted_lift_impulse, + "accepted_to_failed_mean_ratio": float( + accepted_lift_impulse["mean_aggregate_impulse_norm_per_contact_tick"] + / failed_lift_impulse["mean_aggregate_impulse_norm_per_contact_tick"] + ), + "interpretation": "Both fingers remain in contact over the lift interval, but the failed run's aggregate target/gripper impulse is much weaker than the accepted exact-base run. This directly supports a marginal pinch before the later slip.", + }, + "relative_grasp_comparison": relative_table, + "diagnosis": { + "finding": "The source controls, object registration, shelf contact timing, arm tracking, closure, and initial lift all reproduce the accepted chain. Oreo is lifted 58.5 mm, then loses both gripper contacts during extraction and subsequently strikes the bracket and floor.", + "cause_assessment": "The 2.019 mm / 0.000734 rad return residual is the only measured setup discrepancy large enough to distinguish this run from the exact-base accepted chain. It shifts the initial source-relative hand/target transform by about 1.50 mm, delays first gripper contact by about 1.33 source frames, and produces a 5.64x weaker mean aggregate target/gripper impulse during lift frames 211-300. This makes arrival residual the best-supported cause of a marginal pinch that slips at frames 346-348; the causal attribution to base error remains an inference, while the weak pinch and subsequent slip are directly measured.", + "not_supported": [ + "No premature shelf/bracket collision: shelf-board contact ends at exactly frame 212.33 in failed and accepted runs, while bracket contact starts only after grasp loss.", + "No target-registration failure: initial registered pose error is about 10 micrometers.", + "No gross arm-control or finger-closure failure: arm tracking is sub-milliradian RMS and final fingers closely match source closure.", + ], + }, + "retry_recommendation": { + "recommend_fresh_physical_retry": True, + "runner": "Use the already-qualified precision empty-return controller without changing the Oreo162 source trajectory, shelf scene, or three-item tray plan.", + "mandatory_pre_pick_gate": { + "base_xy_error_max_m": 0.0005, + "base_yaw_error_max_rad": 0.0001, + "action_if_failed": "Keep the boundary frozen and re-run physical precision alignment; do not start source controls.", + }, + "runtime_grasp_gate": "Before leaving shelf_pick, require continued two-finger target contact and target-hand transform drift <=5 mm through the final extraction frames; abort as a physical pick failure if contact is lost.", + "rationale": "The same source succeeded from an effectively exact base in accepted layout06, and all first-three placement slots already passed offline solve. A precision-return retry isolates the demonstrated failure variable without adding a new grasp or maneuver.", + }, + } + out = C / "CASE2_PICK_FAILURE_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..25f4c191bbea4d809a905f9477a8004d09d472f5 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +"""Read-only measured-grasp comparison and geometric landing proposal for case 4.""" +import json +import pathlib + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.affinity import translate +from shapely.geometry import Polygon + +C = pathlib.Path(__file__).resolve().parent / "cases/layout_04" + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + result[:3, 3] = pose[:3] + return result + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + proposal_path = C / "STATION_SLOT_REACHABILITY_PROPOSAL.json" + job_path = C / "job.json" + controls_path = C / "controls_item_01.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + proposal = json.loads(proposal_path.read_text()) + job = json.loads(job_path.read_text()) + accepted = proposal["items"][0]["accepted"]["report"] + link = "/World/Robot/gripper_l_base_link" + link_index = checkpoint["robot_link_paths"].tolist().index(link) + target_index = checkpoint["target_paths"].tolist().index(job["items"][0]["target"]) + hand = pose_matrix(checkpoint["robot_link_pose"][-1, link_index]) + target = pose_matrix(checkpoint["target_pose_all"][-1, target_index]) + live_held = np.linalg.inv(hand) @ target + accepted_held = np.asarray(accepted["held"], dtype=float) + accepted_goal = np.asarray(accepted["goal"], dtype=float) + accepted_hand = accepted_goal @ np.linalg.inv(accepted_held) + compensated_goal = accepted_hand @ live_held + held_delta = np.linalg.inv(accepted_held) @ live_held + + vertices = np.load(controls_path)["target_local"] + world = (np.c_[vertices, np.ones(len(vertices))] @ compensated_goal.T)[:, :3] + footprint = Polygon(world[ConvexHull(world[:, :2]).vertices, :2]) + tray = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + reserved = [Polygon(item["placement_slot"]["footprint_xy"]) for item in job["items"][1:]] + # Smallest 1 mm grid shift that restores the required >12 mm reserved-slot gap. + feasible = [] + for dx in np.linspace(-0.08, 0.08, 161): + for dy in np.linspace(-0.08, 0.08, 161): + candidate = translate(footprint, dx, dy) + gaps = [candidate.distance(other) for other in reserved] + if tray.covers(candidate) and min(gaps) > 0.012: + feasible.append((float(np.hypot(dx, dy)), float(dx), float(dy), gaps, tray.exterior.distance(candidate))) + feasible.sort(key=lambda x: x[0]) + _, dx, dy, gaps, boundary = feasible[0] + shifted_goal = compensated_goal.copy() + shifted_goal[:2, 3] += [dx, dy] + measured_base = pose_matrix(checkpoint["base_pose"][-1]) + shifted_base = measured_base.copy() + shifted_base[:2, 3] += [dx, dy] + shifted_footprint = translate(footprint, dx, dy) + + result = { + "schema": "multi_item.case4_live_grasp_place_proposal.v1", + "read_only": True, + "case": 4, + "item": 0, + "product": job["items"][0]["product"], + "checkpoint_tick": int(checkpoint["tick"][-1]), + "evidence": { + "checkpoint": str(checkpoint_path), + "job": str(job_path), + "controls": str(controls_path), + "offline_station_proposal": str(proposal_path), + "exact_chain_proposal": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), + }, + "measured": { + "robot_base": measured_base.tolist(), + "gripper_link": link, + "gripper_world": hand.tolist(), + "target_world": target.tolist(), + "held_gripper_to_target": live_held.tolist(), + }, + "accepted_reference": { + "candidate": accepted["candidate"], + "goal": accepted_goal.tolist(), + "held_gripper_to_target": accepted_held.tolist(), + "held_delta_translation_m": float(np.linalg.norm(held_delta[:3, 3])), + "held_delta_rotation_rad": float(Rotation.from_matrix(held_delta[:3, :3]).magnitude()), + }, + "diagnosis": "The live held transform differs materially from the offline accepted-source grasp, so the old endpoint relation is not directly reusable. The runtime failure magnitude is consistent with this measured mismatch.", + "proposal": { + "derivation": "Keep the accepted candidate-8 hand endpoint, substitute the live measured held transform, then apply the smallest 1 mm-grid XY station/object shift satisfying tray inset and all future-slot gaps.", + "unshifted_compensated_goal": compensated_goal.tolist(), + "station_shift_world_xy_m": [dx, dy], + "proposed_place_base": shifted_base.tolist(), + "proposed_object_goal": shifted_goal.tolist(), + "footprint_xy": np.asarray(shifted_footprint.exterior.coords).tolist(), + "tray_inset_boundary_clearance_m": float(boundary), + "future_slot_gaps_m": [float(x) for x in gaps], + "footprint_inside_tray_inset": bool(tray.covers(shifted_footprint)), + "lowest_vertex_z_m": float(world[:, 2].min()), + "tray_floor_z_m": float(job["tray_floor_z"]), + }, + "validation_scope": { + "passed": ["measured transform reconstruction", "tray 8 mm inset coverage", "future footprint gap >12 mm"], + "requires_live_runner": ["endpoint IK from live state", "full-body/world collision path", "support settling"], + "claim": "geometrically screened repair seed; not an applied or native-validated repair", + }, + } + out = C / "CASE4_ITEM00_PLACE_REPAIR_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..1dbabda08c77871027c6550d4218827ad32cca31 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py @@ -0,0 +1,26 @@ +from pathlib import Path +import json,gzip +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;C=W/'cases/layout_05' +s=json.loads((C/'STATUS.json').read_text());assert s['control_ticks']==15144 and s['status']=='WAITING_FOR_PLANNING_REPAIR' +loaded=np.load(C/'rollout/checkpoint_60hz.npz');z={k:loaded[k] for k in ['target_paths','robot_link_paths','tick','robot_link_pose','target_pose_all']};j=json.loads((C/'job.json').read_text());target=j['items'][1]['target'];ti=z['target_paths'].tolist().index(target);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link') +def matrix(p): + m=np.eye(4);m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m +indices=np.flatnonzero(np.asarray(z['tick'])>=13936);held=np.array([np.linalg.inv(matrix(z['robot_link_pose'][k,hi]))@matrix(z['target_pose_all'][k,ti]) for k in indices]);drifts=np.linalg.norm(held[:,:3,3]-held[0,:3,3],axis=1) +rows=[];last=0 +try: + with gzip.open(C/'rollout/contacts.jsonl.gz','rt') as f: + for line in f: + try:r=json.loads(line) + except json.JSONDecodeError:continue + last=max(last,r['tick']) + if r['tick']>=14900 and target in r['actors']:rows.append(r) +except EOFError:pass +summary={} +for r in rows: + other=next((x for x in r['actors'] if x!=target),'');a=summary.setdefault(other,{'first_tick':r['tick'],'last_tick':r['tick'],'ticks':set(),'min_separation_m':1.,'impulse_norm_sum':0.}) + a['last_tick']=r['tick'];a['ticks'].add(r['tick']);a['min_separation_m']=min(a['min_separation_m'],r['min_separation_m']);a['impulse_norm_sum']+=float(np.linalg.norm(r['impulse_sum'])) +for a in summary.values():a['tick_count']=len(a.pop('ticks')) +report=dict(read_only=True,physics_state_unchanged=True,status=s,checkpoint_ticks=len(z['tick']),complete_contact_through_tick=last,target_hand_center_distance_m=float(np.linalg.norm(held[-1,:3,3])),held_translation_start=held[0,:3,3].tolist(),held_translation_end=held[-1,:3,3].tolist(),max_held_translation_drift_m=float(drifts.max()),held_rotation_drift_rad=float(Rotation.from_matrix(held[0,:3,:3].T@held[-1,:3,:3]).magnitude()),target_z_last120_min_max=[float(z['target_pose_all'][-120:,ti,2].min()),float(z['target_pose_all'][-120:,ti,2].max())],recent_contacts=summary,last_rows=rows[-12:]) +(C/'CASE5_ITEM01_COMPACT_GRASP_DIAGNOSTIC.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k!='last_rows'},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..372ee219565e105a33b367648310ddf4fdd4b520 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py @@ -0,0 +1,307 @@ +#!/usr/bin/env python3 +"""Read-only authoritative-pair diagnosis for case 5 item 1 compact contact.""" +import hashlib +import inspect +import json +import math +import os +import pathlib +import sys +import zlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) + +from pxr import Usd, UsdGeom # noqa: E402 +from collision_scene import Body, burial, collider_mesh, contact, matrix, rigid_part # noqa: E402 +from collision_acceptance import summarize # noqa: E402 +import collision_acceptance # noqa: E402 +import collision_scene # noqa: E402 +import collision_sweep # noqa: E402 + +TARGET = "/World/Assets/Tier2Items/zbar_t" +HAND = "/World/Robot/gripper_r_base_link" +INNER = "/World/Robot/gripper_r_inner_link4" +OUTER = "/World/Robot/gripper_r_outer_link4" +TORSO = "/World/Robot/body_link2" +TORSO_COLLIDER = TORSO + "/collisions/mesh_1/box" +TARGET_COLLIDER = TARGET + "/CanonicalGraspCollision" +FIRST_TICK = 15120 +LAST_TICK = 15144 + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def interpolate(a, b, u): + result = np.eye(4) + result[:3, 3] = (1.0 - u) * a[:3, 3] + u * b[:3, 3] + r0 = Rotation.from_matrix(a[:3, :3]) + delta = (r0.inv() * Rotation.from_matrix(b[:3, :3])).as_rotvec() + result[:3, :3] = (r0 * Rotation.from_rotvec(u * delta)).as_matrix() + return result + + +def local_collider(stage, path, owner_path): + cache = UsdGeom.XformCache() + prim = stage.GetPrimAtPath(path) + owner = stage.GetPrimAtPath(owner_path) + mesh, method, approximation = collider_mesh(prim) + authored_owner = rigid_part(matrix(cache.GetLocalToWorldTransform(owner))) + authored_shape = matrix(cache.GetLocalToWorldTransform(prim)) + mesh.apply_transform(np.linalg.inv(authored_owner) @ authored_shape) + return Body( + path, + mesh, + metadata={ + "owner": owner_path, + "collision_geometry_method": method, + "collision_approximation": approximation, + }, + ) + + +def partial_contacts(path): + raw = zlib.decompressobj(16 + zlib.MAX_WBITS).decompress(path.read_bytes()) + result = [] + incomplete = 0 + for line in raw.splitlines(): + try: + result.append(json.loads(line)) + except json.JSONDecodeError: + incomplete += 1 + return result, incomplete + + +def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contacts_path = C / "rollout/contacts.jsonl.gz" + scene_path = C / "rollout/scene.usda" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + status = json.loads((C / "STATUS.json").read_text()) + ticks = checkpoint["tick"].astype(int) + selected = np.flatnonzero((ticks >= FIRST_TICK) & (ticks <= LAST_TICK)) + if ticks[selected[0]] != FIRST_TICK or ticks[selected[-1]] != LAST_TICK: + raise RuntimeError("Pinned case 5 diagnostic interval is unavailable") + link_paths = checkpoint["robot_link_paths"].tolist() + target_paths = checkpoint["target_paths"].tolist() + torso_index = link_paths.index(TORSO) + hand_index = link_paths.index(HAND) + target_index = target_paths.index(TARGET) + + stage = Usd.Stage.Open(str(scene_path)) + torso = local_collider(stage, TORSO_COLLIDER, TORSO) + target = local_collider(stage, TARGET_COLLIDER, TARGET) + geometry = { + "torso": { + "path": TORSO_COLLIDER, + "method": torso.metadata["collision_geometry_method"], + "approximation": torso.metadata["collision_approximation"], + "watertight": bool(torso.mesh.is_watertight), + }, + "target": { + "path": TARGET_COLLIDER, + "method": target.metadata["collision_geometry_method"], + "approximation": target.metadata["collision_approximation"], + "watertight": bool(target.mesh.is_watertight), + }, + } + + exact_samples = [] + exact_hits = [] + q = checkpoint["q"] + torso_poses = checkpoint["robot_link_pose"][:, torso_index] + target_poses = checkpoint["target_pose_all"][:, target_index] + for position, frame in enumerate(selected): + next_frame = selected[position + 1] if position + 1 < len(selected) else frame + torso0, torso1 = pose_matrix(torso_poses[frame]), pose_matrix(torso_poses[next_frame]) + target0, target1 = pose_matrix(target_poses[frame]), pose_matrix(target_poses[next_frame]) + link_translation = float(np.linalg.norm(torso1[:3, 3] - torso0[:3, 3])) + target_translation = float(np.linalg.norm(target1[:3, 3] - target0[:3, 3])) + link_rotation = float( + (Rotation.from_matrix(torso0[:3, :3]).inv() * Rotation.from_matrix(torso1[:3, :3])).magnitude() + ) + target_rotation = float( + (Rotation.from_matrix(target0[:3, :3]).inv() * Rotation.from_matrix(target1[:3, :3])).magnitude() + ) + joint_step = float(np.max(np.abs(q[next_frame] - q[frame]))) + subdivisions = max( + 1, + math.ceil(joint_step / 0.03), + math.ceil(link_translation / 0.005), + math.ceil(target_translation / 0.005), + math.ceil(link_rotation / 0.03), + math.ceil(target_rotation / 0.03), + ) + for subdivision in range(subdivisions): + alpha = subdivision / subdivisions + torso.update(interpolate(torso0, torso1, alpha)) + target.update(interpolate(target0, target1, alpha)) + hit = contact(torso, target, containment=True) + row = { + "source_control_tick": int(ticks[frame]), + "next_control_tick": int(ticks[next_frame]), + "alpha": float(alpha), + "reviewer_frame": float(ticks[frame] - 1 + alpha * (ticks[next_frame] - ticks[frame])), + "triangle_intersection": hit, + } + exact_samples.append(row) + if hit is not None: + row["sampled_burial"] = burial(torso, target, max_points=2048) + exact_hits.append(row) + + native_rows, incomplete = partial_contacts(contacts_path) + target_rows = [] + inner_ticks = set() + outer_ticks = set() + torso_rows = [] + for row in native_rows: + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(TARGET, values): + continue + target_rows.append(row) + tick = int(row["tick"]) + if touches(INNER, values): + inner_ticks.add(tick) + if touches(OUTER, values): + outer_ticks.add(tick) + if touches(TORSO, values): + torso_rows.append(row) + body_ticks = sorted({int(row["tick"]) for row in torso_rows}) + bilateral = sorted(inner_ticks & outer_ticks) + + hand_relative = [] + for frame in selected: + hand_relative.append( + np.linalg.inv(pose_matrix(checkpoint["robot_link_pose"][frame, hand_index])) + @ pose_matrix(checkpoint["target_pose_all"][frame, target_index]) + ) + hand_relative = np.asarray(hand_relative) + shift = np.linalg.norm(hand_relative[:, :3, 3] - hand_relative[0, :3, 3], axis=1) + rotation_shift = np.asarray( + [ + (Rotation.from_matrix(hand_relative[0, :3, :3]).inv() * Rotation.from_matrix(value[:3, :3])).magnitude() + for value in hand_relative + ] + ) + + pair = { + "a": TORSO_COLLIDER + "#native_robot_collider", + "b": TARGET_COLLIDER + "#dynamic_physical_collider", + "category": "robot_target", + "sample_count": len(exact_hits), + "first_frame": exact_hits[0]["reviewer_frame"] if exact_hits else None, + "last_frame": exact_hits[-1]["reviewer_frame"] if exact_hits else None, + "a_collision_geometry_method": geometry["torso"]["method"], + "b_collision_geometry_method": geometry["target"]["method"], + "a_collision_approximation": geometry["torso"]["approximation"], + "b_collision_approximation": geometry["target"]["approximation"], + } + authoritative_input = { + "static_collider_coverage": {"complete": True}, + "all_scene_pairs_checked": True, + "solid_containment_checked": True, + "frame_stride": 1, + "swept_sampling": True, + "complete_trajectory_checked": True, + "pairs": [pair] if exact_hits else [], + } + authoritative_decision = summarize(authoritative_input, contact_tolerance_mm=0.5) + + result = { + "schema": "multi_item.case5_item01_compact_grasp_diagnostic.v1", + "read_only": True, + "decision": "CONFIRMED_PHYSICAL_PREFIX_FAILURE" if exact_hits else "NO_EXACT_INTERSECTION_IN_PINNED_INTERVAL", + "status_at_probe": status, + "item_index": 1, + "target": TARGET, + "interval_control_ticks": [FIRST_TICK, LAST_TICK], + "inputs": { + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "partial_contacts": str(contacts_path), + "partial_contacts_sha256": hashlib.sha256(contacts_path.read_bytes()).hexdigest(), + "discarded_incomplete_tail_rows": incomplete, + "scene": str(scene_path), + "scene_sha256": hashlib.sha256(scene_path.read_bytes()).hexdigest(), + }, + "native_contact_evidence": { + "target_torso_contact_tick_range": [body_ticks[0], body_ticks[-1]] if body_ticks else None, + "target_torso_distinct_control_ticks": len(body_ticks), + "target_torso_callback_rows": len(torso_rows), + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in torso_rows) if torso_rows else None, + "sum_impulse_norm_over_callback_rows": float(sum(np.linalg.norm(row["impulse_sum"]) for row in torso_rows)), + "bilateral_active_finger_ticks_in_interval": [tick for tick in bilateral if FIRST_TICK <= tick <= LAST_TICK], + "bilateral_active_finger_all_interval_ticks": all(tick in bilateral for tick in range(FIRST_TICK, LAST_TICK + 1)), + "target_to_hand_max_translation_shift_over_interval_m": float(np.max(shift)), + "target_to_hand_final_translation_shift_m": float(shift[-1]), + "target_to_hand_max_rotation_shift_rad": float(np.max(rotation_shift)), + }, + "authoritative_geometry_reproduction": { + "geometry": geometry, + "method": "The same collision_scene.collider_mesh exact authored cube boundaries, triangle contact/containment test, 5 mm literal-link/target translation step, 0.03 rad rotation/joint step, and collision_acceptance.summarize policy used by the native world reviewer.", + "sample_count": len(exact_samples), + "exact_intersection_count": len(exact_hits), + "exact_intersections": exact_hits, + "pair_category": "robot_target", + "acceptance_result": authoritative_decision, + }, + "policy_interpretation": { + "robot_target_is_forbidden": True, + "active_finger_10mm_policy_applies": False, + "default_0p5mm_contact_margin_applies": False, + "reason": "collision_acceptance treats robot_target as a forbidden non-contact category. The target/body_link2 pair is neither source/tray support nor the assigned distal link4 grasp pair; exact triangle crossing is a hard failure regardless of its shallow sampled depth.", + }, + "repair_decision": { + "retained_grasp": bool(all(tick in bilateral for tick in range(FIRST_TICK, LAST_TICK + 1))), + "safe_replan_may_continue_this_prefix_for_acceptance": False if exact_hits else None, + "recommended_action": "Preserve this native capture as a physical failure and use the screened proposal only in a fresh retry whose compact path avoids torso contact." if exact_hits else "No conclusion beyond the pinned interval.", + "no_active_case_write_or_motion_performed": True, + }, + "reviewer_sources": { + "collision_scene_sha256": hashlib.sha256(pathlib.Path(collision_scene.__file__).read_bytes()).hexdigest(), + "collision_sweep_sha256": hashlib.sha256(pathlib.Path(collision_sweep.__file__).read_bytes()).hexdigest(), + "collision_acceptance_sha256": hashlib.sha256(pathlib.Path(collision_acceptance.__file__).read_bytes()).hexdigest(), + }, + "limitations": [ + "Only the requested ticks 15120..15144 and the reported physical target/body_link2 collider pair are replayed here; the eventual independent reviewer still checks the whole episode and every in-scope pair.", + "Sampled burial is a lower bound. The acceptance decision relies on exact triangle crossing in exact authored cube boundaries, not on the burial magnitude.", + ], + } + output = W / "CASE5_ITEM01_COMPACT_GRASP_DIAGNOSTIC.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print( + json.dumps( + { + "output": str(output), + "decision": result["decision"], + "native": result["native_contact_evidence"], + "exact_intersection_count": len(exact_hits), + "exact_frames": [row["reviewer_frame"] for row in exact_hits], + "acceptance_geometry_status": authoritative_decision["geometry_status"], + "hard_failures": authoritative_decision["hard_failures"], + }, + indent=2, + ) + ) + raise SystemExit(0 if exact_hits else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..f203b8a79e747ad172f143d539105ab0dfbf7ed2 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +"""Reproduce the immutable case 9 HoneyMaid false-pick diagnosis.""" +import gzip +import hashlib +import json +import pathlib +import zlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_09" +SOURCE = pathlib.Path( + "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table50_20261001/cases/layout_18/rollout" +) +TARGET = "/World/Assets/Tier2Items/honeymaid" +OREO = "/World/Assets/Tier2Items/oreo_gf" + + +def decode(value): + return value.decode() if isinstance(value, bytes) else str(value) + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def load_open_gzip(path): + raw = zlib.decompressobj(16 + zlib.MAX_WBITS).decompress(path.read_bytes()) + rows = [] + incomplete = 0 + for line in raw.splitlines(): + try: + rows.append(json.loads(line)) + except json.JSONDecodeError: + incomplete += 1 + return rows, incomplete + + +def load_closed_gzip(path): + with gzip.open(path, "rt") as stream: + return [json.loads(line) for line in stream] + + +def target_category(row): + other = next(actor for actor in row["actors"] if actor != TARGET) + if other.startswith("/World/Robot"): + collider = next(value for value in row["colliders"] if value.startswith("/World/Robot")) + if "gripper_r_inner_link4" in collider: + return "right_inner_link4" + if "gripper_r_outer_link4" in collider: + return "right_outer_link4" + return "robot_other" + if other == OREO: + return "oreo" + if "ShelvingUnit" in other: + return "source_shelf" + return other + + +def target_tick_categories(rows): + result = {} + for row in rows: + if TARGET not in row.get("actors", []): + continue + result.setdefault(int(row["tick"]), set()).add(target_category(row)) + return result + + +def range_for(categories, name): + ticks = sorted(tick for tick, values in categories.items() if name in values) + return { + "first_tick": ticks[0] if ticks else None, + "last_tick": ticks[-1] if ticks else None, + "distinct_control_ticks": len(ticks), + } + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contacts_path = C / "rollout/contacts.jsonl.gz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + phases = np.array([decode(value) for value in checkpoint["phase"]]) + ticks = checkpoint["tick"].astype(int) + item2 = np.flatnonzero(checkpoint["item_index"] == 2) + target_index = checkpoint["target_paths"].tolist().index(TARGET) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_pose = checkpoint["target_pose_all"][:, target_index] + hand_pose = checkpoint["robot_link_pose"][:, hand_index] + current_rows, incomplete = load_open_gzip(contacts_path) + current_categories = target_tick_categories(current_rows) + last_complete_tick = max(current_categories) + current_window = range(last_complete_tick - 29, last_complete_tick + 1) + simultaneous = sorted( + tick + for tick, values in current_categories.items() + if {"right_inner_link4", "right_outer_link4"} <= values + ) + current_relative = [ + np.linalg.inv(matrix(hand_pose[index])) @ matrix(target_pose[index]) for index in item2 + ] + final_relative = current_relative[-1] + relative_translation_drift = np.array( + [np.linalg.norm(value[:3, 3] - final_relative[:3, 3]) for value in current_relative] + ) + relative_rotation_drift = np.array( + [Rotation.from_matrix(value[:3, :3].T @ final_relative[:3, :3]).magnitude() for value in current_relative] + ) + + robot_oreo = [ + row + for row in current_rows + if OREO in row.get("actors", []) and any(actor.startswith("/World/Robot") for actor in row.get("actors", [])) + ] + outer_oreo = [ + row + for row in robot_oreo + if any("gripper_r_outer_link4" in collider for collider in row.get("colliders", [])) + ] + + source_recording_path = SOURCE / "recording_60hz.npz" + source_contacts_path = SOURCE / "contacts.jsonl.gz" + source_recording = np.load(source_recording_path, allow_pickle=True) + source_phases = np.array([decode(value) for value in source_recording["phase"]]) + source_pick = np.flatnonzero(source_phases == "shelf_pick") + source_pick_ticks = source_recording["tick"][source_pick].astype(int) + source_last30 = set(source_pick_ticks[-30:].tolist()) + source_rows = load_closed_gzip(source_contacts_path) + source_categories = target_tick_categories(source_rows) + + def window_count(categories, name, window): + return sum(name in categories.get(tick, set()) for tick in window) + + result = { + "schema": "multi_item.case9_item2_false_pick_diagnosis.v1", + "read_only": True, + "decision": "PHYSICAL_PICK_FAILURE_NOT_CARRY_PLANNING_BOUNDARY", + "case": "layout_09", + "item_index": 2, + "target": TARGET, + "obstructing_unrequested_prop": OREO, + "frozen_status": json.loads((C / "STATUS.json").read_text()), + "inputs": { + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "open_contact_stream": str(contacts_path), + "open_contact_stream_sha256": hashlib.sha256(contacts_path.read_bytes()).hexdigest(), + "discarded_incomplete_tail_rows": incomplete, + "accepted_source_recording": str(source_recording_path), + "accepted_source_contacts": str(source_contacts_path), + }, + "current_native_evidence": { + "item2_phase_tick_range": [int(ticks[item2[0]]), int(ticks[item2[-1]])], + "target_initial_z_m": float(target_pose[item2[0], 2]), + "target_maximum_z_m": float(np.max(target_pose[item2, 2])), + "target_final_z_m": float(target_pose[item2[-1], 2]), + "height_above_item2_initial_at_final_m": float(target_pose[item2[-1], 2] - target_pose[item2[0], 2]), + "right_inner_target_contact": range_for(current_categories, "right_inner_link4"), + "right_outer_target_contact": range_for(current_categories, "right_outer_link4"), + "simultaneous_bilateral_target_contact": { + "first_tick": simultaneous[0] if simultaneous else None, + "last_tick": simultaneous[-1] if simultaneous else None, + "distinct_control_ticks": len(simultaneous), + }, + "source_shelf_target_contact": range_for(current_categories, "source_shelf"), + "oreo_target_contact": range_for(current_categories, "oreo"), + "final_30_complete_contact_ticks": { + "range": [current_window.start, current_window.stop - 1], + "right_inner_ticks": window_count(current_categories, "right_inner_link4", current_window), + "right_outer_ticks": window_count(current_categories, "right_outer_link4", current_window), + "simultaneous_bilateral_ticks": sum( + {"right_inner_link4", "right_outer_link4"} <= current_categories.get(tick, set()) + for tick in current_window + ), + "source_shelf_ticks": window_count(current_categories, "source_shelf", current_window), + "oreo_ticks": window_count(current_categories, "oreo", current_window), + }, + "right_outer_gripper_oreo_contact": { + "first_tick": min(int(row["tick"]) for row in outer_oreo), + "last_tick": max(int(row["tick"]) for row in outer_oreo), + "rows": len(outer_oreo), + "precedes_oreo_target_contact": min(int(row["tick"]) for row in outer_oreo) + < range_for(current_categories, "oreo")["first_tick"], + }, + "target_to_hand_nonrigidity_during_item2": { + "median_translation_drift_from_final_m": float(np.median(relative_translation_drift)), + "maximum_translation_drift_from_final_m": float(np.max(relative_translation_drift)), + "median_rotation_drift_from_final_rad": float(np.median(relative_rotation_drift)), + "maximum_rotation_drift_from_final_rad": float(np.max(relative_rotation_drift)), + }, + }, + "accepted_honeymaid_source_baseline": { + "source_case": "shelf_to_table50_20261001/layout_18", + "final_30_shelf_pick_tick_range": [min(source_last30), max(source_last30)], + "right_inner_ticks": window_count(source_categories, "right_inner_link4", source_last30), + "right_outer_ticks": window_count(source_categories, "right_outer_link4", source_last30), + "simultaneous_bilateral_ticks": sum( + {"right_inner_link4", "right_outer_link4"} <= source_categories.get(tick, set()) + for tick in source_last30 + ), + "source_shelf_ticks": window_count(source_categories, "source_shelf", source_last30), + "bilateral_continues_through_shelf_exit": True, + }, + "causal_sequence": [ + "Right outer gripper first contacts the unrequested Oreo at tick 21549.", + "Oreo first contacts HoneyMaid at tick 21601.", + "A one-sided HoneyMaid/right-inner contact begins at tick 21763; the later right-outer contact does not overlap it.", + "All HoneyMaid/gripper contact ends by tick 22239 while HoneyMaid remains in shelf and Oreo contact through the final complete callback tick 22901.", + "The height-only lift signal therefore came from obstructed object motion, not a retained grasp.", + ], + "repair_recommendation": { + "initial_scene": "Deactivate/remove the unrequested Oreo distractor before a fresh native retry; preserve every requested prop and physical setting.", + "prelaunch": "Screen every transferred pick approach/closure/lift path against all live non-target props plus robot/self/wrist geometry in the actual composed scene.", + "runtime_pick_gate": "Require sustained active-gripper contact and loss of source support in addition to retained lift; store the evidence window in ITEM_EVENTS/CHECKPOINT.", + "no_local_carry_repair": True, + "no_reset_or_active_case_write_performed": True, + }, + "limitations": [ + "The open native contact gzip ends with one incomplete JSON row; only complete rows are counted.", + "The failed capture remains authoritative evidence; this report does not alter or replay it.", + ], + } + output = W / "CASE09_ITEM02_FALSE_PICK_DIAGNOSIS.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"output": str(output), "decision": result["decision"], "current": result["current_native_evidence"]["final_30_complete_contact_ticks"], "source": result["accepted_honeymaid_source_baseline"]}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb6d277d547c873b89331651153546a87f88741 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +"""Read-only audit of multi-item pickup stations against their source shelf frames.""" +import glob +import json +import math +import pathlib +import sys + +import numpy as np +from pxr import Usd, UsdGeom +from scipy.spatial import ConvexHull +from shapely.geometry import Point, Polygon +from shapely.ops import nearest_points + +W = pathlib.Path(__file__).resolve().parent +ROOT = W.parents[1] +OLD = ROOT / "work/shelf_to_table50_20261001" +ACTIVE = (1, 3, 4, 6, 7, 8, 9) +sys.path.insert(0, str(W)) +from geometry_transfer import board_path, registration # noqa: E402 +from place_tray_layout import mesh_world_points # noqa: E402 + + +def matrix(stage, path): + value = UsdGeom.Xformable(stage.GetPrimAtPath(path)).ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + return np.asarray(value, dtype=float).T + + +def polygon(stage, path): + points = mesh_world_points(stage, stage.GetPrimAtPath(path))[:, :2] + return Polygon(points[ConvexHull(points).vertices]).buffer(0) + + +def rigid_error(a, b): + delta = np.linalg.inv(a) @ b + return { + "translation_m": float(np.linalg.norm(delta[:3, 3])), + "yaw_rad": float(abs(math.atan2(delta[1, 0], delta[0, 0]))), + "max_abs_matrix": float(np.max(np.abs(a - b))), + } + + +def main(): + old_jobs = [] + for path in sorted((OLD / "cases").glob("layout_*/job.json")): + data = json.loads(path.read_text()) + old_jobs.append((path, data)) + summary = { + "schema": "multi_item.pick_base_registration_audit.v1", + "read_only": True, + "active_cases": list(ACTIVE), + "method": "Recompute each raw source->released shelf rigid registration; independently transfer every accepted old-chain initial_base sharing the exact pick_source; compare current target/base relative transforms. Measure pickup-base point clearance to the released CoffeeTable hull.", + "cases": [], + } + for case_id in ACTIVE: + case = W / "cases" / f"layout_{case_id:02d}" + job_path = case / "job.json" + job = json.loads(job_path.read_text()) + room = Usd.Stage.Open(job["layout"]) + composed = Usd.Stage.Open(job["scene"]) + table = polygon(room, "/World/Assets/CoffeeTable/Mesh") + shelf = polygon(room, board_path(room)) + row = { + "case": case_id, + "job": str(job_path), + "layout": job["layout"], + "source_layout_id": job.get("source_layout_id"), + "coffee_table_hull_xy": np.asarray(table.exterior.coords).tolist(), + "shelf_hull_xy": np.asarray(shelf.exterior.coords).tolist(), + "items": [], + } + for index, item in enumerate(job["items"]): + source_path = pathlib.Path(item["pick_source"]) + source = Usd.Stage.Open(str(source_path / "scene.usda")) + spec = json.loads((source_path / "episode.json").read_text()) + shelf_delta, shelf_reg = registration(source, room, board_path(source)) + source_base = matrix(source, "/World/Robot") + expected_base = shelf_delta @ source_base + current_base = np.asarray(item["pick_base"], dtype=float) + current_target = matrix(composed, item["target"]) + source_target = matrix(source, spec["target"]["prim_path"]) + expected_target = shelf_delta @ source_target + p = Point(current_base[:2, 3]) + closest = nearest_points(table, p)[0] + escape = current_base[:2, 3] - np.asarray(closest.coords[0]) + escape_norm = float(np.linalg.norm(escape)) + if escape_norm > 1e-12: + escape /= escape_norm + yaw = math.atan2(current_base[1, 0], current_base[0, 0]) + side_plus = np.array([-math.sin(yaw), math.cos(yaw)]) + exact_matches = [] + for prior_path, prior in old_jobs: + if prior.get("pick_source") != item["pick_source"]: + continue + prior_stage = Usd.Stage.Open(prior["layout"]) + accepted_delta, accepted_reg = registration(prior_stage, room, board_path(prior_stage)) + transferred = accepted_delta @ np.asarray(prior["initial_base"], dtype=float) + exact_matches.append({ + "prior_job": str(prior_path), + "prior_layout": prior["layout"], + "transferred_initial_base": transferred.tolist(), + "current_error": rigid_error(current_base, transferred), + "registration": accepted_reg, + }) + direct_error = rigid_error(current_base, expected_base) + target_error = rigid_error(current_target, expected_target) + rel_source = np.linalg.inv(source_base) @ source_target + rel_current = np.linalg.inv(current_base) @ current_target + relation_error = rigid_error(rel_current, rel_source) + row["items"].append({ + "item": index, + "product": item["product"], + "target": item["target"], + "pick_source": item["pick_source"], + "current_pick_base": current_base.tolist(), + "recomputed_pick_base": expected_base.tolist(), + "direct_source_registration_error": direct_error, + "target_source_registration_error": target_error, + "target_relative_to_base_error": relation_error, + "source_registration_correct": max(direct_error["max_abs_matrix"], target_error["max_abs_matrix"], relation_error["max_abs_matrix"]) < 1e-6, + "exact_accepted_chain_matches": exact_matches, + "coffee_table_point_clearance_m": float(table.distance(p)), + "coffee_table_nearest_xy": list(closest.coords[0]), + "coffee_table_escape_unit_xy": escape.tolist(), + "escape_dot_local_side_plus": float(escape @ side_plus), + "preferred_local_side_sign": "+" if float(escape @ side_plus) >= 0 else "-", + "shelf_point_clearance_m": float(shelf.distance(p)), + "shelf_registration": shelf_reg, + }) + row["all_source_registrations_correct"] = all(x["source_registration_correct"] for x in row["items"]) + (case / "PICK_BASE_REGISTRATION_DIAGNOSTIC.json").write_text(json.dumps(row, indent=2) + "\n") + summary["cases"].append(row) + (W / "PICK_BASE_REGISTRATION_AUDIT.json").write_text(json.dumps(summary, indent=2) + "\n") + concise = [] + for r in summary["cases"]: + items = [] + for x in r["items"]: + items.append({ + "item": x["item"], "product": x["product"], + "table_clearance_m": x["coffee_table_point_clearance_m"], + "side": x["preferred_local_side_sign"], + "direct_err": x["direct_source_registration_error"]["max_abs_matrix"], + "accepted_errs": [m["current_error"]["max_abs_matrix"] for m in x["exact_accepted_chain_matches"]], + }) + concise.append({"case": r["case"], "all_correct": r["all_source_registrations_correct"], "items": items}) + print(json.dumps({"report": str(W / "PICK_BASE_REGISTRATION_AUDIT.json"), "cases": concise}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/geometry_transfer.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/geometry_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..0c02bfb9306e2705a148761d223642b94f21ee05 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/geometry_transfer.py @@ -0,0 +1,43 @@ +"""Rigid geometry registration; never replace released furniture or scale props.""" +import numpy as np +from pxr import Usd,UsdGeom +from shapely.geometry import Polygon +from scipy.spatial import ConvexHull +from place_tray_layout import mesh_world_points + +def board_path(stage): + return next(str(p.GetPath()) for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Assets/ShelvingUnit')) if p.IsA(UsdGeom.Mesh) and 'shelf_boards' in str(p.GetPath())) + +def registration(source,destination,path): + a=mesh_world_points(source,source.GetPrimAtPath(path));b=mesh_world_points(destination,destination.GetPrimAtPath(path)) + if a.shape!=b.shape:raise ValueError('Furniture mesh correspondence changed') + ca=a.mean(0);cb=b.mean(0);u,_,vt=np.linalg.svd((a[:,:2]-ca[:2]).T@(b[:,:2]-cb[:2]));rotation=vt.T@u.T + if np.linalg.det(rotation)<0:raise ValueError('Furniture registration reflected') + m=np.eye(4);m[:2,:2]=rotation;m[:3,3]=cb-m[:3,:3]@ca + return m,dict(source_centroid=ca.tolist(),released_centroid=cb.tolist(),source_extents=np.ptp(a,axis=0).tolist(),released_extents=np.ptp(b,axis=0).tolist(),matrix=m.tolist(),furniture_preserved=True) + +def fit_inventory(stage,objects,target,rng,get_matrix,set_matrix): + """Scene-first supported packing, preserving identities, scales and rotations.""" + boards=mesh_world_points(stage,stage.GetPrimAtPath(board_path(stage)));rows=[];footprints={};bylayer={} + for obj in objects: + path=obj['prim_path'];v=mesh_world_points(stage,stage.GetPrimAtPath(path));bottom=v[:,2].min();z=boards[:,2][np.argmin(abs(boards[:,2]-bottom))] + if abs(z-bottom)>.006:raise ValueError('Source item height not supported in released shelf') + layer=round(float(z),4);surface=boards[abs(boards[:,2]-z)<1e-4,:2];floor=Polygon(surface[ConvexHull(surface).vertices]).buffer(-.004) + poly=Polygon(v[ConvexHull(v[:,:2]).vertices,:2]);footprints[path]=(poly,floor,layer);bylayer.setdefault(layer,[]).append(path) + for layer,paths in bylayer.items(): + placed=[] + paths.sort(key=lambda p:p!=target) + for path in paths: + poly,floor,_=footprints[path];original=get_matrix(stage,path);chosen=None + for attempt in range(4000): + if attempt==0:shift=np.zeros(2) + elif path==target:break + else: + lo=np.array(floor.bounds[:2]);hi=np.array(floor.bounds[2:]);shift=rng.uniform(lo,hi)-np.array(poly.centroid.coords[0]) + from shapely.affinity import translate + proposed=translate(poly,*shift) + if floor.covers(proposed) and all(proposed.distance(p)>.012 for p in placed):chosen=(shift,proposed);break + if chosen is None:raise ValueError('Released shelf cannot support sampled inventory: '+path) + shift,proposed=chosen;updated=original.copy();updated[:2,3]+=shift;set_matrix(stage,path,updated);placed.append(proposed) + rows.append(dict(path=path,xy_shift=shift.tolist(),support_layer=layer,attempt=attempt)) + return dict(policy='scene_first_supported_inventory_no_scaling_v1',items=rows,target_pose_preserved=True) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/grasp_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/grasp_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..cae3f6f9bcdba8840ac201d4af1c174740814f22 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/grasp_stability.py @@ -0,0 +1,73 @@ +"""Measured held-object slip and high-frequency hand motion for every item cycle.""" +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np +from scipy.ndimage import uniform_filter1d +from scipy.spatial.transform import Rotation + +from multi_validation import items, recording_contract + + +def _one(z, phases, item_index, target_pose_all, item, index): + carry = { + f"item_{index:02d}_shelf_exit_reverse", + f"item_{index:02d}_carry_raise", + f"item_{index:02d}_carry_compact", + f"item_{index:02d}_forward_navigation", + } + ids = np.flatnonzero((item_index == index) & np.isin(phases, list(carry))) + if len(ids) < 10: + return dict(item_index=index, target=item["target"], passed=False, reason="Missing item navigation samples") + path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + paths = z["robot_link_paths"].tolist() + if path not in paths: + return dict(item_index=index, target=item["target"], passed=False, reason=f"Missing hand link {path}") + hp = z["robot_link_pose"][ids, paths.index(path)] + tp = target_pose_all[ids, index] + bp = z["base_pose"][ids] + relative = Rotation.from_quat(hp[:, 3:]).inv().apply(tp[:, :3] - hp[:, :3]) + slip = np.linalg.norm(relative - relative[0], axis=1) + hand_in_base = Rotation.from_quat(bp[:, 3:]).inv().apply(hp[:, :3] - bp[:, :3]) + residual = hand_in_base - uniform_filter1d(hand_in_base, size=7, axis=0, mode="nearest") + amplitude = np.linalg.norm(residual, axis=1) + relative_hand = Rotation.from_quat(bp[:, 3:]).inv() * Rotation.from_quat(hp[:, 3:]) + angles = (relative_hand[0].inv() * relative_hand).as_rotvec() + angular_residual = np.linalg.norm(angles - uniform_filter1d(angles, size=7, axis=0, mode="nearest"), axis=1) + bad = (amplitude > .005) | (angular_residual > np.deg2rad(3)) + run = longest = 0 + for value in bad: + run = run + 1 if value else 0 + longest = max(longest, run) + return dict( + item_index=index, target=item["target"], hand=item["hand"], + passed=bool(slip.max() <= .025 and longest < 6), native_hz=60, + navigation_samples=len(ids), first_source_control_tick=int(ids[0] + 1), + last_source_control_tick=int(ids[-1] + 1), + maximum_object_in_hand_translation_drift_m=float(slip.max()), + maximum_hand_highpass_residual_m=float(amplitude.max()), + maximum_hand_highpass_angular_residual_rad=float(angular_residual.max()), + angular_highpass_limit_rad=float(np.deg2rad(3)), + hand_highpass_p99_m=float(np.percentile(amplitude, 99)), + maximum_consecutive_highpass_exceedance_ticks=longest, + slip_limit_m=.025, hand_highpass_limit_m=.005, + sustained_threshold_ticks=6, filter_window_ticks=7) + + +def metrics(z, requested): + phases, item_index, _, target_pose_all = recording_contract(z, requested) + phases = np.asarray(phases) + rows = [_one(z, phases, item_index, target_pose_all, item, i) for i, item in enumerate(requested)] + return dict(passed=all(row["passed"] for row in rows), items=rows, + semantics="Measured hand pose relative to moving base and each active target pose relative to its assigned hand; all item carries use the unchanged single-item thresholds.") + + +if __name__ == "__main__": + case = Path(sys.argv[1]); out = case / "rollout"; path = out / "recording_60hz.npz" + job = json.loads((case / "job.json").read_text()) + report = metrics(np.load(path, allow_pickle=False), items(job)) + report["recording_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + (out / "GRASP_STABILITY_AUDIT.json").write_text(json.dumps(report, indent=2) + "\n") + sys.exit(0 if report["passed"] else 1) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/launch.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..727c3e0a0179c694c662fc62728c02efcacab992 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/launch.py @@ -0,0 +1,63 @@ +"""Launch one isolated continuous episode without disturbing preserved old attempts.""" +import fcntl,hashlib,json,os,subprocess,sys,time +from pathlib import Path +W=Path(__file__).resolve().parent;R=W.parents[1] +OLD=R/'work/shelf_to_table_multi10_20261005' +C=R/'work/throughput_12h_20260912' +layout,gpu=map(int,sys.argv[1:]);assert gpu in range(8) +case=W/'cases'/f'layout_{layout:02d}';owner=f'multi10-stable-layout{layout}-pid{os.getpid()}' +container=f'ziche_shelf_chain50_gpu{gpu}' +(W/'leases').mkdir(exist_ok=True) +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +def read(p):return json.loads(p.read_text()) +def frozen_owned(pid): + try: + proc=Path('/proc')/str(pid);state=next(x for x in (proc/'status').read_text().splitlines() if x.startswith('State:')) + cmd=(proc/'cmdline').read_bytes().replace(b'\0',b' ').decode() + return 'T (stopped)' in state and str(OLD) in cmd or 'T (stopped)' in state and pid==875607 and 'run_precision_return_diagnostic_v2.py' in cmd + except (FileNotFoundError,StopIteration):return False +lock=(C/f'locks/gpu{gpu}.lock').open('a+');reservation=C/f'reservations/gpu{gpu}.json' +lease=W/f'leases/gpu{gpu}.json';shared_claim=False +while True: + fcntl.flock(lock,fcntl.LOCK_EX) + live=read(reservation) + uuid=subprocess.check_output(['nvidia-smi','-i',str(gpu),'--query-gpu=uuid','--format=csv,noheader'],text=True).strip() + apps=subprocess.check_output(['nvidia-smi','--query-compute-apps=gpu_uuid,pid','--format=csv,noheader'],text=True) + pids=[int(line.split(',')[1]) for line in apps.splitlines() if line.split(',')[0].strip()==uuid] + free=int(subprocess.check_output(['nvidia-smi','-i',str(gpu),'--query-gpu=memory.free','--format=csv,noheader,nounits'],text=True).strip()) + preserved_reservation=bool(live.get('reserved') and str(live.get('scope','')).startswith(str(OLD)+'/')) + own_busy=lease.exists() and read(lease).get('active',False) + if not own_busy and (not live.get('reserved') or preserved_reservation) and all(frozen_owned(pid) for pid in pids) and free>=16000: + if subprocess.check_output(['docker','inspect','--format','{{.State.Running}}',container],text=True).strip()!='true':subprocess.run(['docker','start',container],check=True,timeout=60) + visible=subprocess.check_output(['docker','exec',container,'nvidia-smi','--query-gpu=uuid','--format=csv,noheader'],text=True).strip();assert visible==uuid + if not preserved_reservation: + write(reservation,dict(reserved=True,physical_gpu=gpu,container=container,owner=owner,pid=os.getpid(),scope=str(case),started_unix=time.time()));shared_claim=True + write(lease,dict(active=True,physical_gpu=gpu,owner=owner,pid=os.getpid(),case=str(case),preserved_stopped_pids=pids,preserved_global_reservation=live if preserved_reservation else None,free_mib_at_start=free,started_unix=time.time())) + fcntl.flock(lock,fcntl.LOCK_UN);break + fcntl.flock(lock,fcntl.LOCK_UN) + write(case/'STATUS.json',dict(status='WAIT_GPU',phase='gpu_reservation',gpu=gpu,updated=time.time(),current_pids=pids,free_mib=free)) + time.sleep(15) +try: + if (case/'rollout').exists():raise RuntimeError('Existing attempt is preserved; fresh launch may not overwrite a rollout.') + (case/'repairs').mkdir(exist_ok=True) + snapshot=case/'code_snapshot';snapshot.mkdir(exist_ok=False) + for p in W.glob('*.py'):(snapshot/p.name).write_bytes(p.read_bytes()) + write(case/'CODE_HASHES.json',{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in snapshot.glob('*.py')}) + subprocess.run([sys.executable,'-m','py_compile',*[str(p) for p in snapshot.glob('*.py')]],check=True) + cmd=['docker','exec','--user','0:0','-e','CHAIN_PROJECT_ROOT='+str(R),'-e','CHAIN_RENDER=1','-e','OMP_NUM_THREADS=2','-e','OPENBLAS_NUM_THREADS=1','-e','MKL_NUM_THREADS=1',container,'/isaac-sim/python.sh',str(snapshot/'run_episode.py'),str(case)] + write(case/'COMMAND.json',cmd);write(case/'STATUS.json',dict(status='INITIALIZING_PHYSICS',phase='startup',gpu=gpu,updated=time.time())) + with (case/'simulator.log').open('w') as stream:result=subprocess.run(cmd,stdout=stream,stderr=subprocess.STDOUT) + status=read(case/'STATUS.json') + if result.returncode or status.get('status') not in ['FAILED','PHYSICS_COMPLETE_UNVERIFIED','PLANNING_BLOCKED']: + write(case/'STATUS.json',dict(status='FAILED',phase='simulator_exit',error=f'Exit {result.returncode}; inspect simulator.log',gpu=gpu,updated=time.time())) +except Exception as error: + write(case/'STATUS.json',dict(status='FAILED',phase='launcher',error=repr(error),gpu=gpu,updated=time.time()));raise +finally: + subprocess.run(['docker','exec','--user','0:0',container,'/bin/chown','-R',f'{os.getuid()}:{os.getgid()}',str(case)],check=False) + fcntl.flock(lock,fcntl.LOCK_EX) + live=read(reservation) + if shared_claim and live.get('owner')==owner:write(reservation,dict(reserved=False,physical_gpu=gpu,container=container,owner=None,pid=None,previous_owner=owner,released_unix=time.time())) + owned=read(lease) + if owned.get('owner')==owner:owned.update(active=False,released_unix=time.time());write(lease,owned) + fcntl.flock(lock,fcntl.LOCK_UN) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/motion_limits.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/motion_limits.py new file mode 100644 index 0000000000000000000000000000000000000000..b59d93b1743935eb9afd69fb28ad70eb241a2e72 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/motion_limits.py @@ -0,0 +1,4 @@ +"""Reference command envelope; does not change authored drive or joint parameters.""" +import numpy as np +LOWER=np.array([-3.139847,-2.089159,-3.099705,-1.480039,-3.099705,-1.740093,-3.099705,-3.139847,-1.480039,-3.099705,-1.480039,-3.099705,-1.740093,-3.099705,0,0,-np.pi/2,-.349066,0,0]) +UPPER=np.array([3.139847,1.480039,3.099705,1.480039,3.099705,1.740093,3.099705,3.139847,2.089159,3.099705,1.480039,3.099705,1.740093,3.099705,.55,np.pi/2,np.pi/2,.523599,np.pi/4,np.pi/4]) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_motion.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..f68d248ca26a527b8ace9614bcc494dca99fc78e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_motion.py @@ -0,0 +1,168 @@ +"""Collision-screened empty-hand return motion for continuous multi-item runs.""" +import math,time +import numpy as np +from scipy.spatial.transform import Rotation as Rot + +from carry_transition import body_route,compact_postures +from navigation import angle_delta + + +class EmptyClearance: + """The carry full-body checker without an attached-object surrogate.""" + def __init__(self,stage,robot,names,prop_poses,deadline_s=900): + from pxr import Usd,UsdGeom,UsdPhysics + from collision_scene import Body,collider_mesh,matrix,rigid_ancestor,contact + from native_planner import build + + self.deadline=time.monotonic()+deadline_s + self.robot=robot;self.names=names;self.contact=contact + self.self_check=build(stage);self.cache=UsdGeom.XformCache() + self.environment=[];self.results={};self.posture_cache={};self.last=None + for prim in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies()): + path=str(prim.GetPath()) + if path.startswith('/World/Robot/') or not prim.HasAPI(UsdPhysics.CollisionAPI):continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False:continue + mesh,_,_=collider_mesh(prim);world=matrix(self.cache.GetLocalToWorldTransform(prim));owner=rigid_ancestor(prim) + if owner and str(owner.GetPath()) in prop_poses: + authored=matrix(self.cache.GetLocalToWorldTransform(owner));authored[:3,:3]/=np.linalg.norm(authored[:3,:3],axis=0) + world=prop_poses[str(owner.GetPath())]@np.linalg.inv(authored)@world + mesh.apply_transform(world) + # Match carry preflight: the real floor is checked by the released + # floor polygon while raised furniture remains full 3-D geometry. + if mesh.bounds[1,2]<.08:continue + self.environment.append(Body(path,mesh)) + self.low=np.array([b.lo for b in self.environment]);self.high=np.array([b.hi for b in self.environment]) + + def check(self,q,base): + if time.monotonic()>self.deadline:raise RuntimeError('Empty-hand geometry preflight exceeded its budget') + q=np.asarray(q);base=np.asarray(base);key=(q.tobytes(),base.tobytes()) + if key in self.results: + okay,self.last=self.results[key];return okay + okay=self.check_uncached(q,base);self.results[key]=(okay,self.last);return okay + + def check_uncached(self,q,base): + key=q.tobytes() + if key not in self.posture_cache: + local=self.robot.fk(self.names,q,np.eye(4));okay,detail=self.self_check(local,self.robot.root) + self.posture_cache[key]=(local,okay,detail) + local,okay,detail=self.posture_cache[key] + if not okay:self.last=dict(kind='self_or_wrist',detail=detail);return False + fk={name:base@transform for name,transform in local.items()} + for body in self.self_check.bodies:body.update(fk[body.name.replace('/World/Robot',self.robot.root,1)]) + for body in self.self_check.bodies: + indices=np.flatnonzero(np.all(self.high>=body.lo,axis=1)&np.all(self.low<=body.hi,axis=1)) + for index in indices: + other=self.environment[index] + if self.contact(body,other,containment=True) is not None: + self.last=dict(kind='environment',robot=body.name,obstacle=other.name);return False + self.last=None;return True + + def segment(self,q0,q1,b0,b1): + y0=Rot.from_matrix(b0[:3,:3]).as_euler('xyz')[2];y1=Rot.from_matrix(b1[:3,:3]).as_euler('xyz')[2] + dy=angle_delta(y1,y0) + steps=max(1,math.ceil(np.linalg.norm(b1[:3,3]-b0[:3,3])/.025),math.ceil(abs(dy)/math.radians(3)),math.ceil(np.max(abs(q1-q0))/.02)) + fixed=np.array_equal(q0,q1) + for u in np.linspace(0,1,steps+1): + b=b0.copy();b[:3,3]=(1-u)*b0[:3,3]+u*b1[:3,3];b[:3,:3]=Rot.from_euler('z',y0+u*dy).as_matrix() + if not self.check(q0 if fixed else q0*(1-u)+q1*u,b):return False + return True + + +def _behind(base,distance): + result=np.asarray(base,dtype=float).copy();yaw=Rot.from_matrix(result[:3,:3]).as_euler('xyz')[2] + result[:2,3]-=distance*np.array([math.cos(yaw),math.sin(yaw)]) + return result + + +def _lateral_transfers(next_pick_base,job): + """Yield shelf transfer poses that can approach around nearby furniture.""" + yaw=Rot.from_matrix(next_pick_base[:3,:3]).as_euler('xyz')[2] + heading=np.array([math.cos(yaw),math.sin(yaw)]) + lateral=np.array([-heading[1],heading[0]]) + offsets=job.get('return_lateral_transfer_offsets',[ + [0.,0.],[0.,-.10],[.25,.10],[-.25,.10], + [.35,.35],[-.35,.35],[.50,.35],[-.50,.35], + [.35,.55],[-.35,.55],[.55,.55],[-.55,.55], + [.40,.75],[-.40,.75],[.65,.75],[-.65,.75], + [.35,-.10],[-.35,-.10],[.50,0.],[-.50,0.], + ]) + for side,back in offsets: + result=next_pick_base.copy() + result[:2,3]=next_pick_base[:2,3]-float(back)*heading+float(side)*lateral + yield result,dict(side_m=float(side),back_m=float(back)) + + +def plan_return(stage,robot,names,q,base,prop_poses,next_pick_base,next_pick_q,release,job): + """Plan table retreat, raised transit, bridge, and exact shelf approach. + + Every segment uses EmptyClearance. The returned primitives carry a + ``bridge_before`` marker on the final shelf approach; execution must stop + at that boundary and move from the compact posture to ``next_pick_q``. + """ + from shapely.geometry import Polygon,LineString,Point + from shapely.ops import unary_union + + started=time.monotonic();q=np.asarray(q,dtype=float);base=np.asarray(base,dtype=float) + next_pick_base=np.asarray(next_pick_base,dtype=float);next_pick_q=np.asarray(next_pick_q,dtype=float) + checker=EmptyClearance(stage,robot,names,prop_poses,deadline_s=float(job.get('return_plan_budget_s',900))) + floor=unary_union([Polygon(f).buffer(0) for f in release['floors']]).buffer(-.34) + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + failures=[] + for retreat_distance in job.get('return_exit_candidates',[.45,.60,.80,.30,.20,.10,.05]): + exit_base=_behind(base,retreat_distance) + if not floor.covers(LineString([base[:2,3],exit_base[:2,3]])):continue + if not checker.segment(q,q,base,exit_base): + failures.append(dict(stage='table_retreat',distance=retreat_distance,detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base): + failures.append(dict(stage='raise',distance=retreat_distance,detail=checker.last));continue + for compact,posture in compact_postures(robot,names,high,exit_base): + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(stage='compact',distance=retreat_distance,posture=posture,detail=checker.last));continue + for standoff_distance in job.get('shelf_standoff_candidates',[.40,.30,.50,.65,.80,1.0]): + standoff=_behind(next_pick_base,standoff_distance) + if not floor.covers(Point(standoff[:2,3])):continue + # These endpoint gates do not depend on the room route. Check + # them first so an impossible pickup posture/approach does not + # consume a full lattice-search budget. + if not checker.segment(compact,next_pick_q,standoff,standoff): + failures.append(dict(stage='pickup_posture_bridge',distance=retreat_distance,standoff=standoff_distance,detail=checker.last));continue + if not checker.segment(next_pick_q,next_pick_q,standoff,next_pick_base): + failures.append(dict(stage='pickup_base_approach',distance=retreat_distance,standoff=standoff_distance,detail=checker.last));continue + route,detail=body_route(checker,compact,exit_base,standoff,release['floors'],budget=float(job.get('return_route_budget_s',120))) + if route is None: + failures.append(dict(stage='room_route',distance=retreat_distance,standoff=standoff_distance,detail=detail));continue + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2] + approach_yaw=Rot.from_matrix(next_pick_base[:3,:3]).as_euler('xyz')[2] + primitives=[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1,return_stage='table_retreat')] + primitives += [dict(p,return_stage='room_navigation') for p in route] + primitives.append(dict(kind='drive',goal=[*next_pick_base[:2,3],approach_yaw],direction=1,return_stage='shelf_approach',bridge_before=True)) + report=dict(policy='empty_hand_full_body_return_v1',table_retreat_m=retreat_distance,shelf_standoff_m=standoff_distance,standoff_base=standoff.tolist(),posture=posture,route_search=detail,failures=failures,planning_seconds=time.monotonic()-started,all_live_props_checked=True,no_attached_object=True) + return compact,primitives,report + # A coffee table can block every collinear shelf standoff even + # though the exact pickup station itself is valid. In that case, + # route to a lateral transfer pose, bridge there to the next pickup + # posture, and plan a second fully screened route to the exact base. + lateral_budget=float(job.get('return_lateral_route_budget_s',60)) + for transfer,offset in _lateral_transfers(next_pick_base,job): + if not floor.covers(Point(transfer[:2,3])):continue + if not checker.check(compact,transfer): + failures.append(dict(stage='lateral_transfer_compact',distance=retreat_distance,offset=offset,detail=checker.last));continue + if not checker.check(next_pick_q,transfer): + failures.append(dict(stage='lateral_transfer_pick_posture',distance=retreat_distance,offset=offset,detail=checker.last));continue + if not checker.segment(compact,next_pick_q,transfer,transfer): + failures.append(dict(stage='lateral_transfer_bridge',distance=retreat_distance,offset=offset,detail=checker.last));continue + route,route_detail=body_route(checker,compact,exit_base,transfer,release['floors'],budget=lateral_budget) + if route is None: + failures.append(dict(stage='lateral_transfer_route',distance=retreat_distance,offset=offset,detail=route_detail));continue + approach,approach_detail=body_route(checker,next_pick_q,transfer,next_pick_base,release['floors'],budget=lateral_budget) + if approach is None: + failures.append(dict(stage='lateral_pickup_route',distance=retreat_distance,offset=offset,detail=approach_detail));continue + if not approach:raise RuntimeError('Lateral pickup route unexpectedly returned no primitives') + approach=[dict(p) for p in approach];approach[0]['bridge_before']=True + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2] + primitives=[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1,return_stage='table_retreat')] + primitives += [dict(p,return_stage='room_navigation') for p in route] + primitives += [dict(p,return_stage='shelf_approach') for p in approach] + report=dict(policy='empty_hand_full_body_lateral_return_v2',table_retreat_m=retreat_distance,lateral_transfer=offset,transfer_base=transfer.tolist(),posture=posture,route_search=route_detail,pickup_route_search=approach_detail,failures=failures,planning_seconds=time.monotonic()-started,all_live_props_checked=True,no_attached_object=True,exact_pick_base_retained=True) + return compact,primitives,report + raise RuntimeError('No collision-screened empty-hand return route: '+str(failures[-8:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..8e26e6957723fc581c9cfeb7fec463e97a03249a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/multi_validation.py @@ -0,0 +1,301 @@ +"""Shared, strict helpers for the uninterrupted multi-item episode contract.""" +from __future__ import annotations + +import gzip +import json +import re +from pathlib import Path + +import numpy as np + + +ITEM_PHASE = re.compile(r"^item_(\d\d)_(.+)$") +CORE_PHASES = ( + "waist_to_selected_shelf", + "shelf_pick", + "shelf_exit_reverse", + "forward_navigation", + "bridge_to_place_initial", + "place_descent", + "place_release", + "release_retreat", + "settle", + "complete", +) +OPTIONAL_PHASES = {"carry_planning", "carry_raise", "carry_compact", "place_planning", "empty_return"} + + +def items(job): + result = job.get("items") + if not isinstance(result, list) or len(result) not in (3, 4): + raise ValueError("job.items must contain exactly three or four requested items") + required = {"target", "hand", "product", "pick_source", "pick_base", "controls_file", "destination_xyz"} + for i, row in enumerate(result): + missing = sorted(required - set(row)) + if missing: + raise ValueError(f"job.items[{i}] missing {missing}") + if row["hand"] not in ("l", "r"): + raise ValueError(f"job.items[{i}].hand is invalid") + targets = [row["target"] for row in result] + if len(set(targets)) != len(targets): + raise ValueError("Every requested target must be distinct") + if int(job.get("initial_tray_items", -1)) != 0: + raise ValueError("The multi-item task requires an initially empty tray") + return result + + +def controls_path(case, item): + path = Path(item["controls_file"]) + if not path.is_absolute(): + path = Path(case) / path + return path.resolve() + + +def controls(case, item): + path = controls_path(case, item) + data = np.load(path, allow_pickle=False) + missing = [key for key in ("pick", "place_initial", "target_local") if key not in data] + if missing: + raise ValueError(f"{path} missing controls arrays {missing}") + return data + + +def decoded(values): + return [x.decode() if isinstance(x, bytes) else str(x) for x in values.tolist()] + + +def recording_contract(z, requested): + n = len(z["q"]) + required = {"phase", "item_index", "active_target_path", "target_paths", "target_pose_all"} + missing = sorted(required - set(z.files)) + if missing: + raise ValueError(f"recording_60hz.npz missing multi-item fields {missing}") + paths = decoded(z["target_paths"]) + expected = [row["target"] for row in requested] + if paths != expected: + raise ValueError(f"target_paths are not in requested order: {paths!r}") + pose = np.asarray(z["target_pose_all"]) + if pose.shape != (n, len(requested), 7) or not np.isfinite(pose).all(): + raise ValueError(f"target_pose_all has invalid shape/value {pose.shape}") + item_index = np.asarray(z["item_index"], dtype=np.int64) + active = decoded(z["active_target_path"]) + if item_index.shape != (n,) or len(active) != n: + raise ValueError("item_index/active_target_path must have one value per control tick") + if np.any((item_index < -1) | (item_index >= len(requested))): + raise ValueError("item_index contains an out-of-range value") + phases = decoded(z["phase"]) + for tick, (index, path, phase) in enumerate(zip(item_index, active, phases)): + match = ITEM_PHASE.match(phase) + if phase == "initial_upper_shelf_posture": + authentic = (index == -1 and path == "") or (index == 0 and path == expected[0]) + if not authentic or any(ITEM_PHASE.match(x) for x in phases[:tick]): + raise ValueError(f"tick {tick} has invalid or non-prefix initial encoding") + continue + if index == -1: + raise ValueError(f"inactive tick {tick} occurs outside the initial posture: {phase}") + if path != expected[index]: + raise ValueError(f"tick {tick} active target does not match item_index") + if not match or int(match.group(1)) != index: + raise ValueError(f"tick {tick} phase is not prefixed for active item: {phase}") + return phases, item_index, active, pose + + +def compressed(values): + return list(dict.fromkeys(values)) + + +def item_phase_indices(phases, item_index, index, suffix=None): + mask = item_index == index + if suffix is not None: + mask &= np.asarray([p == f"item_{index:02d}_{suffix}" for p in phases]) + return np.flatnonzero(mask) + + +def validate_phase_order(phases, item_index, count): + details = [] + matches = [ITEM_PHASE.match(phase) for phase in phases] + first = np.flatnonzero([match is not None for match in matches]) + if not len(first): + raise ValueError("No active item ticks were recorded") + if any(p != "initial_upper_shelf_posture" for p in phases[: first[0]]): + raise ValueError("Only initial_upper_shelf_posture may precede item 0") + seen_items = compressed([int(match.group(1)) for match in matches if match]) + if seen_items != list(range(count)): + raise ValueError(f"item cycles are not continuous and ordered: {seen_items}") + for index in range(count): + indices = np.flatnonzero([(match is not None and int(match.group(1)) == index and item_index[tick] == index) for tick, match in enumerate(matches)]) + suffixes = [] + for tick in indices: + match = ITEM_PHASE.match(phases[tick]) + suffixes.append(match.group(2)) + unique = compressed(suffixes) + unknown = [name for name in unique if name not in set(CORE_PHASES) | OPTIONAL_PHASES] + if unknown: + raise ValueError(f"item {index} has unknown phases {unknown}") + positions = [] + for name in CORE_PHASES: + if name not in unique: + raise ValueError(f"item {index} is missing required phase {name}") + positions.append(unique.index(name)) + if positions != sorted(positions): + raise ValueError(f"item {index} core phases are out of order: {unique}") + has_return = "empty_return" in unique + if index < count - 1 and (not has_return or not (unique.index("settle") < unique.index("empty_return") < unique.index("complete"))): + raise ValueError(f"item {index} must return empty-handed to the next source before completion") + if index == count - 1 and has_return: + raise ValueError("The final item must complete at the table without an extra return") + if indices[-1] - indices[0] + 1 != len(indices): + raise ValueError(f"item {index} cycle is not contiguous") + details.append(dict(item_index=index, phases=unique, first_tick=int(indices[0] + 1), last_tick=int(indices[-1] + 1))) + return details + + +def audit_ranges(z, requested, minimum_shards=4, maximum_shards=16): + """Create target-homogeneous ranges; split long ranges without crossing item cycles.""" + _, item_index, _, _ = recording_contract(z, requested) + active = item_index.copy() + active[active < 0] = 0 + starts = np.r_[0, np.flatnonzero(active[1:] != active[:-1]) + 1] + ends = np.r_[starts[1:], len(active)] + ranges = [[int(a), int(b), int(active[a])] for a, b in zip(starts, ends)] + while len(ranges) < minimum_shards: + eligible = [(b - a, i) for i, (a, b, _) in enumerate(ranges) if b - a >= 2] + if not eligible: + break + _, i = max(eligible) + a, b, item = ranges[i] + middle = (a + b) // 2 + ranges[i : i + 1] = [[a, middle, item], [middle, b, item]] + if not (minimum_shards <= len(ranges) <= maximum_shards): + raise ValueError(f"Could not form {minimum_shards}..{maximum_shards} homogeneous audit shards") + covered = [tick for a, b, _ in ranges for tick in range(a, b)] + if covered != list(range(len(active))): + raise ValueError("Audit ranges do not cover every control tick exactly once") + return ranges + + +def load_contact_rows(path): + opener = gzip.open if str(path).endswith(".gz") else open + with opener(path, "rt") as stream: + return [json.loads(line) for line in stream if line.strip()] + + +def contact_capture_audit(out, requested, control_ticks, item_index=None, active_target=None, phases=None): + receipt_path = Path(out) / "CONTACT_CAPTURE.json" + contacts_path = Path(out) / "contacts.jsonl.gz" + if not receipt_path.exists() or not contacts_path.exists(): + return dict(passed=False, reason="Missing CONTACT_CAPTURE.json or contacts.jsonl.gz") + receipt = json.loads(receipt_path.read_text()) + expected = [row["target"] for row in requested] + rows = load_contact_rows(contacts_path) + captured = set() + invalid = [] + identity_mismatch = [] + for row_number, row in enumerate(rows, 1): + actors = list(map(str, row.get("actors", []))) + for target in expected: + if any(a == target or a.startswith(target + "/") or a.startswith(target + "#") for a in actors): + captured.add(target) + tick = int(row.get("tick", 0)) + if not 1 <= tick <= control_ticks: + invalid.append(row_number) + elif item_index is not None and active_target is not None: + if (int(row.get("item_index", -999)) != int(item_index[tick-1]) + or str(row.get("active_target_path", "")) != str(active_target[tick-1]) + or (phases is not None and str(row.get("phase", "")) != str(phases[tick-1]))): + identity_mismatch.append(row_number) + passed = ( + receipt.get("requested_targets") == expected + and receipt.get("filter_semantics") == "robot_or_any_requested_target" + and receipt.get("all_physics_ticks") is True + and int(receipt.get("dropped_rows", -1)) == 0 + and int(receipt.get("control_ticks", -1)) == control_ticks + and int(receipt.get("callback_rows", -1)) == len(rows) + and captured == set(expected) + and not invalid + and not identity_mismatch + ) + return dict(passed=passed, receipt=receipt, contact_rows=len(rows), targets_with_recorded_contacts=sorted(captured), invalid_tick_rows=invalid, active_item_identity_mismatch_rows=identity_mismatch) + + +def grasp_retention_audit(contact_rows, phases, item_index, requested): + """Reject height-only pickups without a retained, unsupported pinch. + + Contact callbacks can repeat within one 60 Hz control tick, so this gate + counts distinct control ticks. It mirrors the future producer gate while + remaining usable for already-running frozen snapshots that lack PICK_GATE + receipts. + """ + phases = np.asarray(phases) + item_index = np.asarray(item_index, dtype=np.int64) + details = [] + passed = True + + def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + for index, item in enumerate(requested): + target = str(item["target"]) + hand = str(item["hand"]) + pick = item_phase_indices(phases, item_index, index, "shelf_pick") + if not len(pick): + details.append(dict(item_index=index, target=target, passed=False, reason="missing shelf_pick phase")) + passed = False + continue + end_tick = int(pick[-1] + 1) + window_start = max(int(pick[0] + 1), end_tick - 29) + tail_start = max(window_start, end_tick - 2) + environment_start = max(int(pick[0] + 1), end_tick - 14) + prefix = f"/World/Robot/gripper_{hand}" + inner_path = prefix + "_inner_link4" + outer_path = prefix + "_outer_link4" + inner = set() + outer = set() + environment = set() + for row in contact_rows: + tick = int(row.get("tick", 0)) + if tick < min(window_start, environment_start) or tick > end_tick: + continue + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(target, values): + continue + if tick >= window_start: + if touches(inner_path, values): + inner.add(tick) + if touches(outer_path, values): + outer.add(tick) + if tick >= environment_start: + if any(not (value == target or value.startswith(target + "/") or value.startswith(target + "#") + or value == "/World/Robot" or value.startswith("/World/Robot/")) for value in values): + environment.add(tick) + bilateral = sorted(inner & outer) + minimum = max(15, int(item.get("pick_grasp_contact_min_bilateral_ticks", 15))) + tail = [tick for tick in bilateral if tick >= tail_start] + item_passed = len(bilateral) >= minimum and bool(tail) and not environment + passed &= item_passed + details.append(dict( + item_index=index, + target=target, + active_hand=hand, + gripper_prefix=prefix, + shelf_pick_tick_range=[int(pick[0] + 1), end_tick], + window_control_ticks=30, + window_start_tick=window_start, + window_end_tick=end_tick, + inner_contact_ticks=sorted(inner), + outer_contact_ticks=sorted(outer), + simultaneous_bilateral_contact_ticks=bilateral, + simultaneous_bilateral_contact_tick_count=len(bilateral), + minimum_simultaneous_bilateral_ticks=minimum, + final_tail_control_ticks=3, + final_tail_bilateral_ticks=tail, + environment_clear_control_ticks=15, + environment_contact_ticks=sorted(environment), + passed=item_passed, + )) + return dict( + schema="ziche.multi_item.grasp_retention_audit.v1", + passed=bool(passed), + semantics="distinct 60 Hz control ticks; sustained opposing active-finger contact; final-tail contact; no target/source-or-clutter contact", + items=details, + ) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/native_planner.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/native_planner.py new file mode 100644 index 0000000000000000000000000000000000000000..6818134a1b771bfa03af1b8ade2c40150004fca1 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/native_planner.py @@ -0,0 +1,33 @@ +"""Planning filter using unchanged native self-collision geometry and exclusions.""" +import numpy as np +import sys,os +from pathlib import Path +project=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(Path(__file__).resolve().parents[2]))) +sys.path.insert(0,str(project/'work/shelf_training_visible360_20260916')) +from pxr import Usd,UsdGeom,UsdPhysics +import trimesh +from collision_scene import Body,collider_mesh,matrix,broad_overlap,contact,traversal +from robot_collision_profile import collision_disabled_joint_neighbors,audit_full_robot_collision_profile + +def build(stage): + cache=UsdGeom.XformCache();profile=audit_full_robot_collision_profile(stage);filtered={tuple(sorted(p)) for p in profile['filtered_body_pairs']};filtered.update(collision_disabled_joint_neighbors(stage));grouped={} + for path in profile['collision_enabled_paths']: + p=stage.GetPrimAtPath(path);owner=p + while owner and not owner.HasAPI(UsdPhysics.RigidBodyAPI):owner=owner.GetParent() + if not owner:raise ValueError(path) + mesh,_,_=collider_mesh(p);mesh.apply_transform(np.linalg.inv(matrix(cache.GetLocalToWorldTransform(owner)))@matrix(cache.GetLocalToWorldTransform(p)));grouped.setdefault(str(owner.GetPath()),[]).append(mesh) + from wrist_upright import camera_mount + mounts={h:camera_mount(stage,h) for h in ['l','r']} + bodies=[Body(p,trimesh.util.concatenate(ms),metadata={'link':p}) for p,ms in grouped.items()] + def check(fk,root): + for hand,mount in mounts.items(): + rotation=fk[root+'/gripper_'+hand+'_base_link'][:3,:3] + if (rotation@mount[:3,3])[2]<.010 or (rotation@mount[:3,1])[2]<.25:return False,dict(reason='wrist_not_upright',hand=hand) + for body in bodies:body.update(fk[body.name.replace('/World/Robot',root,1)]) + for i,a in enumerate(bodies): + for b in bodies[i+1:]: + if tuple(sorted((a.name,b.name))) in filtered or not broad_overlap(a,b):continue + if contact(a,b,containment=True) is not None:return False,dict(a=a.name,b=b.name) + return True,None + check.bodies=bodies + return check diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/navigation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/navigation.py new file mode 100644 index 0000000000000000000000000000000000000000..974c30a4a7d77370753c3a95ee86d9f96ecff76a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/navigation.py @@ -0,0 +1,132 @@ +"""Planar A* with collision-checked shortcutting; nonholonomic stop-turn-drive execution.""" +import heapq,math +import numpy as np + +def clear_segment(a,b,obstacles,radius=.30): + a=np.asarray(a);b=np.asarray(b);delta=b-a + for lo,hi in obstacles: + lo=np.asarray(lo)-radius;hi=np.asarray(hi)+radius;t0,t1=0.,1. + for k in range(2): + if abs(delta[k])<1e-12: + if a[k]hi[k]:t0,t1=1.,0.;break + else: + x,y=sorted(((lo[k]-a[k])/delta[k],(hi[k]-a[k])/delta[k]));t0=max(t0,x);t1=min(t1,y) + if t0<=t1:return False + return True + +def plan(start,goal,obstacles,radius=.30,resolution=.05): + start=np.asarray(start);goal=np.asarray(goal) + if clear_segment(start,goal,obstacles,radius):return [start.tolist(),goal.tolist()] + origin=np.minimum(start,goal)-3.;maximum=np.maximum(start,goal)+3.;shape=np.ceil((maximum-origin)/resolution).astype(int) + def point(n):return origin+np.asarray(n)*resolution + def cell(p):return tuple(np.rint((p-origin)/resolution).astype(int)) + s,g=cell(start),cell(goal) + if not clear_segment(start,point(s),obstacles,radius) or not clear_segment(point(g),goal,obstacles,radius):raise ValueError('Endpoint inside inflated obstacle') + queue=[(0.,s)];cost={s:0.};parent={};done=set() + while queue: + _,n=heapq.heappop(queue) + if n in done:continue + done.add(n) + if n==g:break + for dx,dy in [(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)]: + m=(n[0]+dx,n[1]+dy) + if any(m[k]<0 or m[k]>shape[k] for k in range(2)):continue + v=cost[n]+math.hypot(dx,dy)*resolution + if v>=cost.get(m,float('inf')):continue + if not clear_segment(point(n),point(m),obstacles,radius):continue + cost[m]=v;parent[m]=n;heapq.heappush(queue,(v+np.linalg.norm(point(m)-goal),m)) + if g not in done:raise ValueError('No clearance-safe planar path') + nodes=[g] + while nodes[-1]!=s:nodes.append(parent[nodes[-1]]) + route=[start.tolist()]+[point(n).tolist() for n in reversed(nodes)]+[goal.tolist()] + simple=[route[0]];i=0 + while ii+1 and not clear_segment(route[i],route[j],obstacles,radius):j-=1 + simple.append(route[j]);i=j + return simple + +def angle_delta(target,current):return math.atan2(math.sin(target-current),math.cos(target-current)) + +def motion_primitives(route, initial_yaw, final_yaw): + """Choose forward/reverse per straight leg to minimize total stationary turning.""" + points=[np.asarray(x,dtype=float) for x in route] + legs=[(a,b) for a,b in zip(points,points[1:]) if np.linalg.norm(b-a)>1e-6] + if not legs:return [dict(kind='turn',goal=[*points[-1],float(final_yaw)])] + headings=[math.atan2(*(b-a)[::-1]) for a,b in legs] + states=[(0.,initial_yaw,[])] + for heading in headings: + next_states=[] + for sign in (1,-1): + yaw=angle_delta(heading+(math.pi if sign<0 else 0),0) + best=min(states,key=lambda x:x[0]+abs(angle_delta(yaw,x[1]))) + next_states.append((best[0]+abs(angle_delta(yaw,best[1])),yaw,best[2]+[(yaw,sign)])) + states=next_states + best=min(states,key=lambda x:x[0]+abs(angle_delta(final_yaw,x[1]))) + primitives=[] + for (a,b),(yaw,sign) in zip(legs,best[2]): + primitives.extend([dict(kind='turn',goal=[*a,float(yaw)]),dict(kind='drive',goal=[*b,float(yaw)],direction=sign)]) + primitives.append(dict(kind='turn',goal=[*points[-1],float(final_yaw)])) + return primitives + +class PrimitiveController: + """Mutually exclusive stationary yaw and body-axis translation, bounded acceleration.""" + def __init__(self,primitive):self.primitive=primitive;self.speed=0.;self.done=False + def step(self,current,dt=1/60): + goal=np.asarray(self.primitive['goal']);yaw=float(current[2]);heading=np.array([math.cos(yaw),math.sin(yaw)]) + if self.primitive['kind']=='turn': + error=angle_delta(goal[2],yaw);desired=math.copysign(min(.25,max(.02,1.5*abs(error))),error) if abs(error)>.0015 else 0. + self.speed+=float(np.clip(desired-self.speed,-.3*dt,.3*dt));self.done=abs(error)<.003 and abs(self.speed)<.001 + return np.array([0.,0.,self.speed]) + error=goal[:2]-np.asarray(current[:2]);along=float(error@heading);lateral=float(error@np.array([-heading[1],heading[0]])) + if abs(lateral)>.015:raise RuntimeError(f'Straight drive lateral tracking error {lateral:.4f} m; replan instead of strafing') + if abs(angle_delta(goal[2],yaw))>.01:raise RuntimeError('Straight drive heading drift; stop and replan') + desired=math.copysign(min(.18,max(.008,min(1.5*abs(along),.8*math.sqrt(2*.12*abs(along))))),along) if abs(along)>.0015 else 0. + self.speed+=float(np.clip(desired-self.speed,-.12*dt,.12*dt));self.done=abs(along)<.003 and abs(self.speed)<.001 + return np.r_[heading*self.speed,0.] + +def command(current,goal,previous,dt=1/60): + """Stationary manipulation hold only. Never correct XY with a lateral command.""" + if np.linalg.norm(np.asarray(goal[:2])-current[:2])>.02:raise RuntimeError('Base drifted during stationary manipulation; navigation must be replanned') + if abs(angle_delta(goal[2],current[2]))>.02:raise RuntimeError('Base heading drifted during stationary manipulation') + return np.zeros(3) + + +def plan_release_polygons(start,goal,shapes,floors,radius=.34,resolution=.05): + """Use released per-mesh polygons and room floor, not a whole-group AABB.""" + from shapely.geometry import Polygon,LineString,Point + from shapely.ops import unary_union + from shapely.prepared import prep + blocked=unary_union([Polygon(s).buffer(0) for s in shapes]).buffer(radius) + free=unary_union([Polygon(f).buffer(0) for f in floors]).buffer(-radius).difference(blocked) + check=prep(free) + def clear(a,b):return check.covers(LineString([a,b])) if np.linalg.norm(np.asarray(a)-b)>1e-10 else check.covers(Point(a)) + start=np.asarray(start);goal=np.asarray(goal) + if not clear(start,start) or not clear(goal,goal):raise ValueError('Endpoint outside released collision-free floor') + if clear(start,goal):return [start.tolist(),goal.tolist()] + origin=np.array(free.bounds[:2]);shape=np.ceil((np.array(free.bounds[2:])-origin)/resolution).astype(int) + def point(n):return origin+np.asarray(n)*resolution + def cell(p):return tuple(np.rint((p-origin)/resolution).astype(int)) + source,target=cell(start),cell(goal) + if not clear(start,point(source)) or not clear(point(target),goal):raise ValueError('Endpoint grid connector blocked') + queue=[(0.,source)];cost={source:0.};parent={};done=set() + while queue: + _,n=heapq.heappop(queue) + if n in done:continue + done.add(n) + if n==target:break + for dx,dy in [(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)]: + m=(n[0]+dx,n[1]+dy) + if any(m[k]<0 or m[k]>shape[k] for k in range(2)):continue + value=cost[n]+math.hypot(dx,dy)*resolution + if value>=cost.get(m,float('inf')) or not clear(point(n),point(m)):continue + cost[m]=value;parent[m]=n;heapq.heappush(queue,(value+np.linalg.norm(point(m)-goal),m)) + if target not in done:raise ValueError('No route in released free-floor polygons') + nodes=[target] + while nodes[-1]!=source:nodes.append(parent[nodes[-1]]) + route=[start.tolist()]+[point(n).tolist() for n in reversed(nodes)]+[goal.tolist()];simple=[route[0]];i=0 + while ii+1 and not clear(route[i],route[j]):j-=1 + simple.append(route[j]);i=j + return simple diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..16e80f9d57784e3aa5cd5a3b5593dfe191614a5b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +"""Check one staged multi-item episode with the official LeRobot reader.""" +import argparse +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +CAMERAS = { + "observation.images.top_head": (3, 800, 1280), + "observation.images.hand_left": (3, 480, 848), + "observation.images.hand_right": (3, 480, 848), +} + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + info = json.loads((args.dataset / "meta/info.json").read_text()) + expected = int(info["total_frames"]) + parquet_path = args.dataset / "data/chunk-000/episode_000000.parquet" + table = pq.read_table(parquet_path) + dataset = LeRobotDataset(f"local/shelf_to_table_multi10_stable_layout_{args.layout:02d}", + root=args.dataset, video_backend="pyav") + indices = sorted(set((0, expected // 2, expected - 1))) + samples, failures = [], [] + for index in indices: + item = dataset[index] + action_expected = np.asarray(table["action"][index].as_py(), dtype=np.float32) + state_expected = np.asarray(table["observation.state"][index].as_py(), dtype=np.float32) + action_actual = item["action"].detach().cpu().numpy() + state_actual = item["observation.state"].detach().cpu().numpy() + scalars = { + "frame_index": int(item["frame_index"]) == int(table["frame_index"][index].as_py()), + "episode_index": int(item["episode_index"]) == 0, + "source_control_tick": int(item["source_control_tick"]) == int(table["source_control_tick"][index].as_py()), + "item_index": int(item["item_index"]) == int(table["item_index"][index].as_py()), + "phase_index": int(item["phase_index"]) == int(table["phase_index"][index].as_py()), + "timestamp": abs(float(item["timestamp"]) - float(table["timestamp"][index].as_py())) <= 1e-6, + "simulator_time_s": abs(float(item["simulator_time_s"]) - float(table["simulator_time_s"][index].as_py())) <= 2e-6, + } + images = {} + for key, shape in CAMERAS.items(): + value = item[key] + passed = (tuple(value.shape) == shape and bool(torch.isfinite(value).all()) + and float(value.min()) >= 0.0 and float(value.max()) <= 1.0) + images[key] = {"passed": passed, "shape": list(value.shape), + "minimum": float(value.min()), "maximum": float(value.max()), + "mean": float(value.mean()), "standard_deviation": float(value.std())} + passed = (np.array_equal(action_actual, action_expected) and np.array_equal(state_actual, state_expected) + and all(scalars.values()) and all(row["passed"] for row in images.values()) + and bool(item["task"])) + row = {"index": index, "passed": passed, "action_shape": list(action_actual.shape), + "state_shape": list(state_actual.shape), "scalar_alignment": scalars, + "task": item["task"], "images": images} + samples.append(row) + if not passed: + failures.append(row) + passed = (len(dataset) == expected == len(table) and dataset.fps == 20 and not failures + and samples[0]["action_shape"] == [22] and samples[0]["state_shape"] == [23]) + report = {"schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": datetime.now(timezone.utc).isoformat(), "layout_id": args.layout, + "passed": passed, "dataset": str(args.dataset), + "reader": "lerobot LeRobotDataset(video_backend='pyav')", + "dataset_length": len(dataset), "parquet_rows": len(table), + "metadata_frames": expected, "fps": dataset.fps, "sample_indices": indices, + "samples": samples, "failures": failures, "parquet_sha256": sha(parquet_path), + "note": "First/middle/last rows and all three images decoded through the official reader; full sequential decode is reused by exact video hashes."} + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps({"output": str(args.output), "passed": passed}, indent=2)) + raise SystemExit(0 if passed else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/parallel_world_audit.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/parallel_world_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..727d57ad220f91d4ea9c9e6049b06ff6b51524c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/parallel_world_audit.py @@ -0,0 +1,57 @@ +"""Disjoint control-tick shards; every interval retains its original next tick.""" +import sys,os,time,json,subprocess,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);out=case/'rollout' +from task_collision_scope import POLICY +from multi_validation import audit_ranges,items +shard_count=int(os.environ.get('CHAIN_WORLD_AUDIT_SHARDS','4'));assert 4<=shard_count<=16 +job=json.loads((case/'job.json').read_text());requested=items(job) +start=time.monotonic();z=np.load(out/'recording_60hz.npz',allow_pickle=False);n=len(z['q']);ranges=audit_ranges(z,requested,shard_count,16);shard_count=len(ranges);processes=[];logs=[] +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +try: + for i,(a,b,item_index) in enumerate(ranges): + for name in [f'WORLD_COLLISION_AUDIT.part_{i}.json',f'WORLD_AUDIT_PROGRESS.part_{i}.json']: + p=out/name + if p.exists():p.rename(out/(name+'.previous_'+str(time.time_ns()))) + env=dict(os.environ,CHAIN_AUDIT_SHARD=str(i),CHAIN_AUDIT_START=str(a),CHAIN_AUDIT_END=str(b),CHAIN_AUDIT_ITEM=str(item_index),OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + log=(out/f'world_shard_{i}.log').open('w');logs.append(log);processes.append(subprocess.Popen([sys.executable,str(W/'audit_geometry.py'),str(case),'world'],env=env,stdout=log,stderr=subprocess.STDOUT)) + while any(p.poll() is None for p in processes): + if time.monotonic()-start>1050:raise TimeoutError('World audit exceeded 17.5 minutes; review required, never auto-pass') + checked=0 + for i,(a,b,item_index) in enumerate(ranges): + p=out/f'WORLD_COLLISION_AUDIT.part_{i}.json';progress=out/f'WORLD_AUDIT_PROGRESS.part_{i}.json' + if p.exists(): + part=json.loads(p.read_text());checked+=len(part['checked_frame_indices']) + if part['geometry_status']=='REJECT' and part['hard_failures']: + write(out/'WORLD_COLLISION_AUDIT.json',dict(schema='shelf_multi10.parallel_task_collision_summary.v2',geometry_status='REJECT',collision_scope=POLICY,requested_targets=[x['target'] for x in requested],all_scene_pairs_checked=False,complete_trajectory_checked=False,early_rejection=True,hard_failures=part['hard_failures'],unresolved_review=part['unresolved_review'],elapsed_seconds=time.monotonic()-start,source_shard=i,source_item_index=item_index)) + print('Early rejected by physical collision witness in shard',i,flush=True);sys.exit(0) + elif progress.exists():checked+=max(0,min(int(b-a),json.loads(progress.read_text())['frame']-int(a)+1)) + write(out/'WORLD_AUDIT_PROGRESS.json',dict(frame=checked,total=n,updated=time.time(),parallel_shards=shard_count,elapsed_seconds=time.monotonic()-start)) + time.sleep(2) + results=[] + for i,p in enumerate(processes): + if p.returncode:raise RuntimeError(f'Collision shard {i} failed; see world_shard_{i}.log') + results.append(json.loads((out/f'WORLD_COLLISION_AUDIT.part_{i}.json').read_text())) + failures=[];review=[];contacts=[];covered=[] + for i,r in enumerate(results): + a,b,item_index=ranges[i] + indices=r['checked_frame_indices'];assert len(set(indices))==len(indices) + assert all(int(a)<=x=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + retreat_goal=goal@invheld;retreat_goal[2,3]+=.15;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_multi.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_multi.py new file mode 100644 index 0000000000000000000000000000000000000000..abeb8466de5cff5a67266842abd9d9163e868c48 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_multi.py @@ -0,0 +1,201 @@ +"""Prepare one isolated multi-item shelf/tray case from accepted replay sources.""" +import sys,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot +from scipy.spatial import ConvexHull +from pxr import Usd,UsdGeom,UsdPhysics,UsdUtils,Sdf,Gf + +W=Path(__file__).resolve().parent; R=W.parents[1] +OLD=R/'work/shelf_to_table50_20261001' +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop'),str(OLD)] +from shapely.geometry import Polygon +from shapely import affinity +from shapely.ops import unary_union +from place_tray_layout import scene_geometry,mesh_world_points +from geometry_transfer import registration,board_path + +def dump(p,d): p.write_text(json.dumps(d,indent=2)+'\n') +def matrix(stage,path): return np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(stage.GetPrimAtPath(path))).T +def set_matrix(stage,path,m): + p=stage.GetPrimAtPath(path); pm=np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(p.GetParent())).T + local=np.linalg.inv(pm)@m; x=UsdGeom.Xformable(p); x.ClearXformOpOrder(); x.AddTransformOp().Set(Gf.Matrix4d(local.T.tolist())) +def robot_matrix(spec): + m=np.eye(4); m[:3,:3]=Rot.from_quat(np.asarray(spec['robot']['base_wxyz'])[[1,2,3,0]]).as_matrix(); m[:3,3]=spec['robot']['base_xyz_m']; return m +def control_payload(source,names): + targets=[]; drive={} + with (source/'runtime_control_readback.jsonl').open() as stream: + for line in stream: + row=json.loads(line); by={r['joint']:r for r in row['rows']}; targets.append([by[n]['live_position_target_rad_or_m'] for n in names]) + if not drive: drive={n:by[n]['live_stage_drive_attributes'] for n in names} + q=np.asarray(targets); q[:,18:20]=np.clip(q[:,18:20],0,np.pi/4); return q,drive +def bounds(stage,path): + pts=mesh_world_points(stage,stage.GetPrimAtPath(path)); return pts.min(0),pts.max(0) +def intersects(a,b,pad=.012): + return all(a[0][k]-pad < b[1][k] and b[0][k]-pad < a[1][k] for k in range(3)) +DRINKS={'starbucks','coke_c3','sprite_c1','gatorade_m','gatorade_t','good2grow','grapefruit_can','cayman_jacked'} +def assign_slots(items,tray_polygon,place_xy): + """Search reserved, non-overlapping upright placements back-to-front.""" + pts=np.asarray(tray_polygon); floor=Polygon(pts).buffer(-.008); center=np.asarray(floor.centroid.coords[0]); cov=np.cov((pts-center).T); vals,vecs=np.linalg.eigh(cov); u=vecs[:,np.argmax(vals)]; v=np.array([-u[1],u[0]]) + if np.dot(np.asarray(place_xy)-center,v)<0: v=-v + u=np.array([v[1],-v[0]]) + for item in items: + local=np.load(item['controls_file'])['target_local'][:,:2]; item['_poly']=Polygon(local[ConvexHull(local).vertices]) + base_yaw=float(math.atan2(u[1],u[0])); grid_u=[-.11,-.075,-.04,0,.04,.075,.11]; grid_v=[-.06,-.03,0,.03,.06] + def candidates(item): + rows=[] + for vv in grid_v: + for uu in grid_u: + xy=center+uu*u+vv*v + yaws=[item['accepted_landing_yaw_rad'],item['accepted_landing_yaw_rad']+math.pi] if 'accepted_landing_yaw_rad' in item else [base_yaw,base_yaw+math.pi/2] + for yaw in yaws: + p=affinity.translate(affinity.rotate(item['_poly'],yaw*180/math.pi,origin=(0,0)),xy[0],xy[1]) + if floor.covers(p): rows.append((vv,abs(uu),uu,xy,yaw,p)) + return sorted(rows,key=lambda x:(x[0],x[1])) + chosen=[] + def search(i,last_v): + if i==len(items): return True + for vv,_,uu,xy,yaw,p in candidates(items[i]): + if vv+1e-8 str: + h = hashlib.sha256() + with path.open("rb") as f: + for block in iter(lambda: f.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main() -> None: + records = [] + for case_id in range(1, 11): + src = OLD / "cases" / f"layout_{case_id:02d}" + dst = NEW / "cases" / f"layout_{case_id:02d}" + if dst.exists() and any(dst.iterdir()): + raise RuntimeError(f"refusing to overwrite nonempty {dst}") + dst.mkdir(parents=True, exist_ok=True) + + copied = [] + for name in INPUT_NAMES: + shutil.copy2(src / name, dst / name) + copied.append(name) + for p in sorted(src.glob("controls*.npz")): + shutil.copy2(p, dst / p.name) + copied.append(p.name) + + job = json.loads((src / "job.json").read_text()) + if case_id == 4: + # The first three completed and settled in one physical prefix; + # item 3 Pringles failed to lift. Keep it as a shelf distractor. + job["items"] = job["items"][:3] + job["tray_slots"] = job["tray_slots"][:3] + old_s, new_s = str(OLD), str(NEW) + job = json.loads(json.dumps(job).replace(old_s, new_s)) + job["status"] = "PREPARING" + job["attempt_index"] = 1 + (dst / "job.json").write_text(json.dumps(job, indent=2) + "\n") + + # The physical layer sublayers the case-local authored scene/profile. + physical = (dst / "scene_physical.usda").read_text().replace(old_s, new_s) + (dst / "scene_physical.usda").write_text(physical) + + status = { + "status": "PREPARING", + "phase": "fresh_initial_inputs", + "item_index": None, + "completed_objects": [], + "control_ticks": 0, + "source_case": str(src), + "runtime_state_reused": False, + } + (dst / "STATUS.json").write_text(json.dumps(status, indent=2) + "\n") + copied += ["job.json", "STATUS.json"] + records.append({ + "case_id": case_id, + "source_layout_id": job["source_layout_id"], + "item_count": len(job["items"]), + "products": [x["product"] for x in job["items"]], + "files": {name: sha(dst / name) for name in sorted(copied)}, + }) + + combos_path = NEW / "COMBINATIONS.json" + combos = json.loads(combos_path.read_text()) + for case in combos["cases"]: + if case["case_id"] == 4: + case["products"] = [p for p in case["products"] if p["product"] != "pring_bbq"] + combos["stable_retry_changes"] = { + "layout_03": {"inactive_nonrequested_prims": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/Tier2Items/skit_littles", + ]}, + "layout_04": {"item_count": 3, "removed_requested_item": "pring_bbq", + "reason": "preserve three physically successful items; fourth pickup did not lift"}, + "layout_09": {"inactive_nonrequested_prims": ["/World/Assets/Tier2Items/oreo_gf"]}, + } + combos_path.write_text(json.dumps(combos, indent=2) + "\n") + manifest = { + "schema": "shelf_to_table_multi10.stable_input_manifest.v1", + "old_root_read_only": str(OLD), + "new_root": str(NEW), + "copy_policy": "initial inputs only; no rollout/checkpoint/live held transform reused", + "counts": {"cases": 10, "triples": 7, "quads": 3, "items": 33}, + "cases": records, + } + (NEW / "PREPARED_INPUT_MANIFEST.json").write_text(json.dumps(manifest, indent=2) + "\n") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_visual_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_visual_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..52d4e8c646a17dcbf5b9619f47bc10629aeef344 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/prepare_visual_qa.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +"""Create read-only all-view startup and full-phase evidence panels.""" +import hashlib +import json +import sys +from pathlib import Path + +import av +import numpy as np +from PIL import Image, ImageDraw + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def selected_frames(path, indices): + wanted = set(indices) + result = {} + with av.open(str(path)) as container: + for index, frame in enumerate(container.decode(video=0)): + if index in wanted: + result[index] = frame.to_image().convert("RGB") + if len(result) == len(wanted): + break + if set(result) != wanted: + raise RuntimeError(f"{path}: missing selected frames {sorted(wanted - set(result))}") + return result + + +def panel(images, frames, labels, path, title): + cell_w, cell_h, label_h = 256, 160, 38 + canvas = Image.new("RGB", (cell_w * len(frames), 28 + len(CAMERAS) * (cell_h + label_h)), "black") + draw = ImageDraw.Draw(canvas) + draw.text((5, 5), title, fill="white") + for row, camera in enumerate(CAMERAS): + for column, (frame, label) in enumerate(zip(frames, labels)): + image = images[camera][frame].copy() + image.thumbnail((cell_w, cell_h)) + x = column * cell_w + (cell_w - image.width) // 2 + y0 = 28 + row * (cell_h + label_h) + canvas.paste(image, (x, y0 + label_h)) + draw.text((column * cell_w + 3, y0 + 2), f"{camera} f{frame}\n{label[:28]}", fill="white") + canvas.save(path, quality=92) + + +def main(): + case = Path(sys.argv[1]).resolve() + out = case / "rollout" + job = json.loads((case / "job.json").read_text()) + dataset = W / "staged_datasets" / f"layout_{int(job['layout_id']):02d}" + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + sample_rows = np.arange(2, len(recording["q"]), 3) + sampled_phases = recording["phase"][sample_rows].astype(str) + phase_names = list(dict.fromkeys(sampled_phases.tolist())) + phase_frames = [] + for phase in phase_names: + rows = np.flatnonzero(sampled_phases == phase) + phase_frames.append(int(rows[len(rows) // 2])) + first = list(range(min(10, len(sample_rows)))) + needed = sorted(set(first + phase_frames)) + evidence = W / "qa" / f"layout_{int(job['layout_id']):02d}" / "visual_evidence" + evidence.mkdir(parents=True, exist_ok=True) + decode = json.loads((out / "VIDEO_DECODE_AUDIT.json").read_text()) + images = {} + videos = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + digest = sha(video) + if digest != decode["videos"][camera]["sha256"]: + raise RuntimeError(f"{camera} video differs from fully decoded native evidence") + images[camera] = selected_frames(video, needed) + videos[camera] = {"path": str(video), "sha256": digest} + panels = [] + startup = evidence / "FIRST_0P5S_ALL_VIEWS.jpg" + panel(images, first, [f"startup {i}" for i in first], startup, + "Retained first 0.5 s: all native views") + panels.append({"kind": "startup", "path": str(startup), "sha256": sha(startup), + "frame_indices": first}) + for page, begin in enumerate(range(0, len(phase_frames), 12)): + frames = phase_frames[begin:begin + 12] + labels = phase_names[begin:begin + 12] + path = evidence / f"FULL_PHASE_ALL_VIEWS_{page + 1:02d}.jpg" + panel(images, frames, labels, path, f"Full continuous episode phase midpoints page {page + 1}") + panels.append({"kind": "phase", "path": str(path), "sha256": sha(path), + "frame_indices": frames, "phase_names": labels}) + receipt = { + "schema": "ziche.multi10_visual_panels.v1", "layout_id": int(job["layout_id"]), + "accepted": False, "case": str(case), "dataset": str(dataset), + "recording_sha256": sha(out / "recording_60hz.npz"), "videos": videos, + "phase_names": phase_names, "phase_midpoint_frames": phase_frames, "panels": panels, + "scope": "first retained 0.5 seconds and one midpoint from every ordered phase in all three views", + "requires": "independent human review and official LeRobot reader QA", + } + path = evidence / "PANELS.json" + path.write_text(json.dumps(receipt, indent=2) + "\n") + print(path) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/preroll_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/preroll_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..bdc11d2f74ce3271ddbbe2e7e4534807229dffa3 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/preroll_stability.py @@ -0,0 +1,73 @@ +"""Geometry-based startup stability metrics shared by preroll runners/audits.""" +import re +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation + +PASSIVE_RE=re.compile(r'/gripper_[lr]_(?:inner|outer)_link[234]$') +CAMERAS=[('/World/Robot/head_link2','/World/Robot/head_link2/Head_Camera',1280,800), + ('/World/Robot/gripper_l_base_link','/World/Robot/gripper_l_base_link/Left_Camera',848,480), + ('/World/Robot/gripper_r_base_link','/World/Robot/gripper_r_base_link/Right_Camera',848,480)] + +def _Mpose(p): + m=np.eye(4);m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m + +def build_projection_model(stage,link_paths): + cache=UsdGeom.XformCache() + def mw(prim):return np.asarray(cache.GetLocalToWorldTransform(prim),float).T + cameras=[] + for link_path,cam_path,w,h in CAMERAS: + cam=UsdGeom.Camera(stage.GetPrimAtPath(cam_path));cprim=stage.GetPrimAtPath(cam_path) + cameras.append(dict(link_index=link_paths.index(link_path),path=cam_path,width=w,height=h, + relative=np.linalg.inv(mw(stage.GetPrimAtPath(link_path)))@mw(cprim),focal=float(cam.GetFocalLengthAttr().Get()), + horizontal_aperture=float(cam.GetHorizontalApertureAttr().Get()),vertical_aperture=float(cam.GetVerticalApertureAttr().Get()))) + bodies=[] + for i,path in enumerate(link_paths): + if not PASSIVE_RE.search(path):continue + body=stage.GetPrimAtPath(path);bm=mw(body);chunks=[];mesh_paths=[] + for prim in Usd.PrimRange(body): + if not prim.IsA(UsdGeom.Mesh):continue + pts=np.asarray(UsdGeom.Mesh(prim).GetPointsAttr().Get(),float) + if not len(pts):continue + world=(np.c_[pts,np.ones(len(pts))]@mw(prim).T)[:,:3] + chunks.append((np.c_[world,np.ones(len(world))]@np.linalg.inv(bm).T)[:,:3]);mesh_paths.append(str(prim.GetPath())) + local=np.unique(np.round(np.concatenate(chunks),9),axis=0) if chunks else np.empty((0,3)) + bodies.append(dict(index=i,path=path,local_vertices=local,mesh_paths=mesh_paths, + empty_geometry_exclusion=not len(local),descendant_mesh_count=len(mesh_paths))) + driven=[i for i,p in enumerate(link_paths) if not PASSIVE_RE.search(p)] + return dict(cameras=cameras,bodies=bodies,driven_indices=driven) + +def _project(world,cm,c): + loc=(np.c_[world,np.ones(len(world))]@np.linalg.inv(cm).T)[:,:3];depth=-loc[:,2] + uv=np.c_[c['width']/2+(loc[:,0]/depth)*(c['focal']/c['horizontal_aperture'])*c['width'], + c['height']/2-(loc[:,1]/depth)*(c['focal']/c['vertical_aperture'])*c['height']] + valid=(depth>.001)&(uv[:,0]>=0)&(uv[:,0]=0)&(uv[:,1]=2 + if np.any(seen): + span=np.nanmax(masked[:,seen],axis=0)-np.nanmin(masked[:,seen],axis=0) + # The diagonal of each vertex's UV bounding box is a rigorous upper bound + # on every pairwise Euclidean displacement in the window. + mx=float(np.linalg.norm(span,axis=1).max()) + else:mx=0. + overall=max(overall,mx);cams.append(dict(camera=c['path'],max_projected_motion_px_upper_bound=mx, + vertices_visible_at_least_twice=int(seen.sum()))) + records.append(dict(path=b['path'],status='MEASURED',descendant_mesh_count=b['descendant_mesh_count'], + mesh_paths=b['mesh_paths'],unique_mesh_vertex_count=len(local),cameras=cams, + max_projected_motion_px_upper_bound=max(x['max_projected_motion_px_upper_bound'] for x in cams))) + return dict(status='PASSED' if overall= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name in allowed_release_links: + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_arrival_release.v7", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py new file mode 100644 index 0000000000000000000000000000000000000000..1864ef7f799748666a8519fea1c548fb06bb7246 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 2's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_02/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # First try an exact rigid compensation for the measured navigation + # residual: keep the chassis at its current live arrival and apply the + # same SE(2) residual to the source-grounded object goal/slot. This + # preserves the accepted base-to-hand/object relation without asking the + # base controller to repeat an already demonstrated 1.37 mm miss. + nominal = candidates[0] + _, _, _, _, _, _, nominal_goal, nominal_base, nominal_transfer = nominal + residual = live_base @ np.linalg.inv(nominal_base) + compensated_goal = residual @ nominal_goal + compensated_base = live_base.copy() + compensated_transfer = compensated_goal @ np.linalg.inv(accepted_goal) + compensated_yaw = float(math.atan2(compensated_goal[1, 0], compensated_goal[0, 0])) + compensated_x, compensated_y = map(float, compensated_goal[:2, 3]) + # The exact residual-shifted footprint is only about 0.2 mm short of the + # 12 mm Coke clearance. Search a bounded +/-6 mm posture correction while + # holding the base exactly at the measured arrival. `solve` below must + # still find and screen the resulting arm adjustment. + fixed_base_candidates = [] + rotation = Rotation.from_euler("z", compensated_yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for dx in np.arange(-0.006, 0.0061, 0.001): + for dy in np.arange(-0.006, 0.0061, 0.001): + x, y = compensated_x + float(dx), compensated_y + float(dy) + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = compensated_goal.copy() + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + transfer = goal @ np.linalg.inv(accepted_goal) + score = -1.0 + float(np.hypot(dx, dy)) + fixed_base_candidates.append((score, x, y, compensated_yaw, footprint, + gaps, goal, compensated_base.copy(), transfer)) + fixed_base_candidates.sort(key=lambda entry: entry[0]) + candidates = fixed_base_candidates + candidates + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:1]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + # Explicit post-release screen. The placed target is stationary at the + # solved goal while the gripper opens and the hand follows retreat. + from collision_scene import Body, broad_overlap, contact + import trimesh + landed = Body(item["target"], trimesh.convex.convex_hull(vertices)) + landed.update(np.asarray(report["goal"], dtype=float)) + release_environment = checker.environment + [landed] + release_low = np.asarray([b.lo for b in release_environment]) + release_high = np.asarray([b.hi for b in release_environment]) + release_failure = None + allowed_release_contacts = [] + allowed_release_links = { + f"/World/Robot/gripper_{item['hand']}_inner_link4", + f"/World/Robot/gripper_{item['hand']}_outer_link4", + } + def release_check(qq, phase, sample_index, fraction): + nonlocal release_failure + fk = robot.fk(names, qq, base) + okay, detail = pose_check(fk, robot.root) + if not okay: + release_failure = {"kind": "self_or_wrist", "detail": detail}; return False + for body in checker.self_check.bodies: + body.update(fk[body.name.replace('/World/Robot', robot.root, 1)]) + indices = np.flatnonzero(np.all(release_high >= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name in allowed_release_links: + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_arrival_release.v7", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_FIXED_ONE_TMP.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..e008dd529557876d2588ffa3e939c8fdc764ff92 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 2's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_02/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_last_slot.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_last_slot.py new file mode 100644 index 0000000000000000000000000000000000000000..1790ceaddd70b92ae78ec1b4ea2922356980c9f9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case2_last_slot.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +"""Offline full-body search for case 2's final Skittles tray slot.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def pose_matrix(xyz, wxyz): + result = np.eye(4) + result[:3, 3] = xyz + result[:3, :3] = Rotation.from_quat(np.asarray(wxyz)[[1, 2, 3, 0]]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "PREPARED": + raise RuntimeError(f"case2 is no longer PREPARED: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][3] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][3] + data = np.load(item["controls_file"]) + source = pathlib.Path(item["pick_source"]) + recording = np.load(source / "recording_20hz.npz") + names = recording["robot_dof_names"].tolist() + ids = [names.index(name) for name in data["names"]] + target_index = recording["rigid_body_paths"].tolist().index(item["target"]) + hand_path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + hand_index = recording["robot_link_paths"].tolist().index(hand_path) + held = np.linalg.inv(pose_matrix(recording["robot_link_xyz_m"][-1, hand_index], recording["robot_link_wxyz"][-1, hand_index])) @ pose_matrix(recording["prop_xyz_m"][-1, target_index], recording["prop_wxyz"][-1, target_index]) + q = recording["robot_joint_position_rad_or_m"][-1].copy() + q[ids] = data["place_initial"] + q[ids[-2:]] = data["pick"][-1, -2:] + + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + obstacles = [Polygon(prior["placement_slot"]["footprint_xy"]) for prior in job["items"][:3]] + vertices = data["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + accepted_yaw = float(exact["accepted_yaw_rad"]) + minx, miny, maxx, maxy = floor.bounds + geometry = [] + # Include exact accepted orientation and nearby rotations; every candidate + # rigidly rotates/translates the accepted goal/base pair together. + yaw_offsets = [0, math.pi, math.radians(-15), math.radians(15), math.radians(-30), math.radians(30)] + for yaw_offset in yaw_offsets: + yaw = accepted_yaw + yaw_offset + rotated = vertices @ Rotation.from_euler("z", yaw).as_matrix().T + local_hull = Polygon(rotated[ConvexHull(rotated[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local_hull.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(other) for other in obstacles] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = Rotation.from_euler("z", yaw).as_matrix() + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ goal[:3, :3].T)[:, 2].min() + transform = goal @ np.linalg.inv(accepted_goal) + base = transform @ accepted_base + station_shift = float(np.linalg.norm(base[:2, 3] - np.asarray(item["place_base"])[:2, 3])) + goal_shift = float(np.linalg.norm(goal[:2, 3] - accepted_goal[:2, 3])) + geometry.append((station_shift + 0.15 * goal_shift + 0.02 * abs(yaw_offset), x, y, yaw, footprint, gaps, goal, base, transform)) + geometry.sort(key=lambda candidate: candidate[0]) + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + selfcheck = build(stage) + attempts = [] + accepted = None + for rank, candidate in enumerate(geometry[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transform = candidate + object_pose = robot.fk(names, q, base)[robot.root + hand_path.removeprefix("/World/Robot")] @ held + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, held, {}) + if not checker.check(q, base): + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "station_clearance", "detail": checker.last}) + continue + slot = { + "xy": [float(x), float(y)], + "yaw_rad": float(yaw), + "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), + "tolerance_m": 0.001, + } + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": []} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, q, base, object_pose, vertices, item_job, obstacles, selfcheck, world_check=checker) + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "accepted_pair_transform": transform.tolist(), + "geometry": {"tray_inset_m": 0.008, "future_obstacle_gaps_m": [float(g) for g in gaps], "inside": True}, + "full_body_station_clearance": True, + "plan_frames": int(len(plan)), + "retreat_q": retreat.tolist(), + "placement_report": report, + "elapsed_s": time.monotonic() - began, + } + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "passed", "frames": len(plan)}) + break + except Exception as exc: + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "solve", "error": str(exc), "elapsed_s": time.monotonic() - began}) + result = { + "schema": "multi_item.case2_last_slot_repair.v2", + "read_only": True, + "case_status_at_probe": status, + "method": "Rigidly transform the accepted-chain goal and robot station together; preserve the source held transform and place_initial; require tray geometry, stationary full-body/world clearance, and placement.solve full path/world checks.", + "evidence": {"job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "pick_recording": str(source / "recording_20hz.npz")}, + "candidate_count_geometry": len(geometry), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "LAST_SLOT_REPAIR_PROPOSAL_V2.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "candidate_count": len(geometry), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "geometry", "full_body_station_clearance", "plan_frames", "elapsed_s")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bcb4fa1120790b9a0d86ded6172ba9f6f571c0 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 3's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_03" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case3 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_03/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case3_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT03_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..61e450de1472cc5023d95c91edb0a273a555e3ac --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 3's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_03" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus both future reservations. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future_items = job["items"][2:] + futures = [Polygon(entry["placement_slot"]["footprint_xy"]) for entry in future_items] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(future) for future in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [entry["placement_slot"]["footprint_xy"] for entry in future_items]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(value) for value in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case3_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE3_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..16835e1dabdf7dbd9375269696285954ab62a078 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis and abort-retreat qualification for case 3 item 2.""" +import gzip +import json +import math +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +SRC = R / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000556" +OLD = R / "work/shelf_to_table50_20261001/cases/layout_44" +TARGET = "/World/Assets/ShelfItems/gatorade_t" +CAYMAN = "/World/Assets/ShelfItems/cayman_jacked" +BOARD_TOKEN = "shelf_boards_0/Object_4/Object_0" + +sys.path[:0] = [ + str(W), + str(R / "tools/family3"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "code/family3_repair/real2sim/teleop"), +] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from shapely.geometry import LineString, Polygon # noqa: E402 +from shapely.ops import unary_union # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + + +def matrix(pose): + out = np.eye(4) + out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def yaw(mat): + return float(Rotation.from_matrix(mat[:3, :3]).as_euler("xyz")[2]) + + +def angle_error(a, b): + return float(Rotation.from_matrix(a[:3, :3].T @ b[:3, :3]).magnitude()) + + +def parse_contacts(path, first_tick, last_tick): + result = { + "target_board": [], + "target_gripper": [], + "target_cayman": [], + "target_other": [], + "cayman_gripper": [], + } + try: + with gzip.open(path, "rt") as stream: + while True: + try: + line = stream.readline() + except EOFError: + break # Frozen native process has not written the gzip footer. + if not line: + break + row = json.loads(line) + tick = int(row["tick"]) + if tick < first_tick or tick > last_tick: + continue + actors = row.get("actors", []) + if TARGET in actors: + other = actors[1] if actors[0] == TARGET else actors[0] + if BOARD_TOKEN in other: + key = "target_board" + elif "/World/Robot/gripper_r" in other: + key = "target_gripper" + elif other == CAYMAN: + key = "target_cayman" + else: + key = "target_other" + result[key].append((tick, other, row)) + if CAYMAN in actors: + other = actors[1] if actors[0] == CAYMAN else actors[0] + if "/World/Robot/gripper_r" in other: + result["cayman_gripper"].append((tick, other, row)) + except EOFError: + pass + + summary = {} + for key, rows in result.items(): + ticks = sorted({entry[0] for entry in rows}) + impulse = sum(float(np.linalg.norm(entry[2].get("impulse_sum", [0, 0, 0]))) for entry in rows) + summary[key] = { + "row_count": len(rows), + "contact_tick_count": len(ticks), + "first_tick": ticks[0] if ticks else None, + "last_tick": ticks[-1] if ticks else None, + "minimum_separation_m": min((float(entry[2]["min_separation_m"]) for entry in rows), default=None), + "sum_impulse_norm": impulse, + "other_paths": sorted({entry[1] for entry in rows}), + } + return summary + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case3 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + accepted = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + controls = np.load(item["controls_file"]) + + phase_ids = np.flatnonzero((checkpoint["item_index"] == 2) & (checkpoint["phase"] == "item_02_shelf_pick")) + first_tick = int(checkpoint["tick"][phase_ids[0]]) + last_tick = int(checkpoint["tick"][phase_ids[-1]]) + samples = phase_ids[2::3] + names = checkpoint["dof_names"].tolist() + q = checkpoint["q"][-1].astype(float) + base = matrix(checkpoint["base_pose"][-1]) + target_index = checkpoint["target_paths"].tolist().index(TARGET) + target_poses = checkpoint["target_pose_all"][phase_ids, target_index] + target = matrix(checkpoint["target_pose_all"][-1, target_index]) + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + hand = matrix(checkpoint["robot_link_pose"][-1, hand_index]) + held = np.linalg.inv(hand) @ target + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1]) + } + + contacts = parse_contacts(C / "rollout/contacts.jsonl.gz", first_tick, last_tick) + motion = np.linalg.norm(target_poses[:, :3] - target_poses[0, :3], axis=1) + rotation = ( + Rotation.from_quat(target_poses[0, 3:]).inv() * Rotation.from_quat(target_poses[:, 3:]) + ).magnitude() + first_motion = int(np.flatnonzero(motion > 0.001)[0]) + first_rotation = int(np.flatnonzero(rotation > 0.05)[0]) + final_stability_translation = float(np.max(np.linalg.norm(target_poses[-120:, :3] - target_poses[-1, :3], axis=1))) + final_stability_rotation = float( + np.max((Rotation.from_quat(target_poses[-1, 3:]).inv() * Rotation.from_quat(target_poses[-120:, 3:])).magnitude()) + ) + + desired_base = np.asarray(item["pick_base"], dtype=float) + actual_start_base = matrix(checkpoint["base_pose"][phase_ids[0]]) + source_names = source["robot_dof_names"].tolist() + reorder = [names.index(name) for name in source_names] + q_error = checkpoint["q"][samples][:, reorder] - source["robot_joint_position_rad_or_m"] + arm_ids = [source_names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + gripper_ids = [i for i, name in enumerate(source_names) if "gripper_r" in name] + + source_target_index = source["rigid_body_paths"].tolist().index(TARGET) + source_hand_index = source["robot_link_paths"].tolist().index(hand_path) + source_hand_target_distance = float( + np.linalg.norm( + source["robot_link_xyz_m"][-1, source_hand_index] + - source["prop_xyz_m"][-1, source_target_index] + ) + ) + accepted_pick = np.flatnonzero(accepted["phase"] == "shelf_pick") + accepted_hand_index = accepted["robot_link_paths"].tolist().index(hand_path) + accepted_hand_target_distance = float( + np.linalg.norm( + accepted["robot_link_pose"][accepted_pick[-1], accepted_hand_index, :3] + - accepted["target_pose"][accepted_pick[-1], :3] + ) + ) + source_contact = json.loads((SRC / "runtime_contact_readback.json").read_text()) + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + carried_checker = Clearance( + stage, robot, names, item["hand"], TARGET, controls["target_local"], held, prop_poses + ) + carried_checker.deadline = time.monotonic() + 900 + carried_start_clear = bool(carried_checker.check(q, base)) + carried_start_detail = carried_checker.last + + empty_checker = EmptyClearance(stage, robot, names, prop_poses, deadline_s=900) + empty_start_clear = bool(empty_checker.check(q, base)) + empty_start_detail = empty_checker.last + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / "03.json" + release = json.loads(release_path.read_text()) + floor = unary_union([Polygon(points).buffer(0) for points in release["floors"]]).buffer(-0.34) + heading = np.array([math.cos(yaw(base)), math.sin(yaw(base))]) + retreat_checks = [] + for distance in (0.05, 0.10, 0.15, 0.25, 0.35): + endpoint = base.copy() + endpoint[:2, 3] -= distance * heading + floor_clear = bool(floor.covers(LineString([base[:2, 3], endpoint[:2, 3]]))) + geometry_clear = bool(empty_checker.segment(q, q, base, endpoint)) if floor_clear else False + retreat_checks.append({ + "distance_m": distance, + "endpoint_base": endpoint.tolist(), + "floor_corridor_clear": floor_clear, + "robot_world_segment_clear": geometry_clear, + "detail": None if geometry_clear else empty_checker.last, + }) + + current_paths = checkpoint["rigid_body_paths"].tolist() + cayman_i = current_paths.index(CAYMAN) + target_prop_i = current_paths.index(TARGET) + cayman_initial = checkpoint["prop_pose"][0, cayman_i] + target_initial = checkpoint["prop_pose"][0, target_prop_i] + cayman_final = checkpoint["prop_pose"][-1, cayman_i] + + result = { + "schema": "multi_item.case3_item2_carry_boundary.v1", + "read_only": True, + "status_at_probe": status, + "evidence": { + "checkpoint": str(checkpoint_path), + "contacts": str(C / "rollout/contacts.jsonl.gz"), + "source": str(SRC), + "accepted_chain": str(OLD), + "controls": item["controls_file"], + "release_floor": str(release_path), + }, + "pickup_phase": { + "tick_range": [first_tick, last_tick], + "ticks": int(len(phase_ids)), + "source_frames": int(len(source["frame_index"])), + "three_ticks_per_source_frame": bool(len(phase_ids) == 3 * len(source["frame_index"])), + "right_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, arm_ids] ** 2))), + "right_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, arm_ids]))), + "right_gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, gripper_ids] ** 2))), + "right_gripper_tracking_max_rad": float(np.max(np.abs(q_error[:, gripper_ids]))), + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_start_base[:2, 3].tolist(), + "xy_error_m": float(np.linalg.norm(actual_start_base[:2, 3] - desired_base[:2, 3])), + "yaw_error_rad": float(math.atan2(math.sin(yaw(actual_start_base) - yaw(desired_base)), math.cos(yaw(actual_start_base) - yaw(desired_base)))), + }, + "measured_target_outcome": { + "initial_pose": target_poses[0].tolist(), + "frozen_pose": target_poses[-1].tolist(), + "translation_m": (target_poses[-1, :3] - target_poses[0, :3]).tolist(), + "rotation_rad": float(rotation[-1]), + "maximum_center_lift_m": float(np.max(target_poses[:, 2] - target_poses[0, 2])), + "first_translation_over_1mm": {"phase_offset_ticks": first_motion, "tick": int(checkpoint["tick"][phase_ids[first_motion]])}, + "first_rotation_over_0_05rad": {"phase_offset_ticks": first_rotation, "tick": int(checkpoint["tick"][phase_ids[first_rotation]])}, + "frozen_hand_target_center_distance_m": float(np.linalg.norm(hand[:3, 3] - target[:3, 3])), + "source_endpoint_hand_target_center_distance_m": source_hand_target_distance, + "accepted_layout44_endpoint_hand_target_center_distance_m": accepted_hand_target_distance, + "last_120_ticks_translation_range_m": final_stability_translation, + "last_120_ticks_rotation_range_rad": final_stability_rotation, + }, + "physical_contact_timeline": contacts, + "source_contact_evidence": { + "pads": source_contact["pads"], + "source_target_contact_count": source_contact["target_contact_count"], + "current_target_gripper_contact_rows": contacts["target_gripper"]["row_count"], + }, + "distractor_causality": { + "cayman_absent_from_episode556_source": bool(CAYMAN not in source["rigid_body_paths"].tolist()), + "initial_target_cayman_center_distance_m": float(np.linalg.norm(target_initial[:3] - cayman_initial[:3])), + "cayman_translation_by_boundary_m": (cayman_final[:3] - cayman_initial[:3]).tolist(), + "sequence": "right gripper contacts Cayman Jack at tick 20017; Cayman Jack contacts Gatorade at tick 20041; Gatorade begins rotating at tick 20047 and translating >1 mm at tick 20057. The source episode contains no Cayman Jack.", + }, + "planner_collision_assessment": { + "carried_surrogate_clear_at_frozen_start": carried_start_clear, + "carried_surrogate_failure": carried_start_detail, + "empty_robot_clear_at_frozen_start": empty_start_clear, + "empty_robot_failure": empty_start_detail, + "classification": "real_physical_support_contact_not_convex_only", + "reason": "Physics records target/shelf-board contact on every pickup tick through tick 21250. The carry surrogate fails at its initial state because it incorrectly treats the ungrasped, shelf-supported bottle as attached to the hand.", + }, + "continuation_qualification": { + "carry_continuation": None, + "qualified": False, + "reason": "There is no live grasp to preserve. The bottle is tipped 1.581 rad, displaced, resting on the shelf, and 0.474 m from the gripper base. A carry continuation would spoof attachment of an ungrasped target.", + "safe_abort_retreat_only": { + "qualified": bool(empty_start_clear and all(entry["floor_corridor_clear"] and entry["robot_world_segment_clear"] for entry in retreat_checks)), + "motion": "Hold all 34 joint targets (including all gripper commands) fixed and execute one physical reverse drive primitive from the measured base. No target attachment, pose write, reset, or arm command.", + "tested_distances": retreat_checks, + "recommended_distance_m": 0.15, + "semantics": "This only withdraws the empty robot safely and must end the attempt as a physical pickup failure; it does not complete item 2.", + }, + }, + "recommendation": { + "action": "Do not apply a carry repair. End this attempt as a real pickup failure and perform a fresh physical retry from the released scene/prefix policy.", + "scene_prevention": "Use CASE3_FRESH_RETRY_INPUTS.json. Full four-source qualification shows that strict all-world clearance requires removing the three nonrequested blockers Cayman Jack, Oreo, and Skittles from a newly prepared scene; Cayman-only removal is insufficient.", + "pickup_gate": "Require bilateral target/gripper contact and bounded target-to-hand transform at the endpoint. Center lift alone is insufficient because tipping on the shelf produced a false >25 mm lift.", + "safe_state_option": "If the native process must be parked before termination, the qualified 0.15 m constant-posture physical reverse is safe-abort motion only.", + }, + "native_motion_executed": False, + "scene_or_job_modified": False, + } + out = C / "CASE3_ITEM02_CARRY_BOUNDARY_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({ + "out": str(out), + "carry_continuation_qualified": result["continuation_qualification"]["qualified"], + "safe_abort_retreat_qualified": result["continuation_qualification"]["safe_abort_retreat_only"]["qualified"], + "contact_timeline": contacts, + "planner_collision": result["planner_collision_assessment"], + "recommendation": result["recommendation"], + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..d39ed23425852c345f5252db69a5617c1ef81b3f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_03' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_03/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case3_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT03_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..239a690d8ac96b94da435886957077fde9fcffa0 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 4's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_04" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case4 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_04/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case4_item1_fresh_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT04_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..a768edbf209de66516ed5f027c5e3f375c246b6b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +"""Read-only full-path Honey Maid place repair probe from case 4's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_04" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case4 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][2:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:300]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][2:]]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank":rank,"stage":"actual_compact_to_place_bridge","detail":checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_gaps_m": [float(x) for x in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case4_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE4_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py new file mode 100644 index 0000000000000000000000000000000000000000..2f4b5185330b6009131987ac920e152bb4a8560a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Qualify a slot rigidly compensated to case 5's measured station arrival.""" +import hashlib +import json +import math +import os +from pathlib import Path +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), + str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def planar(base): + result = np.eye(4) + yaw = Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2] + result[:3, :3] = Rotation.from_euler("z", yaw).as_matrix() + result[:3, 3] = base[:3, 3] + return result + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 1 and status["control_ticks"] == 16072 +job = json.loads((C / "job.json").read_text()) +item = job["items"][1] +source_path = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" +source = json.loads(source_path.read_text())["accepted"] +checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = checkpoint["q"][-1].astype(float) +names = checkpoint["dof_names"].tolist() +actual_base = matrix(checkpoint["base_pose"][-1]) +hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") +target_index = checkpoint["target_paths"].tolist().index(item["target"]) +target = matrix(checkpoint["target_pose_all"][-1, target_index]) +held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ target +controls = np.load(item["controls_file"]) +ids = [names.index(name) for name in controls["names"].tolist()] +exact_q = q.copy() +exact_q[ids] = np.asarray(source["place_initial20"]) +grippers = [index for index, name in enumerate(names) if "gripper_" in name] +exact_q[grippers] = q[grippers] + +desired_base = np.asarray(source["place_base"], dtype=float) +transfer = planar(actual_base) @ np.linalg.inv(planar(desired_base)) +center = transfer @ np.r_[source["slot"]["xy"], 0.0, 1.0] +footprint = [] +for point in source["slot"]["footprint_xy"]: + footprint.append((transfer @ np.r_[point, 0.0, 1.0])[:2].tolist()) +dyaw = Rotation.from_matrix(transfer[:3, :3]).as_euler("xyz")[2] +slot = {"xy": center[:2].tolist(), "yaw_rad": float(source["slot"]["yaw_rad"] + dyaw), + "footprint_xy": footprint, "tolerance_m": 0.001} + +stage = Usd.Stage.Open(job["scene"]) +robot = Robot(ROBOT) +robot.bodies = [] +pose_check = build(stage) +props = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} +checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) +checker.deadline = time.monotonic() + 1800 +assert checker.segment(q, exact_q, actual_base, actual_base), checker.last + +item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) +item0_local = np.load(job["items"][0]["controls_file"])["target_local"] +item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] +obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] +floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) +current_poly = Polygon(footprint) +future = [Polygon(entry["placement_slot"]["footprint_xy"]) for entry in job["items"][2:]] +gaps = [current_poly.distance(obstacles[0])] + [current_poly.distance(other) for other in future] +assert floor.covers(current_poly) and min(gaps) > 0.012, (gaps, floor.covers(current_poly)) + +object_pose = robot.fk(names, exact_q, actual_base)[robot.root + "/gripper_r_base_link"] @ held +item_job = {**job, **item, "placement_slot": slot, "place_base": actual_base.tolist(), + "destination_xyz": [float(center[0]), float(center[1]), float(job["tray_floor_z"])], + "reserved_tray_footprints": [entry["placement_slot"]["footprint_xy"] for entry in job["items"][2:]], + "require_release_sweep": True} +release = ReleaseSweep(stage, robot, names, item["hand"], item["target"], controls["target_local"], props, + item["place_source"], pose_check=pose_check, deadline_s=1800) +planned, retreat, report = solve(robot, names, exact_q, actual_base, object_pose, controls["target_local"], item_job, + obstacles, pose_check, world_check=checker, release_sweep=release) +result = {"schema": "multi_item.case5_item1_actual_arrival_place.v1", "read_only": True, + "status_tick": status["control_ticks"], "source_proposal": str(source_path), + "source_proposal_sha256": hashlib.sha256(source_path.read_bytes()).hexdigest(), + "measured_base": actual_base.tolist(), "desired_base": desired_base.tolist(), + "actual_from_desired_planar_transform": transfer.tolist(), "slot": slot, + "destination_xyz": item_job["destination_xyz"], "geometry": {"inside": True, "gaps_m": gaps}, + "place_initial20": source["place_initial20"], "bridge_clear": True, + "placement_frames": len(planned), "retreat_q": retreat.tolist(), "placement_report": report, + "full_world_checks": True, "native_validation_required": True} +output = W / "diagnostics/LAYOUT05_ITEM01_ACTUAL_ARRIVAL_PLACE_PROPOSAL.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest(), + "slot": slot, "gaps_m": gaps, "frames": len(planned), "release": report["release_sweep"]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py new file mode 100644 index 0000000000000000000000000000000000000000..3e4ace8b26fced60ce8c04056f4d054f5e6e8ac7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""Read-only carried-route and endpoint qualification for case 5 station prebias.""" +import hashlib +import json +import math +import os +from pathlib import Path +import sys +import time + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance, plan as plan_carry + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def yaw(base): + return float(Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2]) + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 1 and status["control_ticks"] == 14645 +job = json.loads((C / "job.json").read_text()) +item = job["items"][1] +proposal_path = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" +proposal = json.loads(proposal_path.read_text())["accepted"] +checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = checkpoint["q"][-1].astype(float) +names = checkpoint["dof_names"].tolist() +base = matrix(checkpoint["base_pose"][-1]) +hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") +target_index = checkpoint["target_paths"].tolist().index(item["target"]) +target = matrix(checkpoint["target_pose_all"][-1, target_index]) +held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ target +controls = np.load(item["controls_file"]) +ids = [names.index(name) for name in controls["names"].tolist()] +exact_q = q.copy() +exact_q[ids] = np.asarray(proposal["place_initial20"]) +grippers = [index for index, name in enumerate(names) if "gripper_" in name] +exact_q[grippers] = q[grippers] + +old_desired = np.asarray(item["place_base"], dtype=float) +desired = np.asarray(proposal["place_base"], dtype=float) +current = np.r_[base[:2, 3], yaw(base)] +old_endpoint = np.r_[old_desired[:2, 3], yaw(old_desired)] +residual = current - old_endpoint +residual[2] = math.atan2(math.sin(residual[2]), math.cos(residual[2])) +command = desired.copy() +command[:2, 3] -= residual[:2] +command[:3, :3] = Rotation.from_euler("z", yaw(desired) - residual[2]).as_matrix() + +props = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]) +robot = Robot(ROBOT) +robot.bodies = [] +checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) +checker.deadline = time.monotonic() + 900 + +source_layout = int(job.get("source_layout_id", job["layout_id"])) +release = json.loads((Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout:02d}.json").read_text()) +repair_job = {**job, **item, "place_base": command.tolist(), "placement_slot": proposal["slot"], + "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + "reserved_tray_footprints": [future["placement_slot"]["footprint_xy"] for future in job["items"][2:]]} +repair_job.pop("carry_route_seed", None) +compact, primitives, carry_report = plan_carry(stage, robot, names, q, base, item["hand"], item["target"], controls["target_local"], target, props, repair_job, release) +checks = {} +for label, endpoint in (("command", command), ("desired", desired)): + checks[label] = { + "compact_clear": bool(checker.check(compact, endpoint)), + "compact_detail": checker.last, + "exact_clear": bool(checker.check(exact_q, endpoint)), + "exact_detail": checker.last, + "compact_to_exact_clear": bool(checker.segment(compact, exact_q, endpoint, endpoint)), + "segment_detail": checker.last, + } + assert checks[label]["compact_clear"] and checks[label]["exact_clear"] and checks[label]["compact_to_exact_clear"] + +result = { + "schema": "multi_item.case5_item1_station_command_prebias.v1", + "read_only": True, + "status_tick": status["control_ticks"], + "source_proposal": str(proposal_path), + "source_proposal_sha256": hashlib.sha256(proposal_path.read_bytes()).hexdigest(), + "observed_prior_arrival_residual": {"world_xy_m": residual[:2].tolist(), "yaw_rad": float(residual[2])}, + "desired_qualified_base4x4": desired.tolist(), + "command_base4x4_inverse_residual": command.tolist(), + "prediction": "If the measured prior endpoint residual repeats, physical arrival equals desired_qualified_base4x4.", + "checks": checks, + "carry": {"primitive_count": len(primitives), "compact_q": compact.tolist(), "primitives": primitives, "report": carry_report}, + "required_live_arrival_gate": {"translation_m": 0.0005, "yaw_rad": 0.001, "relative_to": "desired_qualified_base4x4", + "then_recompute_live_bridge_and_place": True}, + "no_motion": True, + "native_validation_required": True, +} +output = W / "diagnostics/LAYOUT05_ITEM01_STATION_COMMAND_PREBIAS.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest(), + "residual": result["observed_prior_arrival_residual"], "checks": checks, + "carry_primitives": len(primitives)})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py new file mode 100644 index 0000000000000000000000000000000000000000..e828420f634346f50903f91fa9df70ac6113c495 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [2.9377098028668382] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in [-1.378553647218034]: + for y in [-0.16699245336876373]: + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_DIRECT_OLD_SLOT_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..9a3f4eac7b3b5041ac0addd952bc95a4bed5bc35 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..18e1bac3878737d9e78405875017b16e8352796a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: (np.linalg.norm(np.asarray(entry[1:3])-np.asarray([-1.378553647218034,-0.16699245336876373])), entry[0])) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_TARGETED_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py new file mode 100644 index 0000000000000000000000000000000000000000..bf46cd53cf57bf8b307f451dcd23fdf56df4193b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5 item 2 frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed items 0 and 1, plus future reservations. + obstacles = [] + for completed_index in range(2): + completed_pose = matrix(checkpoint["target_pose_all"][-1, completed_index]) + completed_local = np.load(job["items"][completed_index]["controls_file"])["target_local"] + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles.append(Polygon(completed_world[ConvexHull(completed_world).vertices])) + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(3, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # Recheck the prepared source-grounded slot that passed the case-level + # offline solve, now against the actual two supported objects and live + # held transform. + prepared_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/SLOT_REACHABILITY_PROPOSAL.json" + prepared = json.loads(prepared_path.read_text())["items"][2]["report"] + ps = prepared["slot"] + px, py, pyaw = float(ps["xy"][0]), float(ps["xy"][1]), float(ps["yaw_rad"]) + prot = Rotation.from_euler("z", pyaw).as_matrix() + ppts = vertices @ prot.T + plocal = Polygon(ppts[ConvexHull(ppts[:, :2]).vertices, :2]) + pfoot = Polygon(np.asarray(plocal.exterior.coords)[:-1] + [px, py]) + pgaps = [pfoot.distance(o) for o in obstacles] + [pfoot.distance(f) for f in futures] + pgoal = np.asarray(prepared["goal"], dtype=float) + pbase = np.asarray(item["place_base"], dtype=float) + pbase[2, 3] = live_base[2, 3] + ptransfer = pgoal @ np.linalg.inv(accepted_goal) + if not floor.covers(pfoot) or (pgaps and min(pgaps) <= 0.012): + raise RuntimeError(f"prepared item2 footprint no longer valid: {pgaps}") + candidates = [(-1.0, px, py, pyaw, pfoot, pgaps, pgoal, pbase, ptransfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(3, len(job["items"]))]} + began = time.monotonic() + try: + release_checker = ReleaseSweep(stage, robot, names, item["hand"], item["target"], + vertices, prop_poses, item["place_source"], + pose_check=pose_check, deadline_s=900) + item_job["require_release_sweep"] = True + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, + item_job, obstacles, pose_check, + world_check=checker, release_sweep=release_checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(g) for g in gaps[:2]], "future_item_gaps_m": [float(g) for g in gaps[2:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_sweep": report["release_sweep"], + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item2_release_sweep.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM02_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..8a1f047679fcc11156837f8c3e0465501a9cd48a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_05' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case5_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT05_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..b73c4550633896415531b1eebf95630d2947a5f7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 6's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_06" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case6 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case6_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT06_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe70d23ad4a33d03e02a1d522f4a798fefeb575 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py @@ -0,0 +1,283 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 6 item 2 frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_06" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case6 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed items 0 and 1, plus future reservations. + obstacles = [] + for completed_index in range(2): + completed_pose = matrix(checkpoint["target_pose_all"][-1, completed_index]) + completed_local = np.load(job["items"][completed_index]["controls_file"])["target_local"] + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles.append(Polygon(completed_world[ConvexHull(completed_world).vertices])) + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(3, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # The prior physical attempt reached this same item boundary and its + # source-grounded candidate passed the full place/world probe. Recheck + # that exact candidate first against the new live held transform and the + # two newly measured supported objects instead of spending minutes on + # weaker grid candidates. + old_safe_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/CASE6_ITEM02_SAFE_PLACE_PROPOSAL.json" + old_safe = json.loads(old_safe_path.read_text())["accepted"] + old_slot = old_safe["slot"] + old_x, old_y, old_yaw = float(old_slot["xy"][0]), float(old_slot["xy"][1]), float(old_slot["yaw_rad"]) + old_rotation = Rotation.from_euler("z", old_yaw).as_matrix() + old_points = vertices @ old_rotation.T + old_local = Polygon(old_points[ConvexHull(old_points[:, :2]).vertices, :2]) + old_footprint = Polygon(np.asarray(old_local.exterior.coords)[:-1] + [old_x, old_y]) + old_gaps = [old_footprint.distance(o) for o in obstacles] + [old_footprint.distance(f) for f in futures] + old_goal = np.asarray(old_safe["placement_report"]["goal"], dtype=float) + old_base = np.asarray(old_safe["place_base"], dtype=float) + old_base[2, 3] = live_base[2, 3] + old_transfer = old_goal @ np.linalg.inv(accepted_goal) + if floor.covers(old_footprint) and (not old_gaps or min(old_gaps) > 0.012): + candidates = [(-1.0, old_x, old_y, old_yaw, old_footprint, old_gaps, + old_goal, old_base, old_transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(3, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + # Explicit post-release screen. The placed target is stationary at the + # solved goal while the gripper opens and the hand follows retreat. + from collision_scene import Body, broad_overlap, contact + import trimesh + landed = Body(item["target"], trimesh.convex.convex_hull(vertices)) + landed.update(np.asarray(report["goal"], dtype=float)) + release_environment = checker.environment + [landed] + release_low = np.asarray([b.lo for b in release_environment]) + release_high = np.asarray([b.hi for b in release_environment]) + release_failure = None + allowed_release_contacts = [] + assigned_gripper_prefix = f"/World/Robot/gripper_{item['hand']}_" + def release_check(qq, phase, sample_index, fraction): + nonlocal release_failure + fk = robot.fk(names, qq, base) + okay, detail = pose_check(fk, robot.root) + if not okay: + release_failure = {"kind": "self_or_wrist", "detail": detail}; return False + for body in checker.self_check.bodies: + body.update(fk[body.name.replace('/World/Robot', robot.root, 1)]) + indices = np.flatnonzero(np.all(release_high >= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name.startswith(assigned_gripper_prefix): + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + if release_samples % 10 == 0: print(f"RELEASE_PROGRESS {release_samples} {phase} {sample_index}/{count}", flush=True) + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(g) for g in gaps[:2]], "future_item_gaps_m": [float(g) for g in gaps[2:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case6_item2_arrival_release.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT06_ITEM02_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..88885c1be4c38a314deff31ee490d1678c14cf7a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 7's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_07" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case7 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_07/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case7_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT07_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py new file mode 100644 index 0000000000000000000000000000000000000000..18de7747a0abb899896d81dbde3816ec003f76ee --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Find a screened carried escape route for case 7's frozen blocked turn.""" +import hashlib, json, math, os, sys, time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def base(x,y,yaw,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',yaw).as_matrix();m[:3,3]=[x,y,z];return m +status=json.load(open(C/'STATUS.json'));assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 and status['control_ticks']==33066 +job=json.load(open(C/'job.json'));item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();b0=mat(cp['base_pose'][-1]);z=b0[2,3] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link");ti=cp['target_paths'].tolist().index(item['target']);target=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@target;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])} +stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];ctrl=np.load(item['controls_file']);check=Clearance(stage,robot,names,item['hand'],item['target'],ctrl['target_local'],held,props);check.deadline=time.monotonic()+900 +s=np.array(status['planning_error'].split('"measured_base": [')[1].split(']')[0].split(','),float);goal=np.array([-1.0663820670686106,1.0637987903151804,-1.5746476816413626]);assert check.check(q,b0),check.last +direct=base(goal[0],goal[1],goal[2],z);direct_clear=check.segment(q,q,b0,direct);direct_detail=check.last +candidates=[] +for signed_distance in [-.03,.03,-.05,.05,-.08,.08,-.10,.10,-.15,.15,-.20,.20]: + heading=np.array([math.cos(s[2]),math.sin(s[2])]);xy=s[:2]+signed_distance*heading;theta=math.atan2(goal[1]-xy[1],goal[0]-xy[0]);poses=[b0,base(xy[0],xy[1],s[2],z),base(xy[0],xy[1],theta,z),base(goal[0],goal[1],theta,z),direct] + ok=True;details=[] + for a,b in zip(poses,poses[1:]): + good=check.segment(q,q,a,b);details.append(check.last) + if not good:ok=False;break + candidates.append({'signed_escape_m':signed_distance,'passed':ok,'connector_yaw':theta,'details':details, + 'primitives':[{'kind':'drive','goal':[float(xy[0]),float(xy[1]),float(s[2])],'direction':1 if signed_distance>0 else -1}, + {'kind':'turn','goal':[float(xy[0]),float(xy[1]),float(theta)]}, + {'kind':'drive','goal':[float(goal[0]),float(goal[1]),float(theta)],'direction':1}, + {'kind':'turn','goal':goal.tolist()}]}) + if ok:break +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v1','read_only':True,'status_tick':33066,'failure_sha256':'eda9b457e975bd02f417c18a0024af7559fdc8ac070eb222d9b55686166c5961','direct_turn_geometry_clear':direct_clear,'direct_detail':direct_detail,'held_transform':held.tolist(),'candidates':candidates,'accepted':next((x for x in candidates if x['passed']),None),'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'direct':direct_clear,'accepted':out['accepted']},indent=2)) +if out['accepted'] is None:raise SystemExit(2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..40e55e47d3fd203def74ca3c7587f624954c7e93 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python3 +"""Screen a within-gate endpoint offset away from the live table contact.""" +import hashlib,json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(x,y,yaw,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',yaw).as_matrix();m[:3,3]=[x,y,z];return m +status=json.load(open(C/'STATUS.json'));assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 and status['control_ticks']==34142 +job=json.load(open(C/'job.json'));item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();start=np.array([-1.0671910047531128,1.0646690130233765,-0.812772274017334]);goal=np.array([-1.0663820670686106,1.0637987903151804,-1.5746476816413626]);z=cp['base_pose'][-1,2] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link");ti=cp['target_paths'].tolist().index(item['target']);target=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@target;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];ctrl=np.load(item['controls_file']);check=Clearance(stage,robot,names,item['hand'],item['target'],ctrl['target_local'],held,props);check.deadline=time.monotonic()+900 +heading=np.array([math.cos(start[2]),math.sin(start[2])]);escape=start[:2]-.03*heading;candidate=goal[:2]+np.array([.00067,.00067]);connector=math.atan2(candidate[1]-escape[1],candidate[0]-escape[0]);states=[start,[*escape,start[2]],[*escape,connector],[*candidate,connector],[*candidate,goal[2]]];primitives=[{'kind':'drive','goal':states[1],'direction':-1},{'kind':'turn','goal':states[2]},{'kind':'drive','goal':states[3],'direction':1},{'kind':'turn','goal':states[4]}];details=[] +for a,b in zip(states,states[1:]): + ok=check.segment(q,q,bm(*a,z),bm(*b,z));details.append(check.last);assert ok,check.last +error=float(np.linalg.norm(candidate-goal[:2]));assert error<.001 +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v2','read_only':True,'status_tick':34142,'failure_sha256':'0abde6995ad418d2f1703eb6061ec1a69adbd63a3f2c0fe19f92f44363a91be8','physical_blocker':{'pair':['/World/Robot/base_link','/World/Assets/CoffeeTable/Mesh'],'latest_min_separation_m':1.6e-6,'impulse_xy_approx':[14.29,14.32],'inferred_away_direction_world_xy':[1,1]},'original_goal':goal.tolist(),'candidate_endpoint':[*candidate,goal[2]],'candidate_xy_error_m':error,'parent_gate_m':.001,'primitives':primitives,'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V2_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'xy_error':error,'primitives':primitives},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..ec30abb613aaa6fd4a72c5e8847b8d75bb2d16b6 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +"""Screen a carried route that finishes rotation before approaching the table-edge endpoint.""" +import hashlib, json, math, os, sys, time +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_07" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance + + +def mat(p): + m = np.eye(4); m[:3, 3] = p[:3]; m[:3, :3] = Rotation.from_quat(p[3:]).as_matrix(); return m + + +def bm(state, z): + m = np.eye(4); m[:3, :3] = Rotation.from_euler("z", state[2]).as_matrix(); m[:3, 3] = [state[0], state[1], z]; return m + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 2 and status["control_ticks"] == 35270 +job = json.loads((C / "job.json").read_text()); item = job["items"][2] +cp = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = cp["q"][-1].astype(float); names = cp["dof_names"].tolist(); z = float(cp["base_pose"][-1, 2]) +start = np.array([-1.0666694641113281, 1.0653468370437622, -0.8118601441383362]) +goal = np.array([-1.0657120670686107, 1.0644687903151804, -1.5746476816413626]) +hi = cp["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") +ti = cp["target_paths"].tolist().index(item["target"]) +target = mat(cp["target_pose_all"][-1, ti]); held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ target +props = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] +ctrl = np.load(item["controls_file"]) +check = Clearance(stage, robot, names, item["hand"], item["target"], ctrl["target_local"], held, props) +check.deadline = time.monotonic() + 900 + +# First retreat along the current heading. Approach the endpoint only after the +# chassis has reached the final yaw at a point 35 mm away along that yaw. +heading = np.array([math.cos(start[2]), math.sin(start[2])]) +escape = start[:2] - 0.035 * heading +final_heading = np.array([math.cos(goal[2]), math.sin(goal[2])]) +staging = goal[:2] - 0.035 * final_heading +connector = math.atan2(staging[1] - escape[1], staging[0] - escape[0]) +states = [start, [*escape, start[2]], [*escape, connector], [*staging, connector], [*staging, goal[2]], goal] +primitives = [ + {"kind": "drive", "goal": states[1], "direction": -1}, + {"kind": "turn", "goal": states[2]}, + {"kind": "drive", "goal": states[3], "direction": 1}, + {"kind": "turn", "goal": states[4]}, + {"kind": "drive", "goal": states[5], "direction": 1}, +] +details = [] +for a, b in zip(states, states[1:]): + ok = check.segment(q, q, bm(a, z), bm(b, z)); details.append(check.last) + assert ok, check.last + +failure_raw = json.loads((C / "rollout/NAVIGATION_MOTION_BLOCK.item_02.tick_00035270.37a837efc4e9.json").read_text()) +out = { + "schema": "multi_item.case7_item2_carry_recovery_probe.v3", + "read_only": True, + "status_tick": 35270, + "failure_sha256": failure_raw["failure_sha256"], + "physical_strategy": "rotate_to_final_yaw_35mm_before_table_edge_then_straight_approach", + "original_goal": goal.tolist(), + "primitives": primitives, + "segment_details": details, + "all_full_body_held_target_live_props_screened": True, + "native_validation_required": True, +} +p = W / "diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V3_PROPOSAL.json" +p.write_text(json.dumps(out, indent=2, default=lambda value: value.tolist() if hasattr(value, "tolist") else str(value)) + "\n") +print(json.dumps({"out": str(p), "sha256": hashlib.sha256(p.read_bytes()).hexdigest(), "primitives": primitives}, indent=2, default=lambda value: value.tolist() if hasattr(value, "tolist") else str(value))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py new file mode 100644 index 0000000000000000000000000000000000000000..9d21b84a7870f773504da0f78bdac110be5e669d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python3 +"""Screen a robust overshoot-and-return correction for carried turn drift.""" +import hashlib, json, math, os, sys, time +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent; R = W.parents[1]; C = W / "cases/layout_07" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance + + +def mat(p): + m = np.eye(4); m[:3, 3] = p[:3]; m[:3, :3] = Rotation.from_quat(p[3:]).as_matrix(); return m + + +def bm(state, z): + m = np.eye(4); m[:3, :3] = Rotation.from_euler("z", state[2]).as_matrix(); m[:3, 3] = [state[0], state[1], z]; return m + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 2 and status["control_ticks"] == 36805 +job = json.loads((C / "job.json").read_text()); item = job["items"][2] +cp = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = cp["q"][-1].astype(float); names = cp["dof_names"].tolist(); z = float(cp["base_pose"][-1, 2]) +start = np.array([-1.0898466110229492, 1.089781641960144, 0.3164128363132477]) +goal = np.array([-1.0907547127921056, 1.090741734089923, 0.33365312012539144]) +hi = cp["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") +ti = cp["target_paths"].tolist().index(item["target"]) +target = mat(cp["target_pose_all"][-1, ti]); held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ target +props = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] +ctrl = np.load(item["controls_file"]) +check = Clearance(stage, robot, names, item["hand"], item["target"], ctrl["target_local"], held, props) +check.deadline = time.monotonic() + 900 + +delta = goal[:2] - start[:2]; direction = delta / np.linalg.norm(delta); approach_yaw = math.atan2(direction[1], direction[0]) +past = goal[:2] + 0.030 * direction +states = [start, [*start[:2], approach_yaw], [*past, approach_yaw], [*past, approach_yaw + math.pi], [*goal[:2], approach_yaw + math.pi], goal] +motion = [ + {"kind": "turn", "goal": states[1]}, + {"kind": "drive", "goal": states[2], "direction": 1}, + {"kind": "turn", "goal": states[3]}, + {"kind": "drive", "goal": states[4], "direction": 1}, + {"kind": "turn", "goal": states[5]}, +] +details = [] +for a, b in zip(states, states[1:]): + ok = check.segment(q, q, bm(a, z), bm(b, z)); details.append(check.last); assert ok, check.last + +block = json.loads((C / "rollout/NAVIGATION_MOTION_BLOCK.item_02.tick_00036805.f410ac1f6e25.json").read_text()) +out = { + "schema": "multi_item.case7_item2_carry_recovery_probe.v4", "read_only": True, + "status_tick": 36805, "failure_sha256": block["failure_sha256"], + "strategy": "30mm_overshoot_and_return_with_high_static_friction_floor", + "start": start.tolist(), "goal": goal.tolist(), "motion": motion, + "controller": {"turn_min_rate_rad_s": 0.06, "turn_cap_rad_s": 0.18, "drive_min_speed_m_s": 0.015, "drive_cap_m_s": 0.08, "xy_gate_m": 0.0008, "yaw_gate_rad": 0.0008}, + "segment_details": details, "all_full_body_held_target_live_props_screened": True, + "native_validation_required": True, +} +p = W / "diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V4_PROPOSAL.json" +p.write_text(json.dumps(out, indent=2, default=lambda x: x.tolist() if hasattr(x, "tolist") else str(x)) + "\n") +print(json.dumps({"out": str(p), "sha256": hashlib.sha256(p.read_bytes()).hexdigest(), "motion": motion}, indent=2, default=lambda x: x.tolist() if hasattr(x, "tolist") else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py new file mode 100644 index 0000000000000000000000000000000000000000..73447b7f40e7c20dc185548a124d687771771507 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==39547 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112915277481079,-0.9873537215446402]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v5','read_only':True,'status_tick':39547,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'249760697aee4340b1ab2b2218c74e1c2c7ac1d77a777c2c946e9badc11c75f9','strategy':'complete_v4_from_measured_partial_state_at_full_existing_turn_cap','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_command_rate_rad_s':0.25,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V5_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py new file mode 100644 index 0000000000000000000000000000000000000000..23389a5d46709f8982fbb1a42999273c09914b27 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==41347 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.9051387196416416]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v6','read_only':True,'status_tick':41347,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'08a6363b8f00797c3a1aedeea980a87bda4e635be5a9641dc8a1e9c5769015f1','strategy':'complete_from_measured_partial_state_with_acceleration_aware_braking','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V6_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py new file mode 100644 index 0000000000000000000000000000000000000000..1c589bf02112800d83a3e0a0295227f7e6ef1db8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43147 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8151900665756764]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v7','read_only':True,'status_tick':43147,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'f77b0ccbf8343be1d3142f4138e5a2b50e64aa7ab7065909bbc152f4e4e610bf','strategy':'coarse_stopping_distance_then_adaptive_recorded_control_tick_pulses','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.19,'fine_pulse_max_rate_rad_s':0.25,'fine_pulse_hold_ticks':6,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V7_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py new file mode 100644 index 0000000000000000000000000000000000000000..02654847343a86770685d11dfcfcabe52cb81c0e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43568 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8153694789297252]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v8','read_only':True,'status_tick':43568,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'e9f139540c9d02df0bf60a0fa0eb793c9b54ba684f1664f5c0ac49a0cdc7cd5d','strategy':'measured_single_tick_pulse_calibration_then_remaining_route','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.179,'fine_pulse_max_rate_rad_s':0.19,'fine_pulse_hold_ticks':2,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V8_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py new file mode 100644 index 0000000000000000000000000000000000000000..e6d6eb34cb9ee099e6b79508a4c9bc05826b2c6d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43752 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8150531268867094]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v9','read_only':True,'status_tick':43752,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'abe13de9b4f3b0f3028afc9dc1b29adc58550f49a89fac85354a9b230aa208c0','strategy':'lower_direct_pulse_rate_from_measured_response_then_remaining_route','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.11,'fine_pulse_max_rate_rad_s':0.18,'fine_pulse_hold_ticks':2,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V9_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a75807d254bc65093cd8cdbee5bbd919dc8e10 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +"""Screen a final-yaw straight projection into the original carried endpoint gate.""" +import hashlib,json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==44988 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.0666638612747192,1.0657597780227661,-1.5737453699111938]);parent=np.array([-1.0657120670686107,1.0644687903151804,-1.5746476816413626]);heading=np.array([math.cos(start[2]),math.sin(start[2])]);along=float((parent[:2]-start[:2])@heading);candidate=start.copy();candidate[:2]+=along*heading +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900;assert check.segment(q,q,bm(start,z),bm(candidate,z)),check.last +xy_error=float(np.linalg.norm(candidate[:2]-parent[:2]));yaw_error=abs(math.atan2(math.sin(candidate[2]-parent[2]),math.cos(candidate[2]-parent[2])));assert xy_error<.001 and yaw_error<.001 +out={'schema':'multi_item.case7_item2_endpoint_projection.v10','read_only':True,'status_tick':44988,'parent_failure_sha256':'37a837efc4e96403795fe238572cbdfab6c487e4d240c977914f8c87da4b76bb','start':start.tolist(),'parent_endpoint':parent.tolist(),'projected_endpoint':candidate.tolist(),'commanded_along_m':along,'projected_xy_error_to_parent_m':xy_error,'yaw_error_to_parent_rad':yaw_error,'controller':{'drive_speed_cap_m_s':.05,'drive_acceleration_cap_m_s2':.12,'drive_min_speed_m_s':.008,'parent_xy_gate_m':.001,'parent_yaw_gate_rad':.001},'full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_ENDPOINT_PROJECTION_V10_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'projected_endpoint':candidate.tolist(),'xy_error':xy_error,'yaw_error':yaw_error},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py new file mode 100644 index 0000000000000000000000000000000000000000..a121fc6019fbd45df16b8c914e03c2477a1999cb --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + grid = json.loads((W / "diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json").read_text())["top"] + candidates = [(float(entry["score"]), x, y, yaw, footprint, gaps, goal, np.asarray(entry["base"], dtype=float), transfer) for entry in grid] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_grounded_station_candidates_full_qualification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_GROUNDED_STATION_CANDIDATES_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py new file mode 100644 index 0000000000000000000000000000000000000000..fb702e75605e3b144f9fb326ecd34bb915d6194b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base = np.asarray(json.loads((W / "diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json").read_text())["top"][0]["base"], dtype=float) + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_grounded_station_full_qualification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_GROUNDED_STATION_FULL_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..e2a07be4b942b37003f9cedbe27a8dc958cf7817 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..4baaca9648c622daa9ea0e4f46558599664d5252 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_old_success_seed_requalification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_OLD_SUCCESS_SEED_REQUALIFICATION.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..6801f94fafe8dfa859e320663e417921313e3eb7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_08' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==1 +job=json.loads((C/'job.json').read_text());item=job['items'][1];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][1] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +old=json.loads(Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json').read_text())['accepted'];center=np.array(old['slot']['xy']);desired=float(old['slot']['yaw_rad']);base0=np.array(old['place_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case8_item1_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..189f4e706803bcd621c6c8c563e32cb20093c469 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 9's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_09" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case9 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Native navigation preserves the grounded root height. The + # historical source station carried an unrealizable 11 mm Z + # bias that must not enter FK, bridge, or placement checks. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + # The native repair transitions from the carry planner's compact + # destination posture, not directly from the frozen live posture. + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "post_carry_destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "carry_bridge_and_place_descent_world_checks": True, + "release_retreat_offline_scope": "self_and_wrist_only; authoritative native all-target world audit required", + "base_z_normalized_to_live_checkpoint": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case9_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "carry_bridge_and_place_descent_world_checks", "release_retreat_offline_scope")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d7e2d5f6901f7ed5d4d761abe26686373a6f41 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_09" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case9 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case9_item1_old_success_seed_requalification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE9_ITEM01_OLD_SUCCESS_SEED_REQUALIFICATION.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py new file mode 100644 index 0000000000000000000000000000000000000000..6e54295176cebaf6db13f880263bd2247d0aa44d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +"""Read-only future Honeymaid carry/place prequalification for case 9.""" +import json, math, os, pathlib, sys, time +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from ik_solver import Arm + +def matrix_xyzw(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +def matrix_wxyz(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m + +def footprint(local,pose): + world=(np.c_[local,np.ones(len(local))]@pose.T)[:,:3] + return Polygon(world[ConvexHull(world[:,:2]).vertices,:2]) + +def main(): + status=json.loads((C/'STATUS.json').read_text());job=json.loads((C/'job.json').read_text());item=job['items'][2] + checkpoint_path=C/'rollout/checkpoint_60hz.npz';checkpoint=np.load(checkpoint_path,allow_pickle=True) + if int(checkpoint['item_index'][-1])!=2:raise RuntimeError('Pinned case9 checkpoint is not the live item2 carry boundary') + zbar_proposal_path=W/'CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json';zbar_proposal=json.loads(zbar_proposal_path.read_text());zbar=zbar_proposal['accepted'] + exact=json.loads((C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json').read_text())['items'][2] + data=np.load(item['controls_file']);prior=np.load(exact['prior_controls']);names=checkpoint['dof_names'].tolist();control_names=data['names'].tolist();ids=[names.index(name) for name in control_names] + q_pick=checkpoint['q'][-1].astype(float).copy();q_place=q_pick.copy();q_place[ids]=prior['place_initial'] + grippers=[i for i,name in enumerate(names) if 'gripper_' in name];q_place[grippers]=q_pick[grippers] + hand_path=f"/World/Robot/gripper_{item['hand']}_base_link";hi=checkpoint['robot_link_paths'].tolist().index(hand_path);ti=checkpoint['target_paths'].tolist().index(item['target']) + held=np.linalg.inv(matrix_xyzw(checkpoint['robot_link_pose'][-1,hi]))@matrix_xyzw(checkpoint['target_pose_all'][-1,ti]) + live_base_z=float(checkpoint['base_pose'][-1,2]);pick_base=matrix_xyzw(checkpoint['base_pose'][-1]) + stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) + predicted_post_pick=matrix_xyzw(checkpoint['target_pose_all'][-1,ti]) + # Preserve actual frozen prop poses, replacing only Zbar with its qualified (not yet final actual) landing. + prop_poses={str(path):matrix_xyzw(pose) for path,pose in zip(checkpoint['rigid_body_paths'],checkpoint['prop_pose'][-1])} + checkpoint_receipt=json.loads((C/'rollout/CHECKPOINT.json').read_text());zbar_is_actual=job['items'][1]['target'] in checkpoint_receipt.get('completed_objects',[]) + if zbar_is_actual: + zbar_goal=matrix_xyzw(checkpoint['target_pose_all'][-1,1]) + else: + zbar_goal=np.asarray(zbar['placement_report']['goal'],float);prop_poses[job['items'][1]['target']]=zbar_goal + gatorade_pose=matrix_xyzw(checkpoint['target_pose_all'][-1,0]);gatorade_local=np.load(job['items'][0]['controls_file'])['target_local'];gatorade_foot=footprint(gatorade_local,gatorade_pose) + zbar_local=np.load(job['items'][1]['controls_file'])['target_local'];zbar_foot=footprint(zbar_local,zbar_goal);obstacles=[gatorade_foot,zbar_foot] + vertices=data['target_local'];floor=Polygon(job['tray_polygon_xy']).buffer(-.008);accepted_goal=np.asarray(exact['transferred_accepted_goal'],float);accepted_base=np.asarray(exact['transferred_accepted_base'],float) + accepted_yaw=float(exact['accepted_yaw_rad']);job_yaw=float(item['placement_slot']['yaw_rad']);yaws=[] + for yaw in [accepted_yaw,accepted_yaw+math.pi,accepted_yaw+math.pi/2,accepted_yaw-math.pi/2,accepted_yaw+math.radians(15),accepted_yaw-math.radians(15),job_yaw,job_yaw+math.pi]: + yaw=float(math.atan2(math.sin(yaw),math.cos(yaw))) + if all(abs(math.atan2(math.sin(yaw-old),math.cos(yaw-old)))>1e-8 for old in yaws):yaws.append(yaw) + minx,miny,maxx,maxy=floor.bounds;candidates=[] + for yaw in yaws: + rot=Rotation.from_euler('z',yaw).as_matrix();points=vertices@rot.T;local=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]) + for x in np.arange(minx,maxx+1e-9,.004): + for y in np.arange(miny,maxy+1e-9,.004): + foot=Polygon(np.asarray(local.exterior.coords)[:-1]+[x,y]);gaps=[foot.distance(o) for o in obstacles] + if not floor.covers(foot) or min(gaps)<=.012:continue + goal=np.eye(4);goal[:3,:3]=rot;goal[:2,3]=[x,y];goal[2,3]=job['tray_floor_z']+.002-(vertices@rot.T)[:,2].min() + transfer=goal@np.linalg.inv(accepted_goal);base=transfer@accepted_base;base[2,3]=live_base_z + score=float(np.linalg.norm(base[:2,3]-np.asarray(item['place_base'])[:2,3])+.12*np.linalg.norm(goal[:2,3]-np.asarray(item['placement_slot']['xy']))) + candidates.append((score,x,y,yaw,foot,gaps,goal,base,transfer)) + candidates.sort(key=lambda row:row[0]);attempts=[];accepted=None;place_only=None + source_layout_id=int(job.get('source_layout_id',job['layout_id']));release_path=pathlib.Path(job['layout']).parents[1]/'audits_release'/f'{source_layout_id:02d}.json';release=json.loads(release_path.read_text()) + checker=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,prop_poses);checker.deadline=time.monotonic()+1800 + arm_ids=[names.index(f"idx{6 if item['hand']=='r' else 2}{k}_arm_{item['hand']}_joint{k}") for k in range(1,8)] + joints={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([joints[names[i]].GetAttribute('physics:lowerLimit').Get() for i in arm_ids])*np.pi/180+1e-4;hi=np.array([joints[names[i]].GetAttribute('physics:upperLimit').Get() for i in arm_ids])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if item['hand']=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + for rank,candidate in enumerate(candidates[:80]): + _,x,y,yaw,foot,gaps,goal,base,transfer=candidate + # Correct the historical station's 11 mm root-height bias by solving only + # the active arm back to the rigidly transferred accepted object goal. + arm=Arm(robot,names,q_place,base,robot.root+hand_path.removeprefix('/World/Robot'),arm_ids);previous=q_place[arm_ids].copy();hand_goal=goal@np.linalg.inv(held) + fit=least_squares(lambda v:arm.evaluate(v,hand_goal,previous)[0],np.clip(previous,lo,hi),jac=lambda v:arm.evaluate(v,hand_goal,previous)[1],bounds=(lo,hi),max_nfev=100) + q_station=q_place.copy();q_station[arm_ids]=fit.x;actual_hand=arm.forward(fit.x)[0];endpoint_error=[float(np.linalg.norm(actual_hand[:3,3]-hand_goal[:3,3])),float(Rotation.from_matrix(hand_goal[:3,:3].T@actual_hand[:3,:3]).magnitude())] + if endpoint_error[0]>.002 or endpoint_error[1]>.02:attempts.append(dict(rank=rank,stage='normalized_station_endpoint',error=endpoint_error));continue + if not checker.check(q_station,base):attempts.append(dict(rank=rank,stage='destination_place_initial_clearance',detail=checker.last));continue + object_pose=robot.fk(names,q_station,base)[robot.root+hand_path.removeprefix('/World/Robot')]@held + slot=dict(xy=[float(x),float(y)],yaw_rad=float(yaw),footprint_xy=np.asarray(foot.exterior.coords)[:-1].tolist(),tolerance_m=.001) + item_job={**job,**item,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':[],'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};item_job.pop('carry_route_seed',None) + began=time.monotonic() + try:plan,retreat,place_report=solve(robot,names,q_station,base,object_pose,vertices,item_job,obstacles,pose_check,world_check=checker) + except Exception as exc:attempts.append(dict(rank=rank,stage='placement',error=str(exc),elapsed_s=time.monotonic()-began));continue + place_only=dict(rank=rank,slot=slot,destination_xyz=[float(x),float(y),float(job['tray_floor_z'])],place_base=base.tolist(),place_initial20=q_station[ids].tolist(),source_place_initial20=prior['place_initial'].tolist(),normalized_station_endpoint_error=endpoint_error,held_live=held.tolist(),geometry=dict(tray_inset_m=.008,gap_to_actual_gatorade_m=float(gaps[0]),gap_to_actual_zbar_m=float(gaps[1]),inside=True),placement_plan_frames=len(plan),placement_report=place_report,retreat_q=retreat.tolist(),base_z_normalized_to_checkpoint=True,station_bridge_and_place_descent_world_checks=True,release_retreat_offline_scope='self_and_wrist_only; authoritative native all-target world audit required') + try:compact,primitives,carry_report=plan_carry(stage,robot,names,q_pick,pick_base,item['hand'],item['target'],vertices,predicted_post_pick,prop_poses,item_job,release) + except Exception as exc:attempts.append(dict(rank=rank,stage='live_source_carry',error=str(exc),elapsed_s=time.monotonic()-began));break + if not checker.segment(compact,q_station,base,base):attempts.append(dict(rank=rank,stage='post_carry_destination_bridge_clearance',detail=checker.last));continue + accepted=dict(rank=rank,slot=slot,destination_xyz=[float(x),float(y),float(job['tray_floor_z'])],place_base=base.tolist(),place_initial20=q_station[ids].tolist(),source_place_initial20=prior['place_initial'].tolist(),normalized_station_endpoint_error=endpoint_error,held_source=held.tolist(),predicted_post_pick_pose=predicted_post_pick.tolist(),pair_transform=transfer.tolist(),geometry=dict(tray_inset_m=.008,gap_to_actual_gatorade_m=float(gaps[0]),gap_to_zbar_m=float(gaps[1]),zbar_pose_source='actual supported checkpoint' if zbar_is_actual else 'qualified predicted landing',inside=True),placement_plan_frames=len(plan),placement_report=place_report,retreat_q=retreat.tolist(),reposition=dict(compact_q=compact.tolist(),primitives=primitives,report=carry_report),base_z_normalized_to_checkpoint=True,carry_bridge_and_place_descent_world_checks=True,release_retreat_offline_scope='self_and_wrist_only; authoritative native all-target world audit required') + attempts.append(dict(rank=rank,stage='passed',placement_frames=len(plan),reposition_primitives=len(primitives)));break + result=dict(schema='multi_item.case9_item2_live_place.v1',read_only=True,prediction_only=False,status_at_probe=status,pinned_checkpoint_tick=int(checkpoint['tick'][-1]),method='Actual live item2 held transform and full prop poses; exact accepted placement pair with grounded-base-normalized place posture; placement and standard carry evaluated separately.',evidence=dict(checkpoint=str(checkpoint_path),job=str(C/'job.json'),exact_source=str(C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json'),prior_controls=exact['prior_controls'],prior_placement=exact['prior_placement_plan'],zbar_qualified_proposal=str(zbar_proposal_path)),pose_evidence=dict(gatorade='actual checkpoint',zbar='actual supported checkpoint',honeymaid='actual live post-pick checkpoint'),geometry_candidates=len(candidates),accepted=accepted,place_only_qualified=place_only,attempts=attempts,native_recheck_required=['custom live shelf egress if standard carry remains blocked','live carry execution','all final validation gates']) + out=W/'CASE09_ITEM02_LIVE_PLACE_PROPOSAL.json';out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(dict(out=str(out),geometry_candidates=len(candidates),accepted=accepted and {k:accepted[k] for k in ['rank','slot','destination_xyz','place_base','geometry','placement_plan_frames','base_z_normalized_to_checkpoint','carry_bridge_and_place_descent_world_checks','release_retreat_offline_scope']},place_only_qualified=place_only and {k:place_only[k] for k in ['rank','slot','destination_xyz','place_base','geometry','placement_plan_frames','base_z_normalized_to_checkpoint','station_bridge_and_place_descent_world_checks','release_retreat_offline_scope']},attempts=attempts),indent=2));raise SystemExit(0 if accepted or place_only else 2) +if __name__=='__main__':main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py new file mode 100644 index 0000000000000000000000000000000000000000..115071938d4f97e0caf45f2e34ea5b1abd1adf70 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 9's frozen HoneyMaid shelf egress boundary.""" +import hashlib +import json +import math +import os +import pathlib +import sys +import time +import zlib + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [ + str(W), + str(R / "tools/family3"), + str(R / "work/place_training_tray_20260918"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "code/family3_repair/real2sim/teleop"), +] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) + +from pxr import Usd, UsdGeom, UsdPhysics # noqa: E402 +from collision_scene import ( # noqa: E402 + Body, + ROBOT, + Robot, + burial, + collider_mesh, + contact, + matrix, + rigid_ancestor, +) +from carry_transition import Clearance # noqa: E402 +from ik_solver import Arm # noqa: E402 + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def rigid_part(value): + value = np.asarray(value, dtype=float) + u, _, vt = np.linalg.svd(value[:3, :3]) + rotation = u @ vt + if np.linalg.det(rotation) < 0: + u[:, -1] *= -1 + rotation = u @ vt + result = np.eye(4) + result[:3, :3] = rotation + result[:3, 3] = value[:3, 3] + return result + + +def decode_native_contacts(path, target, neighbor): + decoder = zlib.decompressobj(16 + zlib.MAX_WBITS) + raw = decoder.decompress(path.read_bytes()) + rows = [] + incomplete_rows = 0 + for line in raw.splitlines(): + try: + row = json.loads(line) + except json.JSONDecodeError: + incomplete_rows += 1 + continue + if set(row.get("actors", [])) == {target, neighbor}: + rows.append(row) + ticks = sorted({int(row["tick"]) for row in rows}) + per_tick = [] + for tick in ticks: + selected = [row for row in rows if int(row["tick"]) == tick] + per_tick.append( + { + "tick": tick, + "rows": len(selected), + "unique_neighbor_colliders": sorted({row["colliders"][0] for row in selected}), + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in selected), + "maximum_impulse_norm": max(float(np.linalg.norm(row["impulse_sum"])) for row in selected), + } + ) + return { + "decoder": "partial gzip stream decoded without final trailer", + "complete_json_rows": len(raw.splitlines()) - incomplete_rows, + "discarded_incomplete_tail_rows": incomplete_rows, + "pair_rows": len(rows), + "first_tick": ticks[0] if ticks else None, + "last_complete_contact_tick": ticks[-1] if ticks else None, + "last_20_tick_summary": per_tick[-20:], + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in rows), + "maximum_impulse_norm": max(float(np.linalg.norm(row["impulse_sum"])) for row in rows), + "native_pair_contact_present": bool(rows), + } + + +def target_native_body(stage, target_path, target_pose): + cache = UsdGeom.XformCache() + target_prim = stage.GetPrimAtPath(target_path) + colliders = [] + for prim in Usd.PrimRange(target_prim, Usd.TraverseInstanceProxies()): + if not prim.HasAPI(UsdPhysics.CollisionAPI): + continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False: + continue + mesh, method, approximation = collider_mesh(prim) + owner = rigid_ancestor(prim) + if owner is None or str(owner.GetPath()) != target_path: + continue + authored_owner = rigid_part(matrix(cache.GetLocalToWorldTransform(owner))) + authored_collider = matrix(cache.GetLocalToWorldTransform(prim)) + mesh.apply_transform(np.linalg.inv(authored_owner) @ authored_collider) + colliders.append( + Body( + str(prim.GetPath()) + "#native_target_collider", + mesh, + target_pose, + {"method": method, "approximation": approximation}, + ) + ) + if not colliders: + raise RuntimeError("No enabled target collider reconstructed") + return colliders + + +def pair_key(a, b): + return (a.name, b.name) + + +def main(): + status_path = C / "STATUS.json" + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contact_path = C / "rollout/contacts.jsonl.gz" + status = json.loads(status_path.read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"Frozen case 9 item 2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + target_path = item["target"] + neighbor_path = "/World/Assets/Tier2Items/oreo_gf" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + base = pose_matrix(checkpoint["base_pose"][-1]) + link_paths = checkpoint["robot_link_paths"].tolist() + target_paths = checkpoint["target_paths"].tolist() + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = link_paths.index(hand_path) + target_index = target_paths.index(target_path) + recorded_hand = pose_matrix(checkpoint["robot_link_pose"][-1, hand_index]) + recorded_target = pose_matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(recorded_hand) @ recorded_target + prop_poses = { + str(path): pose_matrix(value) + for path, value in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1]) + } + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + target_vertices = np.load(item["controls_file"])["target_local"] + checker = Clearance(stage, robot, names, item["hand"], target_path, target_vertices, held, prop_poses) + checker.deadline = time.monotonic() + 1800 + local_fk = robot.fk(names, q, np.eye(4)) + self_ok, self_detail = checker.self_check(local_fk, robot.root) + reconstructed_hand = base @ local_fk[robot.root + "/gripper_r_base_link"] + reconstructed_target = reconstructed_hand @ held + fk_error = { + "hand_translation_m": float(np.linalg.norm(reconstructed_hand[:3, 3] - recorded_hand[:3, 3])), + "hand_rotation_rad": float(Rotation.from_matrix(reconstructed_hand[:3, :3].T @ recorded_hand[:3, :3]).magnitude()), + "target_translation_m": float(np.linalg.norm(reconstructed_target[:3, 3] - recorded_target[:3, 3])), + "target_rotation_rad": float(Rotation.from_matrix(reconstructed_target[:3, :3].T @ recorded_target[:3, :3]).magnitude()), + } + native_targets = target_native_body(stage, target_path, recorded_target) + neighbor_environment = [body for body in checker.environment if body.name.startswith(neighbor_path + "/")] + if not neighbor_environment: + raise RuntimeError("No reconstructed Oreo collider bodies") + + yaw = float(Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2]) + heading = np.array([math.cos(yaw), math.sin(yaw)]) + + def evaluate_state(candidate_q, candidate_base, label): + state_local_fk = robot.fk(names, candidate_q, np.eye(4)) + state_self_ok, state_self_detail = checker.self_check(state_local_fk, robot.root) + world_fk = {name: candidate_base @ value for name, value in state_local_fk.items()} + for body in checker.self_check.bodies: + body.update(world_fk[body.name.replace("/World/Robot", robot.root, 1)]) + candidate_target = world_fk[robot.root + "/gripper_r_base_link"] @ held + checker.target.update(candidate_target) + delta = candidate_target @ np.linalg.inv(recorded_target) + for body in native_targets: + body.update(delta @ body.authored_transform) + + collisions = [] + tested = checker.self_check.bodies + [checker.target] + for body in tested: + indices = np.flatnonzero( + np.all(checker.high >= body.lo, axis=1) & np.all(checker.low <= body.hi, axis=1) + ) + for index in indices: + other = checker.environment[index] + hit = contact(body, other, containment=True) + if hit is not None: + collisions.append({"a": body.name, "b": other.name, "contact": hit}) + native_pair_hits = [] + for target_body in native_targets: + for neighbor_body in neighbor_environment: + hit = contact(target_body, neighbor_body, containment=True) + if hit is not None: + native_pair_hits.append( + { + "target_collider": target_body.name, + "neighbor_collider": neighbor_body.name, + "contact": hit, + "burial": burial(target_body, neighbor_body, max_points=1024), + } + ) + return { + "label": label, + "base_xy": candidate_base[:2, 3].tolist(), + "target_xyz": candidate_target[:3, 3].tolist(), + "self_and_wrist_ok": bool(state_self_ok), + "self_and_wrist_detail": state_self_detail, + "planner_collision_pairs": collisions, + "planner_collision_pair_count": len(collisions), + "planner_target_neighbor_pairs": [ + row for row in collisions if row["a"] == target_path and row["b"].startswith(neighbor_path + "/") + ], + "native_shape_target_neighbor_pairs": native_pair_hits, + "native_shape_target_neighbor_pair_count": len(native_pair_hits), + } + + def evaluate_reverse(distance): + candidate_base = base.copy() + candidate_base[:2, 3] -= distance * heading + result = evaluate_state(q, candidate_base, f"reverse_{distance:.6f}m") + result["reverse_distance_m"] = float(distance) + return result + + # Dense first-centimeter samples isolate the first reverse behavior; later + # samples confirm that the same fixed-grasp base motion remains clear. + distances = [0.0, 0.00025, 0.0005, 0.001, 0.002, 0.003, 0.005, 0.0075, 0.01] + distances += [float(value) for value in np.arange(0.0125, 0.25001, 0.0025)] + samples = [evaluate_reverse(distance) for distance in distances] + initial_pairs = { + (row["a"], row["b"]) for row in samples[0]["planner_collision_pairs"] + } + for sample in samples: + pairs = {(row["a"], row["b"]) for row in sample["planner_collision_pairs"]} + sample["new_planner_pairs_relative_to_start"] = [list(pair) for pair in sorted(pairs - initial_pairs)] + sample["existing_planner_pairs_remaining"] = [list(pair) for pair in sorted(pairs & initial_pairs)] + first_planner_clear = next( + (sample["reverse_distance_m"] for sample in samples if not sample["planner_target_neighbor_pairs"]), None + ) + first_native_shape_clear = next( + (sample["reverse_distance_m"] for sample in samples if sample["native_shape_target_neighbor_pair_count"] == 0), None + ) + no_new_pairs = all(not sample["new_planner_pairs_relative_to_start"] for sample in samples) + all_after_planner_clear = bool( + first_planner_clear is not None + and all( + not sample["planner_target_neighbor_pairs"] + for sample in samples + if sample["reverse_distance_m"] >= first_planner_clear + ) + ) + all_after_native_clear = bool( + first_native_shape_clear is not None + and all( + sample["native_shape_target_neighbor_pair_count"] == 0 + for sample in samples + if sample["reverse_distance_m"] >= first_native_shape_clear + ) + ) + gripper_ids = [index for index, name in enumerate(names) if "gripper_" in name] + native_contacts = decode_native_contacts(contact_path, target_path, neighbor_path) + + # Search small continuous right-arm translations. A target-space move is + # acceptable only if every interpolated posture passes the original + # self/wrist gate, creates no new environment actor pair, and reaches a + # state accepted by the ordinary strict world checker. + arm_ids = [names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + joints = {prim.GetName(): prim for prim in robot.stage.Traverse() if prim.GetAttribute("physics:lowerLimit")} + lower = np.array([joints[names[index]].GetAttribute("physics:lowerLimit").Get() for index in arm_ids]) * np.pi / 180 + 1e-4 + upper = np.array([joints[names[index]].GetAttribute("physics:upperLimit").Get() for index in arm_ids]) * np.pi / 180 - 1e-4 + from motion_limits import LOWER, UPPER + + lower = np.maximum(lower, LOWER[7:14] + 1e-4) + upper = np.minimum(upper, UPPER[7:14] - 1e-4) + arm = Arm(robot, names, q, base, robot.root + "/gripper_r_base_link", arm_ids) + current_hand = reconstructed_hand + + def actor_pair(row): + obstacle = row["b"] + if obstacle.startswith(neighbor_path + "/"): + obstacle = neighbor_path + elif obstacle.startswith("/World/Assets/ShelvingUnit/"): + obstacle = "/World/Assets/ShelvingUnit" + return row["a"], obstacle + + initial_actor_pairs = {actor_pair(row) for row in samples[0]["planner_collision_pairs"]} + arm_candidates = [] + direction_rows = [ + ("world_minus_x", np.array([-1.0, 0.0, 0.0])), + ("world_plus_x", np.array([1.0, 0.0, 0.0])), + ("world_plus_y", np.array([0.0, 1.0, 0.0])), + ("world_minus_y", np.array([0.0, -1.0, 0.0])), + ("world_plus_z", np.array([0.0, 0.0, 1.0])), + ("world_minus_x_plus_z", np.array([-1.0, 0.0, 1.0]) / math.sqrt(2.0)), + ("world_plus_x_plus_z", np.array([1.0, 0.0, 1.0]) / math.sqrt(2.0)), + ("world_plus_y_plus_z", np.array([0.0, 1.0, 1.0]) / math.sqrt(2.0)), + ] + for direction_name, direction in direction_rows: + for distance in (0.002, 0.005, 0.010, 0.015, 0.020, 0.030, 0.040): + goal = current_hand.copy() + goal[:3, 3] += direction * distance + previous = q[arm_ids].copy() + fit = least_squares( + lambda value: arm.evaluate(value, goal, previous)[0], + np.clip(previous, lower, upper), + jac=lambda value: arm.evaluate(value, goal, previous)[1], + bounds=(lower, upper), + max_nfev=120, + ) + endpoint_hand = arm.forward(fit.x)[0] + endpoint_error = { + "translation_m": float(np.linalg.norm(endpoint_hand[:3, 3] - goal[:3, 3])), + "rotation_rad": float(Rotation.from_matrix(endpoint_hand[:3, :3].T @ goal[:3, :3]).magnitude()), + } + row = { + "direction": direction_name, + "distance_m": distance, + "direction_world": direction.tolist(), + "endpoint_error": endpoint_error, + "fit_success": bool(fit.success), + } + if endpoint_error["translation_m"] > 0.001 or endpoint_error["rotation_rad"] > 0.01: + row["qualified"] = False + row["reason"] = "ik_endpoint_error" + arm_candidates.append(row) + continue + endpoint_q = q.copy() + endpoint_q[arm_ids] = fit.x + steps = max(2, int(math.ceil(float(np.max(np.abs(endpoint_q - q))) / 0.005))) + path = [] + prior_native_count = samples[0]["native_shape_target_neighbor_pair_count"] + monotonic_native_count = True + for index, u in enumerate(np.linspace(0.0, 1.0, steps + 1)): + state_q = q * (1.0 - u) + endpoint_q * u + state = evaluate_state(state_q, base, f"{direction_name}_{distance:.3f}_u{u:.6f}") + actor_pairs = {actor_pair(hit) for hit in state["planner_collision_pairs"]} + state["new_actor_pairs"] = [list(pair) for pair in sorted(actor_pairs - initial_actor_pairs)] + native_count = state["native_shape_target_neighbor_pair_count"] + if native_count > prior_native_count: + monotonic_native_count = False + prior_native_count = native_count + path.append(state) + endpoint = path[-1] + endpoint_q_strict_ok = checker.check(endpoint_q, base) + endpoint_q_strict_detail = None if endpoint_q_strict_ok else checker.last + row.update( + { + "endpoint_q": endpoint_q.tolist(), + "max_joint_delta_rad": float(np.max(np.abs(endpoint_q - q))), + "path_sample_count": len(path), + "all_self_and_wrist_ok": all(state["self_and_wrist_ok"] for state in path), + "no_new_actor_pairs": all(not state["new_actor_pairs"] for state in path), + "native_shape_neighbor_count_nonincreasing": monotonic_native_count, + "endpoint_native_shape_neighbor_pair_count": endpoint["native_shape_target_neighbor_pair_count"], + "endpoint_planner_target_neighbor_pair_count": len(endpoint["planner_target_neighbor_pairs"]), + "endpoint_planner_collision_pair_count": endpoint["planner_collision_pair_count"], + "endpoint_strict_world_ok": bool(endpoint_q_strict_ok), + "endpoint_strict_world_detail": endpoint_q_strict_detail, + "path": path, + } + ) + row["qualified"] = bool( + row["all_self_and_wrist_ok"] + and row["no_new_actor_pairs"] + and row["native_shape_neighbor_count_nonincreasing"] + and row["endpoint_native_shape_neighbor_pair_count"] == 0 + and row["endpoint_planner_target_neighbor_pair_count"] == 0 + and row["endpoint_strict_world_ok"] + ) + arm_candidates.append(row) + # The local adjustment is useful only if the unchanged raised posture can + # immediately enter the standard strictly collision-free reverse screen. + for row in arm_candidates: + if not row.get("qualified"): + continue + endpoint_q = np.asarray(row["endpoint_q"], dtype=float) + reverse_results = [] + for distance in (0.05, 0.10, 0.15, 0.25): + exit_base = base.copy() + exit_base[:2, 3] -= distance * heading + okay = checker.segment(endpoint_q, endpoint_q, base, exit_base) + reverse_results.append( + { + "distance_m": distance, + "strict_world_segment_ok": bool(okay), + "detail": None if okay else checker.last, + } + ) + row["ordinary_reverse_rechecks"] = reverse_results + row["qualified_with_ordinary_reverse"] = all(item["strict_world_segment_ok"] for item in reverse_results) + # The ordinary policy already uses a 0.55 m body height after shelf + # departure. Also test a staged escape: a short strict 5 cm reverse, + # raise there, then continue to 25 cm. This avoids dragging the held + # carton through Oreo at its original height. + if reverse_results[0]["strict_world_segment_ok"]: + base_005 = base.copy() + base_005[:2, 3] -= 0.05 * heading + base_025 = base.copy() + base_025[:2, 3] -= 0.25 * heading + high_q = endpoint_q.copy() + high_q[names.index("idx01_body_joint1")] = 0.55 + raise_ok = checker.segment(endpoint_q, high_q, base_005, base_005) + raise_detail = None if raise_ok else checker.last + continue_ok = bool(raise_ok and checker.segment(high_q, high_q, base_005, base_025)) + continue_detail = None if continue_ok else checker.last + row["staged_escape"] = { + "short_reverse_distance_m": 0.05, + "body_height_goal_m": 0.55, + "raise_at_short_reverse_strict_world_ok": bool(raise_ok), + "raise_detail": raise_detail, + "continue_reverse_to_m": 0.25, + "continued_reverse_strict_world_ok": continue_ok, + "continued_reverse_detail": continue_detail, + "high_q": high_q.tolist(), + "qualified": bool(raise_ok and continue_ok), + } + else: + row["staged_escape"] = {"qualified": False, "reason": "first_5cm_reverse_not_clear"} + qualified_arm = next( + ( + row + for row in arm_candidates + if row.get("qualified_with_ordinary_reverse") or row.get("staged_escape", {}).get("qualified") + ), + None, + ) + result = { + "schema": "multi_item.case9_item2_live_egress_diagnosis.v1", + "read_only": True, + "status_at_probe": status, + "pinned_checkpoint_tick": int(checkpoint["tick"][-1]), + "input_sha256": { + "checkpoint": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "partial_contact_stream": hashlib.sha256(contact_path.read_bytes()).hexdigest(), + "job": hashlib.sha256((C / "job.json").read_bytes()).hexdigest(), + }, + "native_contact_evidence": native_contacts, + "reconstruction": { + "fk_error": fk_error, + "self_and_wrist_gate_at_start": bool(self_ok), + "self_and_wrist_detail": self_detail, + "target_collision_representation": "both planner target_local convex hull and authored enabled target collider", + "environment_collision_representation": "authored enabled colliders at literal checkpoint prop poses", + "limitations": [ + "Offline FCL intersections do not reproduce PhysX contact offsets or dynamics.", + "The open native contact gzip ends at the last complete callback row before planning pause.", + ], + }, + "fixed_grasp_invariants": { + "held_target_to_hand_transform": held.tolist(), + "q_unchanged": True, + "gripper_positions_unchanged": q[gripper_ids].tolist(), + "base_z_unchanged_m": float(base[2, 3]), + "base_yaw_unchanged_rad": yaw, + "motion": "straight reverse along negative current heading", + }, + "screen": { + "sample_count": len(samples), + "maximum_reverse_distance_m": max(distances), + "first_planner_hull_neighbor_clear_m": first_planner_clear, + "first_authored_native_shape_neighbor_clear_m": first_native_shape_clear, + "planner_neighbor_stays_clear_after_first_clear": all_after_planner_clear, + "authored_neighbor_stays_clear_after_first_clear": all_after_native_clear, + "no_new_planner_collision_pairs": no_new_pairs, + "initial_planner_pairs": [list(pair) for pair in sorted(initial_pairs)], + "qualified_local_egress": bool( + self_ok + and first_planner_clear is not None + and first_native_shape_clear is not None + and all_after_planner_clear + and all_after_native_clear + and no_new_pairs + ), + "samples": samples, + }, + "local_arm_escape_search": { + "method": "right-hand Cartesian micro-translation with fixed orientation/base; bounded IK; linear joint interpolation at <=0.005 rad per sample", + "initial_allowed_actor_pairs": [list(pair) for pair in sorted(initial_actor_pairs)], + "candidate_count": len(arm_candidates), + "qualified": qualified_arm, + "candidate_summaries": [ + {key: value for key, value in row.items() if key not in ("path", "endpoint_q")} + for row in arm_candidates + ], + }, + "execution_requirement": { + "proposal_only": True, + "no_reset_or_pose_write_performed": True, + "required_native_behavior": "Execute a continuous low-speed reverse from the frozen state while preserving arm and gripper commands; permit only the already-recorded HoneyMaid/Oreo manifold at the first sample, require its impulses/separation to relax immediately, abort on any new contact, then use the ordinary full collision gate after the offline clear distance.", + "ordinary_carry_replan_required_after_egress": True, + }, + } + output = W / "CASE09_ITEM02_LIVE_EGRESS_DIAGNOSIS.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print( + json.dumps( + { + "output": str(output), + "checkpoint_tick": result["pinned_checkpoint_tick"], + "native_contact": { + key: native_contacts[key] + for key in ( + "pair_rows", + "first_tick", + "last_complete_contact_tick", + "minimum_reported_separation_m", + "maximum_impulse_norm", + ) + }, + "fk_error": fk_error, + "screen": {key: value for key, value in result["screen"].items() if key != "samples"}, + "qualified_arm": None if qualified_arm is None else { + key: value for key, value in qualified_arm.items() if key not in ("path", "endpoint_q") + }, + }, + indent=2, + ) + ) + raise SystemExit(0 if result["screen"]["qualified_local_egress"] or qualified_arm is not None else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..080fd6c704f1598f31cb2e9ef9aaf363ae54077d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""Read-only polar station/arm endpoint search for case9 final HoneyMaid.""" +import json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==2 +j=json.load(open(C/'job.json'));it=j['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;invheld=np.linalg.inv(held);verts=np.load(it['controls_file'])['target_local'];props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,'r',it['target'],verts,held,props);checker.deadline=time.monotonic()+3600 +active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;up=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);up=np.minimum(up,UPPER[7:14]-1e-4) +exact=json.load(open(R/'work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json'))['items'][2];prior=np.load(exact['prior_controls']);controls=np.load(it['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];seedq=liveq.copy();seedq[ids]=prior['place_initial'];seedq[[i for i,n in enumerate(names) if 'gripper_' in n]]=liveq[[i for i,n in enumerate(names) if 'gripper_' in n]] +# Measured completed-object footprints and geometry-valid free-space goals. +obs=[] +for k in range(2): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +floor=Polygon(j['tray_polygon_xy']).buffer(-.008);goals=[] +for yaw in [-math.pi/2,math.pi/2,-math.pi/2+math.radians(5),math.pi/2+math.radians(5)]: + Rz=Rotation.from_euler('z',yaw).as_matrix();local=verts@Rz.T;h=ConvexHull(local[:,:2]);lh=local[h.vertices,:2] + for x in [-1.0225,-1.0255,-1.0285,-1.0315,-1.0345]: + for y in [.43,.445,.46,.475,.49,.505]: + fp=Polygon(lh+[x,y]);gaps=[fp.distance(o) for o in obs];extra=floor.boundary.distance(fp) + if floor.covers(fp) and min(gaps)>.012 and extra>.0003: + g=np.eye(4);g[:3,:3]=Rz;g[:2,3]=[x,y];g[2,3]=j['tray_floor_z']+.002-local[:,2].min();goals.append((g,fp,gaps,extra)) +results=[];tested=0 +for gi,(goal,fp,gaps,extra) in enumerate(goals): + for angle in np.deg2rad(np.arange(-180,181,30)): + for radius in [.52,.62,.72,.82]: + bx=goal[0,3]+radius*math.cos(angle);by=goal[1,3]+radius*math.sin(angle) + face=math.atan2(goal[1,3]-by,goal[0,3]-bx) + for da in np.deg2rad([-12,0,12]): + base=np.eye(4);base[:3,:3]=Rotation.from_euler('z',face+da).as_matrix();base[:3,3]=[bx,by,livebase[2,3]] + arm=Arm(robot,names,seedq,base,robot.root+'/gripper_r_base_link',active);target=goal@invheld + best=None + for seed in [seedq[active],liveq[active]]: + fit=least_squares(lambda x:arm.evaluate(x,target,seed)[0],np.clip(seed,lo,up),jac=lambda x:arm.evaluate(x,target,seed)[1],bounds=(lo,up),max_nfev=130);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude()) + if best is None or pe+.1*re.004 or best[2]>.04:continue + q=seedq.copy();q[active]=best[3];checker.deadline=time.monotonic()+3600 + if not checker.check(q,base):continue + travel=float(np.linalg.norm(base[:2,3]-livebase[:2,3]));results.append(dict(score=travel+best[0]-min(gaps),goal=goal.tolist(),footprint_xy=np.asarray(fp.exterior.coords)[:-1].tolist(),gaps_m=[float(x) for x in gaps],extra_inset_m=float(extra),base=base.tolist(),q=q.tolist(),place_initial20=q[ids].tolist(),position_error_m=best[1],rotation_error_rad=best[2],travel_m=travel)) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case9_item2_polar_endpoint.v1','read_only':True,'status_at_probe':s,'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:80]};p=W/'diagnostics/LAYOUT09_ITEM02_POLAR_ENDPOINT.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:5]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py new file mode 100644 index 0000000000000000000000000000000000000000..ae3ef3f4b6930011eeea9959bcf1451ea444ce0f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +"""Read-only case9 item2 repack search from the frozen native boundary. + +The earlier station grid proved IK only at the authored slot, which the two +measured supported objects now occupy. This probe translates each grounded +IK solution and its station together into geometry-valid free tray space, +then performs the normal full carry, placement and release-sweep checks. +""" +import json, math, os, pathlib, sys, time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [str(W), str(R / "tools/family3"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "work/place_training_tray_20260918"), + str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance, plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + + +def mat(pose): + out = np.eye(4); out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + assert status.get("status") == "WAITING_FOR_PLANNING_REPAIR" and status.get("item_index") == 2 + job = json.loads((C / "job.json").read_text()); item = job["items"][2] + cp_path = C / "rollout/checkpoint_60hz.npz"; cp = np.load(cp_path, allow_pickle=True) + names = cp["dof_names"].tolist(); live_q = cp["q"][-1].astype(float); live_base = mat(cp["base_pose"][-1]) + hi = cp["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + ti = cp["target_paths"].tolist().index(item["target"]) + live_target = mat(cp["target_pose_all"][-1, ti]) + held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ live_target + controls = np.load(item["controls_file"]); vertices = controls["target_local"] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2]; prior = np.load(exact["prior_controls"]) + ids = [names.index(n) for n in controls["names"].tolist()] + active = [names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + grippers = [i for i, n in enumerate(names) if "gripper_" in n] + + stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, held, prop_poses) + + obstacles = [] + for i in range(2): + pm = mat(cp["target_pose_all"][-1, i]); pv = np.load(job["items"][i]["controls_file"])["target_local"] + pts = (np.c_[pv, np.ones(len(pv))] @ pm.T)[:, :2] + obstacles.append(Polygon(pts[ConvexHull(pts).vertices])) + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + + grid_path = W / "diagnostics/LAYOUT09_ITEM02_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + raw = [] + # A 3 mm tray grid, retaining at least 12 mm from both measured objects. + minx, miny, maxx, maxy = floor.bounds + for entry_index, entry in enumerate(grid): + source_goal = np.asarray(entry["goal"], float); yaw = float(Rotation.from_matrix(source_goal[:3, :3]).as_euler("xyz")[2]) + local = vertices @ source_goal[:3, :3].T + hull = ConvexHull(local[:, :2]); local_hull = local[hull.vertices, :2] + for x in np.arange(minx, maxx + 1e-6, .003): + for y in np.arange(miny, maxy + 1e-6, .003): + footprint = Polygon(local_hull + [x, y]); gaps = [footprint.distance(o) for o in obstacles] + inset_extra = floor.boundary.distance(footprint) + if not floor.covers(footprint) or min(gaps) <= .012 or inset_extra < .0005: + continue + delta = np.array([x - source_goal[0, 3], y - source_goal[1, 3]]) + base = np.asarray(entry["base"], float).copy(); base[:2, 3] += delta + goal = source_goal.copy(); goal[:2, 3] = [x, y] + # Prefer clearance and short base travel, while preserving diverse stations. + travel = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3])) + score = travel - 2.0 * min(gaps) - .5 * inset_extra + float(entry["score"]) + raw.append((score, entry_index, goal, base, footprint, gaps, inset_extra, entry)) + raw.sort(key=lambda x: x[0]) + # Keep spatially diverse candidates so a single blocked station does not dominate. + candidates = [] + for candidate in raw: + goal, base = candidate[2], candidate[3] + goal_yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + def same_pose(c): + other_yaw = float(Rotation.from_matrix(c[2][:3, :3]).as_euler("xyz")[2]) + dyaw = abs(math.atan2(math.sin(goal_yaw - other_yaw), math.cos(goal_yaw - other_yaw))) + return np.linalg.norm(goal[:2, 3] - c[2][:2, 3]) < .012 and dyaw < math.radians(4) + if any(same_pose(c) for c in candidates): + continue + candidates.append(candidate) + if len(candidates) >= 60: break + + attempts = []; accepted = None + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + for rank, candidate in enumerate(candidates): + _, entry_index, goal, base, footprint, gaps, inset_extra, entry = candidate + q = live_q.copy(); q[ids] = prior["place_initial"]; q[active] = np.asarray(entry["q7"], float); q[grippers] = live_q[grippers] + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + checker.deadline = time.monotonic() + 1800 + if not checker.check(q, base): + attempts.append({"rank": rank, "stage": "destination_clearance", "detail": checker.last}); continue + object_pose = robot.fk(names, q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": goal[:2, 3].tolist(), "yaw_rad": yaw, + "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": .001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), + "reserved_tray_footprints": [], "require_release_sweep": True} + try: + rs = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, q, base, object_pose, vertices, item_job, + obstacles, pose_check, world_check=checker, release_sweep=rs) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [.25, .45, .15, .05]} + carry_job.pop("carry_route_seed", None) + compact, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, + item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact, q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_place_initial", "detail": checker.last}); continue + except Exception as exc: + attempts.append({"rank": rank, "stage": "full", "error": repr(exc)}); continue + accepted = {"rank": rank, "grid_entry_index": entry_index, "slot": slot, + "destination_xyz": [float(goal[0, 3]), float(goal[1, 3]), float(job["tray_floor_z"])], + "place_base": base.tolist(), "place_initial20": q[ids].tolist(), + "geometry": {"actual_completed_gaps_m": [float(v) for v in gaps], + "tray_inset_m": .008, "extra_inset_m": float(inset_extra)}, + "placement_plan_frames": len(plan), "placement_report": report, + "retreat_q": retreat.tolist(), "reposition": {"compact_q": compact.tolist(), + "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, "full_world_checks": True} + attempts.append({"rank": rank, "stage": "passed"}); break + + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks"): + raise RuntimeError("native boundary changed") + result = {"schema": "multi_item.case9_item2_repack_full.v1", "read_only": True, + "status_at_probe": status, "evidence": {"checkpoint": str(cp_path), "grid": str(grid_path)}, + "geometry_raw_count": len(raw), "candidate_count": len(candidates), + "accepted": accepted, "attempts": attempts, "native_validation_required": True} + out = W / "diagnostics/LAYOUT09_ITEM02_REPACK_FULL_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_raw_count": len(raw), "accepted": accepted, + "attempts": attempts}, indent=2)) + if accepted is None: raise SystemExit(2) + + +if __name__ == "__main__": main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7ae631710e08218817bdf46f9fdfb672e73b96 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case9_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT09_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_current_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_current_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..ed1e6ea39ab2277fb1362eb5eda206717eba49de --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_current_live_place.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe for a frozen M2 boundary. + +The accepted-source correspondence comes from the immutable M1 preparation +record. Every motion and collision check uses the current M2 checkpoint, +scene, live grasp transform, grounded base height, and all current props. +""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +OLD = W.parent / "shelf_to_table_multi10_20261005" +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = OLD / f"cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX + 1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + by_yaw = {yaw: [] for yaw in yaws} + for candidate in candidates: + by_yaw[candidate[3]].append(candidate) + candidates = [bucket[rank] for rank in range(max(map(len, by_yaw.values()), default=0)) for bucket in by_yaw.values() if rank < len(bucket)] + print("geometry candidates", len(candidates), flush=True) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + print("station", rank, x, y, yaw, flush=True) + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX + 1:]], "placement_candidate_budget": 20} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(v) for v in gaps[:ITEM_INDEX]], "future_gaps_m": [float(v) for v in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + "retreat_native_world_validation_required": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.current_exact_source_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "old_exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_CURRENT_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py new file mode 100644 index 0000000000000000000000000000000000000000..750b4b9d596d8d7ccc22fe7397d7f1f78a25e25b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py @@ -0,0 +1,48 @@ +"""Probe transfer of the exact accepted chain landing for one case's first item.""" +import sys,os,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +from scipy.spatial import ConvexHull +W=Path(__file__).resolve().parent;R=W.parents[1];OLD=R/'work/shelf_to_table50_20261001' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from geometry_transfer import registration +from shapely.geometry import Polygon +from shapely import affinity + +cid=int(sys.argv[1]);case=W/'cases'/f'layout_{cid:02d}';job=json.loads((case/'job.json').read_text());item=job['items'][0];prior_path=next(p for p in sorted((OLD/'cases').glob('layout_*/job.json')) if json.loads(p.read_text()).get('pick_source')==item['pick_source']);prior=json.loads(prior_path.read_text());oldstage=Usd.Stage.Open(prior['layout']);newstage=Usd.Stage.Open(job['layout']);delta,_=registration(oldstage,newstage,'/World/Assets/CoffeeTable/Mesh') +oldgoal=np.asarray(json.loads((prior_path.parent/'rollout/PLACEMENT_PLAN.json').read_text())['goal']);goal=delta@oldgoal;base=delta@np.asarray(prior['place_base']);base[2,3]=np.asarray(job['initial_base'])[2,3] +data=np.load(item['controls_file']);verts=data['target_local'];foot=Polygon((verts@goal[:3,:3].T)[ConvexHull((verts@goal[:3,:3].T)[:,:2]).vertices,:2]+goal[:2,3]);floor=Polygon(job['tray_polygon_xy']);future=[Polygon(x['placement_slot']['footprint_xy']) for x in job['items'][1:]];gaps=[foot.distance(x) for x in future] +stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];selfcheck=build(stage);pick=Path(item['pick_source']);z=np.load(pick/'recording_20hz.npz');names=z['robot_dof_names'].tolist();ids=[names.index(n) for n in data['names']] +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +ti=z['rigid_body_paths'].tolist().index(item['target']);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link');held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]);q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:];obj=robot.fk(names,q,base)[robot.root+'/gripper_'+item['hand']+'_base_link']@held +yaw=float(math.atan2(goal[1,0],goal[0,0]));first_slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':np.asarray(foot.exterior.coords[:-1]).tolist(),'tolerance_m':.004};proposed=[first_slot];chosen=[foot] +# Repack future reservations around the proven first landing when the original packing overlaps it. +if any(g<=.012 for g in gaps): + pts=np.asarray(job['tray_polygon_xy']);center=np.asarray(floor.centroid.coords[0]);vals,vecs=np.linalg.eigh(np.cov((pts-center).T));u=vecs[:,np.argmax(vals)];v=np.array([-u[1],u[0]]);baseyaw=math.atan2(u[1],u[0]);inside=floor.buffer(-.008) + def search(i): + if i==len(job['items']):return True + it=job['items'][i];local=np.load(it['controls_file'])['target_local'][:,:2];poly=Polygon(local[ConvexHull(local).vertices]) + for vv in [-.07,-.045,-.02,.02,.045,.07]: + for uu in [-.12,-.08,-.04,0,.04,.08,.12]: + xy=center+uu*u+vv*v + for theta in [baseyaw,baseyaw+math.pi/2,baseyaw+math.pi,baseyaw-math.pi/2]: + p=affinity.translate(affinity.rotate(poly,theta*180/math.pi,origin=(0,0)),xy[0],xy[1]) + if not inside.covers(p) or any(p.distance(q)<=.012 for q in chosen):continue + chosen.append(p);proposed.append({'xy':xy.tolist(),'yaw_rad':theta,'footprint_xy':np.asarray(p.exterior.coords[:-1]).tolist(),'tolerance_m':.012}) + if search(i+1):return True + chosen.pop();proposed.pop() + return False + if not search(1):raise RuntimeError('Cannot repack future slots around exact accepted first landing') +ij={**job,**item,'place_base':base.tolist(),'destination_xyz':goal[:3,3].tolist(),'placement_slot':first_slot,'reserved_tray_footprints':[x['footprint_xy'] for x in proposed[1:]]} +result={'case':cid,'source_prior_job':str(prior_path),'base':base.tolist(),'goal':goal.tolist(),'slot':ij['placement_slot'],'proposed_slots':proposed,'floor_inset8_covers':floor.buffer(-.008).covers(foot),'future_gaps_before_repack':gaps,'future_gaps_after_repack':[chosen[a].distance(chosen[b]) for a in range(len(chosen)) for b in range(a+1,len(chosen))]} +try: + plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,[],selfcheck);result.update(passed=True,frames=len(plan),report=report) +except Exception as exc:result.update(passed=False,error=str(exc)) +(case/'EXACT_CHAIN_LANDING_PROPOSAL.json').write_text(json.dumps(result,indent=2,default=lambda x:x.tolist())+'\n');print(json.dumps(result,default=lambda x:x.tolist())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..7bad7bf54c81d8608e9acea5d61ddd500d96e02b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + grid_path = W / f"diagnostics/LAYOUT{CASE_INDEX:02d}_ITEM{ITEM_INDEX:02d}_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + candidates = [] + for entry in grid: + goal = np.asarray(entry["goal"], dtype=float) + x, y = map(float, goal[:2, 3]) + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + points = vertices @ goal[:3, :3].T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps, default=1.0) <= 0.012: + continue + base = np.asarray(entry["base"], dtype=float) + transfer = goal @ np.linalg.inv(accepted_goal) + candidates.append((float(entry["score"]), x, y, yaw, footprint, gaps, goal, base, transfer)) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.station_ik_grid_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_GRID_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py new file mode 100644 index 0000000000000000000000000000000000000000..ba7fb22391b3a36f8364f9b13ef4dba057e2e55b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + grid_path = W / f"diagnostics/LAYOUT{CASE_INDEX:02d}_ITEM{ITEM_INDEX:02d}_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + candidates = [] + for entry in grid: + goal = np.asarray(entry["goal"], dtype=float) + x, y = map(float, goal[:2, 3]) + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + points = vertices @ goal[:3, :3].T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps, default=1.0) <= 0.012: + continue + base = np.asarray(entry["base"], dtype=float) + transfer = goal @ np.linalg.inv(accepted_goal) + candidates.append((float(entry["score"]), x, y, yaw, footprint, gaps, goal, base, transfer, entry)) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer, entry = candidate + # The station grid's collision-independent IK is the endpoint seed. + # Retain all non-active joints from the exact accepted source pair and + # preserve every measured gripper DOF from the live grasp. + q_destination = exact_q.copy() + prefix = 2 if item["hand"] == "l" else 6 + active = [names.index(f"idx{prefix}{k}_arm_{item['hand']}_joint{k}") for k in range(1, 8)] + q_destination[active] = np.asarray(entry["q7"], dtype=float) + q_destination[grippers] = live_q[grippers] + checker.deadline = time.monotonic() + 1800 + if not checker.check(q_destination, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, q_destination, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True, + # The grid already selected an exact supported slot/yaw. + # Five entries cover the solver's direct equivalent-yaw set; + # random 1 mm jitter adds no meaningful geometry here. + "placement_candidate_budget": 5} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, q_destination, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, q_destination, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": q_destination[ids].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.station_ik_grid_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_GRID_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..06b5c56947afe3e42cd3682d077685816ab3c803 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:300]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.exact_source_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place_fast.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..0b212f910bda3495c418f689fe06f244864b0cc1 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_live_place_fast.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # Cover station orientations before spending time on nearby grid offsets. + by_yaw = {yaw: [] for yaw in yaws} + for candidate in candidates: by_yaw[candidate[3]].append(candidate) + candidates = [bucket[rank] for rank in range(max(map(len,by_yaw.values()),default=0)) for bucket in by_yaw.values() if rank.012 and extra>.0003: + g=np.eye(4);g[:3,:3]=Rz;g[:2,3]=xy;g[2,3]=j['tray_floor_z']+.002-local[:,2].min();goals.append((g,fp,gaps,extra)) +# Deduplicate equivalent circular/symmetric goals. +unique=[] +for x in goals: + if not any(np.linalg.norm(x[0][:2,3]-y[0][:2,3])<1e-6 and abs(math.atan2(math.sin(Rotation.from_matrix(x[0][:3,:3]).as_euler('xyz')[2]-Rotation.from_matrix(y[0][:3,:3]).as_euler('xyz')[2]),math.cos(Rotation.from_matrix(x[0][:3,:3]).as_euler('xyz')[2]-Rotation.from_matrix(y[0][:3,:3]).as_euler('xyz')[2])))<1e-6 for y in unique):unique.append(x) +goals=unique;results=[];tested=0 +for gi,(goal,fp,gaps,extra) in enumerate(goals): + for angle in np.deg2rad(np.arange(-180,181,30)): + for radius in [.52,.62,.72,.82]: + bx=goal[0,3]+radius*math.cos(angle);by=goal[1,3]+radius*math.sin(angle);face=math.atan2(goal[1,3]-by,goal[0,3]-bx) + for da in np.deg2rad([-12,0,12]): + base=np.eye(4);base[:3,:3]=Rotation.from_euler('z',face+da).as_matrix();base[:3,3]=[bx,by,livebase[2,3]];arm=Arm(robot,names,seedq,base,robot.root+f'/gripper_{hand}_base_link',active);target=goal@invheld;best=None + for seed in [seedq[active],liveq[active]]: + fit=least_squares(lambda x:arm.evaluate(x,target,seed)[0],np.clip(seed,lo,up),jac=lambda x:arm.evaluate(x,target,seed)[1],bounds=(lo,up),max_nfev=110);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());score=pe+.1*re + if best is None or score.003 or best[2]>.03:continue + q=seedq.copy();q[active]=best[3];q[grips]=liveq[grips] + if not checker.check(q,base):continue + travel=float(np.linalg.norm(base[:2,3]-livebase[:2,3]));results.append(dict(score=travel+best[0]-min(gaps,default=0),goal=goal.tolist(),footprint_xy=np.asarray(fp.exterior.coords)[:-1].tolist(),gaps_m=[float(x) for x in gaps],actual_gap_count=item_index,extra_inset_m=float(extra),base=base.tolist(),q=q.tolist(),place_initial20=q[ids].tolist(),position_error_m=best[1],rotation_error_rad=best[2],travel_m=travel)) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.polar_endpoint.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:100]};p=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:5]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic.py new file mode 100644 index 0000000000000000000000000000000000000000..98189940d673ec49a6a3574958df2ab03d745419 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Full read-only carry/place/release qualification of polar endpoint seeds.""" +import json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];case_index=int(sys.argv[1]);item_index=int(sys.argv[2]);C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json'));it=j['items'][item_index];cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage);controls=np.load(it['controls_file']);verts=controls['target_local'];ids=[names.index(n) for n in controls['names'].tolist()] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link");ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obs=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +ep=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';seeds=json.load(open(ep))['top'];source_layout=int(j.get('source_layout_id',j['layout_id']));release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json'));attempts=[];accepted=None +for rank,seed in enumerate(seeds[:30]): + q=np.asarray(seed['q'],float);base=np.asarray(seed['base'],float);goal=np.asarray(seed['goal'],float);yaw=float(Rotation.from_matrix(goal[:3,:3]).as_euler('xyz')[2]);slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':seed['footprint_xy'],'tolerance_m':.001};ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':5};checker.deadline=time.monotonic()+1800 + if not checker.check(q,base):attempts.append({'rank':rank,'stage':'destination','detail':checker.last});continue + obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held + try: + rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800);checker.deadline=time.monotonic()+1800;plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obs,pose_check,world_check=checker,release_sweep=rs) + except Exception as e:attempts.append({'rank':rank,'stage':'placement','error':repr(e)});continue + cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) + try:compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) + except Exception as e:attempts.append({'rank':rank,'stage':'carry','error':repr(e)});continue + checker.deadline=time.monotonic()+1800 + if not checker.segment(compact,q,base,base):attempts.append({'rank':rank,'stage':'compact_bridge','detail':checker.last});continue + accepted={'rank':rank,'slot':slot,'destination_xyz':[float(goal[0,3]),float(goal[1,3]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':seed['gaps_m'][:item_index],'future_gaps_m':seed['gaps_m'][item_index:],'extra_inset_m':seed['extra_inset_m'],'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True};attempts.append({'rank':rank,'stage':'passed'});break +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.polar_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'endpoint':str(ep),'checkpoint':str(cp_path)},'accepted':accepted,'attempts':attempts,'native_validation_required':True};p=C/f'CASE{case_index}_ITEM{item_index:02d}_POLAR_FULL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'accepted':accepted,'attempts':attempts},indent=2));raise SystemExit(0 if accepted else 2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py new file mode 100644 index 0000000000000000000000000000000000000000..78e7173ff81db771c2fdabd476054638084fff94 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Full read-only carry/place/release qualification of polar endpoint seeds.""" +import json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];case_index=int(sys.argv[1]);item_index=int(sys.argv[2]);C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json'));it=j['items'][item_index];cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage);controls=np.load(it['controls_file']);verts=controls['target_local'];ids=[names.index(n) for n in controls['names'].tolist()] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link");ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obs=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +ep=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';seeds=json.load(open(ep))['top'];source_layout=int(j.get('source_layout_id',j['layout_id']));release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json'));attempts=[];accepted=None +for rank,seed in enumerate(seeds[30:80], start=30): + q=np.asarray(seed['q'],float);base=np.asarray(seed['base'],float);goal=np.asarray(seed['goal'],float);yaw=float(Rotation.from_matrix(goal[:3,:3]).as_euler('xyz')[2]);slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':seed['footprint_xy'],'tolerance_m':.001};ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':5};checker.deadline=time.monotonic()+1800 + if not checker.check(q,base):attempts.append({'rank':rank,'stage':'destination','detail':checker.last});continue + obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held + try: + rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800);checker.deadline=time.monotonic()+1800;plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obs,pose_check,world_check=checker,release_sweep=rs) + except Exception as e:attempts.append({'rank':rank,'stage':'placement','error':repr(e)});continue + cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) + try:compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) + except Exception as e:attempts.append({'rank':rank,'stage':'carry','error':repr(e)});continue + checker.deadline=time.monotonic()+1800 + if not checker.segment(compact,q,base,base):attempts.append({'rank':rank,'stage':'compact_bridge','detail':checker.last});continue + accepted={'rank':rank,'slot':slot,'destination_xyz':[float(goal[0,3]),float(goal[1,3]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':seed['gaps_m'][:item_index],'future_gaps_m':seed['gaps_m'][item_index:],'extra_inset_m':seed['extra_inset_m'],'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True};attempts.append({'rank':rank,'stage':'passed'});break +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.polar_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'endpoint':str(ep),'checkpoint':str(cp_path)},'accepted':accepted,'attempts':attempts,'native_validation_required':True};p=C/f'CASE{case_index}_ITEM{item_index:02d}_POLAR_FULL_TAIL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'accepted':accepted,'attempts':attempts},indent=2));raise SystemExit(0 if accepted else 2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_precision_return_controller.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_precision_return_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..42c80da20fab4e8c8cf9e703b4419519ec1bfb66 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_precision_return_controller.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +"""Pure-kinematic verification of tighter controllers on recorded return plans.""" +import json +import math +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_01" + + +def angle_delta(target, current): + return math.atan2(math.sin(target - current), math.cos(target - current)) + + +class PrecisionController: + """Same stop-turn-drive law and caps, with pickup-safe terminal precision.""" + def __init__(self, primitive): + self.primitive = primitive + self.speed = 0.0 + self.done = False + + def step(self, current, dt=1 / 60): + goal = np.asarray(self.primitive["goal"], dtype=float) + yaw = float(current[2]) + heading = np.array([math.cos(yaw), math.sin(yaw)]) + if self.primitive["kind"] == "turn": + error = angle_delta(goal[2], yaw) + desired = math.copysign(min(0.25, max(0.00002, 1.5 * abs(error))), error) if abs(error) > 0.000002 else 0.0 + self.speed += float(np.clip(desired - self.speed, -0.3 * dt, 0.3 * dt)) + self.done = abs(error) < 0.000005 and abs(self.speed) < 0.000002 + return np.array([0.0, 0.0, self.speed]) + error = goal[:2] - np.asarray(current[:2]) + along = float(error @ heading) + lateral = float(error @ np.array([-heading[1], heading[0]])) + if abs(lateral) > 0.015: + raise RuntimeError(f"Straight drive lateral tracking error {lateral:.6f} m") + if abs(angle_delta(goal[2], yaw)) > 0.01: + raise RuntimeError("Straight drive heading drift") + desired = math.copysign(min(0.18, max(0.0001, min(1.5 * abs(along), 0.8 * math.sqrt(2 * 0.12 * abs(along))))), along) if abs(along) > 0.00001 else 0.0 + self.speed += float(np.clip(desired - self.speed, -0.12 * dt, 0.12 * dt)) + self.done = abs(along) < 0.00002 and abs(self.speed) < 0.000005 + return np.r_[heading * self.speed, 0.0] + + +def simulate(primitives, start, controller_class=PrecisionController, dt=1 / 60): + pose = np.asarray(start, dtype=float).copy() + rows = [] + for index, primitive in enumerate(primitives): + controller = controller_class(primitive) + for tick in range(90 * 60): + velocity = controller.step(pose, dt) + pose[:2] += velocity[:2] * dt + pose[2] += velocity[2] * dt + if controller.done: + break + else: + raise RuntimeError(f"primitive {index} timed out") + goal = np.asarray(primitive["goal"]) + rows.append({ + "primitive": index, + "kind": primitive["kind"], + "return_stage": primitive.get("return_stage"), + "ticks": tick + 1, + "seconds": (tick + 1) * dt, + "xy_error_m": float(np.linalg.norm(pose[:2] - goal[:2])), + "along_error_m": float((goal[:2] - pose[:2]) @ np.array([math.cos(pose[2]), math.sin(pose[2])])), + "yaw_error_rad": float(abs(angle_delta(goal[2], pose[2]))), + "terminal_speed": float(abs(controller.speed)), + }) + return pose, rows + + +def main(): + recording = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + reports = [] + for item_index in (0, 1): + plan_path = C / f"rollout/return_primitives.item_{item_index:02d}.json" + primitives = json.loads(plan_path.read_text()) + indices = np.flatnonzero((recording["item_index"] == item_index) & (recording["phase"] == f"item_{item_index:02d}_empty_return")) + pose7 = recording["base_pose"][indices[0]] + start = np.r_[pose7[:2], Rotation.from_quat(pose7[3:]).as_euler("xyz")[2]] + final, rows = simulate(primitives, start) + goal = np.asarray(primitives[-1]["goal"]) + reports.append({ + "item": item_index, + "plan": str(plan_path), + "observed_start": start.tolist(), + "simulated_final": final.tolist(), + "final_goal": goal.tolist(), + "final_xy_error_m": float(np.linalg.norm(final[:2] - goal[:2])), + "final_yaw_error_rad": float(abs(angle_delta(goal[2], final[2]))), + "total_seconds": float(sum(row["seconds"] for row in rows)), + "max_endpoint_xy_error_m": max(row["xy_error_m"] for row in rows), + "max_endpoint_yaw_error_rad": max(row["yaw_error_rad"] for row in rows), + "primitives": rows, + }) + # Stress the same final shelf approach with the case-1 observed heading + # residual. A precise preceding turn removes it before straight motion. + item1 = json.loads((C / "rollout/return_primitives.item_01.json").read_text()) + final_drive = item1[-1] + standoff = np.asarray(item1[-2]["goal"], dtype=float) + perturbed = standoff.copy() + perturbed[2] += 0.0008466414784971121 + sequence = [{"kind": "turn", "goal": standoff.tolist(), "return_stage": "precision_preapproach"}, final_drive] + stress_final, stress_rows = simulate(sequence, perturbed) + stress_goal = np.asarray(final_drive["goal"]) + result = { + "schema": "multi_item.precision_return_controller_probe.v1", + "read_only": True, + "controller": { + "turn_deadband_rad": 0.000002, + "turn_done_error_rad": 0.000005, + "turn_done_speed_rad_s": 0.000002, + "turn_min_speed_rad_s": 0.00002, + "drive_deadband_m": 0.00001, + "drive_done_error_m": 0.00002, + "drive_done_speed_m_s": 0.000005, + "drive_min_speed_m_s": 0.0001, + "speed_and_acceleration_caps_unchanged": True, + }, + "recorded_plan_exact_kinematics": reports, + "heading_residual_stress": { + "initial_yaw_error_rad": 0.0008466414784971121, + "final_xy_error_m": float(np.linalg.norm(stress_final[:2] - stress_goal[:2])), + "final_yaw_error_rad": float(abs(angle_delta(stress_goal[2], stress_final[2]))), + "seconds": float(sum(row["seconds"] for row in stress_rows)), + "primitives": stress_rows, + }, + "recommended_integration": "Use PrecisionController only for primitives carrying return_stage. Keep the same collision-screened plan and stop-turn-drive primitives. Require a final measured gate <=0.0005 m and <=0.0001 rad before starting source pickup; otherwise freeze at a planning boundary.", + "limits": "Exact-kinematic verification does not prove native wheel/contact tracking. Fresh native physics must confirm the measured terminal gate.", + } + result["passed"] = bool( + all(report["final_xy_error_m"] <= 0.0001 and report["final_yaw_error_rad"] <= 0.00001 and all(row["seconds"] < 90 for row in report["primitives"]) for report in reports) + and result["heading_residual_stress"]["final_xy_error_m"] <= 0.0001 + and result["heading_residual_stress"]["final_yaw_error_rad"] <= 0.00001 + ) + out = W / "PRECISION_RETURN_CONTROLLER_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if not result["passed"]: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..6a761547d79fb4ba09080c4a017f8e933ae14dda --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Read-only left-hand regression against layout10 item02.""" +import json +import os +import pathlib +import sys + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from native_planner import build # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + case = W / "cases/layout_10" + job = json.loads((case / "job.json").read_text()) + item = job["items"][2] + data = np.load(case / "rollout/checkpoint_60hz.npz", allow_pickle=True) + phase = data["phase"].astype(str) + release_rows = np.flatnonzero(phase == "item_02_place_release") + retreat_rows = np.flatnonzero(phase == "item_02_release_retreat") + if not len(release_rows) or not len(retreat_rows): + raise RuntimeError("layout10 release evidence is incomplete") + landing_row = int(release_rows[0] - 1) + retreat_row = int(retreat_rows[-1]) + names = data["dof_names"].tolist() + target_index = data["target_paths"].tolist().index(item["target"]) + target_pose = matrix(data["target_pose_all"][landing_row, target_index]) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(data["rigid_body_paths"], data["prop_pose"][landing_row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + controls = np.load(item["controls_file"]) + checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=build(stage), + deadline_s=1800, + ) + passed, report = checker.check( + data["q"][landing_row], + data["q"][retreat_row], + matrix(data["base_pose"][landing_row]), + target_pose, + ) + result = { + "schema": "ziche.release_sweep_left_regression.v1", + "passed": bool(passed), + "expected": "CLEAR", + "observed": report, + "historical_case": 10, + "item_index": 2, + "landing_tick": landing_row + 1, + "retreat_end_tick": retreat_row + 1, + "hand": "l", + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_LEFT_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if not passed: + raise SystemExit("release sweep rejected the left-hand clear example") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py new file mode 100644 index 0000000000000000000000000000000000000000..38375b702410a94fb647f9821d9b7201002596c8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Read-only negative regression against layout10's known Coke strike.""" +import json +import os +import pathlib +import sys + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from native_planner import build # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + case = W / "cases/layout_10" + job = json.loads((case / "job.json").read_text()) + item = job["items"][1] + data = np.load(case / "rollout/checkpoint_60hz.npz", allow_pickle=True) + phase = data["phase"].astype(str) + release_rows = np.flatnonzero(phase == "item_01_place_release") + retreat_rows = np.flatnonzero(phase == "item_01_release_retreat") + if not len(release_rows) or not len(retreat_rows): + raise RuntimeError("layout10 release evidence is incomplete") + landing_row = int(release_rows[0] - 1) + retreat_row = int(retreat_rows[-1]) + names = data["dof_names"].tolist() + target_index = data["target_paths"].tolist().index(item["target"]) + target_pose = matrix(data["target_pose_all"][landing_row, target_index]) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(data["rigid_body_paths"], data["prop_pose"][landing_row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + controls = np.load(item["controls_file"]) + checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=build(stage), + deadline_s=1800, + ) + passed, report = checker.check( + data["q"][landing_row], + data["q"][retreat_row], + matrix(data["base_pose"][landing_row]), + target_pose, + ) + result = { + "schema": "ziche.release_sweep_negative_regression.v1", + "passed": not passed, + "expected": "BLOCKED", + "observed": report, + "historical_case": 10, + "item_index": 1, + "landing_tick": landing_row + 1, + "retreat_end_tick": retreat_row + 1, + "known_native_contact": "gripper_r_inner_link4 versus previously placed coke_c3", + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_NEGATIVE_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if passed: + raise SystemExit("release sweep failed to reject the known layout10 strike") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..6936bf3e4c6836228a050d8d882d862417887ff8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Read-only regression of ReleaseSweep against the qualified layout02 repair.""" +import hashlib +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def main(): + case = W / "cases/layout_02" + proposal_path = W / "diagnostics/LAYOUT02_ITEM01_NOMINAL_STATION_FULL_PROPOSAL.json" + proposal = json.loads(proposal_path.read_text()) + accepted = proposal["accepted"] + job_path = case / "job.json" + job = json.loads(job_path.read_text()) + item = job["items"][1] + checkpoint_path = W / "diagnostics/release_sweep_case2_checkpoint_tick17975_plus.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + row = int(proposal["status_at_probe"]["control_ticks"]) - 1 + if row < 0 or row >= len(checkpoint["q"]): + raise RuntimeError("qualified boundary row is absent from the checkpoint") + names = checkpoint["dof_names"].tolist() + q = checkpoint["q"][row].astype(float) + controls = np.load(item["controls_file"]) + ids = [names.index(name) for name in controls["names"].tolist()] + q[ids] = np.asarray(accepted["place_initial20"], dtype=float) + # The qualifying probe preserves the measured passive gripper state. + q[[i for i, name in enumerate(names) if "gripper_" in name]] = checkpoint["q"][row, [i for i, name in enumerate(names) if "gripper_" in name]] + base = np.asarray(accepted["place_base"], dtype=float) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + hand_link = robot.root + f"/gripper_{item['hand']}_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][row, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][row, hand_index])) @ live_target + object_pose = robot.fk(names, q, base)[hand_link] @ held + world_check = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + world_check.deadline = time.monotonic() + 1800 + release_checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=pose_check, + deadline_s=1800, + ) + completed_item = job["items"][0] + completed_local = np.load(completed_item["controls_file"])["target_local"] + completed_pose = matrix(checkpoint["target_pose_all"][row, 0]) + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles = [Polygon(completed_world[ConvexHull(completed_world).vertices])] + item_job = {**job, **item, "placement_slot": accepted["slot"], "place_base": base.tolist(), "require_release_sweep": True} + item_job["reserved_tray_footprints"] = [job["items"][2]["placement_slot"]["footprint_xy"]] + started = time.monotonic() + planned, retreat, placement_report = solve( + robot, + names, + q, + base, + object_pose, + controls["target_local"], + item_job, + obstacles, + pose_check, + world_check=world_check, + release_sweep=release_checker, + ) + report = placement_report["release_sweep"] + passed = report["status"] == "CLEAR" + result = { + "schema": "ziche.release_sweep_regression.v1", + "passed": bool(passed), + "case": 2, + "item_index": 1, + "qualified_boundary_tick": row + 1, + "source": { + "proposal": str(proposal_path), + "proposal_sha256": sha(proposal_path), + "job": str(job_path), + "job_sha256": sha(job_path), + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": sha(checkpoint_path), + "release_sweep_sha256": sha(W / "release_sweep.py"), + "placement_sha256": sha(W / "placement.py"), + }, + "elapsed_s": time.monotonic() - started, + "placement_plan_frames": int(len(planned)), + "placement_candidate": int(placement_report["candidate"]), + "placement_goal": placement_report["goal"], + "retreat_q": retreat.tolist(), + "report": report, + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2), flush=True) + if not passed: + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_seed_full_generic.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_seed_full_generic.py new file mode 100644 index 0000000000000000000000000000000000000000..b63c55aae8adb715d2311a00deae64cee61661b8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_seed_full_generic.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +"""Qualify an immutable prior place seed against a current frozen boundary.""" +import hashlib,json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W=pathlib.Path(__file__).resolve().parent; R=W.parents[1] +case_index=int(sys.argv[1]); item_index=int(sys.argv[2]); seed_path=pathlib.Path(sys.argv[3]).resolve() +C=W/f"cases/layout_{case_index:02d}" +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +s=json.load(open(C/'STATUS.json')) +assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json')); it=j['items'][item_index] +seed_doc=json.load(open(seed_path)); seed=seed_doc.get('accepted') or seed_doc.get('proposal') +if not isinstance(seed,dict): raise ValueError('seed has no accepted/proposal object') +cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True) +names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]) +controls=np.load(it['controls_file']); ids=[names.index(n) for n in controls['names'].tolist()] +q=liveq.copy(); q[ids]=np.asarray(seed['place_initial20'],float) +# The live gripper command is evidence-bearing and must survive a posture seed. +for k,n in enumerate(names): + if 'gripper_' in n:q[k]=liveq[k] +base=np.asarray(seed['place_base'],float).copy();base[2,3]=livebase[2,3] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link") +ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]) +held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet +props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])} +stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) +verts=controls['target_local']; checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obstacles=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]); pv=np.load(j['items'][k]['controls_file'])['target_local']; pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2] + obstacles.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +slot=json.loads(json.dumps(seed['slot'])); fp=Polygon(slot['footprint_xy']) +gaps=[fp.distance(x) for x in obstacles]+[fp.distance(Polygon(x)) for x in reserved] +floor=Polygon(j['tray_polygon_xy']).buffer(-.008); inside=bool(floor.covers(fp)); inset=float(fp.distance(floor.boundary)) if inside else -1.0 +if not inside or (gaps and min(gaps)<=.012):raise RuntimeError(f'seed geometry invalid: inside={inside} gaps={gaps}') +ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':40} +checker.deadline=time.monotonic()+1800 +if not checker.check(q,base):raise RuntimeError('destination blocked: '+json.dumps(checker.last,default=str)) +obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held +source_layout=int(j.get('source_layout_id',j['layout_id'])) +release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json')) +rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800) +checker.deadline=time.monotonic()+1800 +plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obstacles,pose_check,world_check=checker,release_sweep=rs) +cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) +compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) +checker.deadline=time.monotonic()+1800 +if not checker.segment(compact,q,base,base):raise RuntimeError('compact bridge blocked: '+json.dumps(checker.last,default=str)) +accepted={'slot':slot,'destination_xyz':[float(slot['xy'][0]),float(slot['xy'][1]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':gaps[:item_index],'future_gaps_m':gaps[item_index:],'extra_inset_m':inset,'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True} +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.current_seed_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'seed':str(seed_path),'seed_sha256':hashlib.sha256(seed_path.read_bytes()).hexdigest(),'checkpoint':str(cp_path)},'accepted':accepted,'native_validation_required':True} +p=C/f'CASE{case_index}_ITEM{item_index:02d}_SEED_FULL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n') +print(json.dumps({'out':str(p),'accepted':accepted},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_slots.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_slots.py new file mode 100644 index 0000000000000000000000000000000000000000..270f339a7561d50098fcb7256cf0dd3b9eafb4ea --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_slots.py @@ -0,0 +1,32 @@ +"""Offline reachability proposal using actual accepted pickup grasps; not acceptance.""" +import sys,os,json,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from shapely.geometry import Polygon +case=W/'cases'/f'layout_{int(sys.argv[1]):02d}';job=json.loads((case/'job.json').read_text());stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];selfcheck=build(stage) +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +result=[];obstacles=[] +for k,item in enumerate(job['items']): + data=np.load(item['controls_file']);source=Path(item['pick_source']);z=np.load(source/'recording_20hz.npz');names=z['robot_dof_names'].tolist();ids=[names.index(n) for n in data['names']] + ti=z['rigid_body_paths'].tolist().index(item['target']);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link') + held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]) + q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:] + base=np.array(item['place_base']);obj=robot.fk(names,q,base)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + t=time.monotonic() + try: + plan,retreat,report=solve(robot,names,q,base,obj,data['target_local'],ij,obstacles,selfcheck) + row=dict(item=k,product=item['product'],passed=True,report=report) + except Exception as exc:row=dict(item=k,product=item['product'],passed=False,error=str(exc)) + row['elapsed']=time.monotonic()-t;result.append(row);print(json.dumps(row,default=lambda a:a.tolist()),flush=True) + obstacles.append(Polygon(item['placement_slot']['footprint_xy'])) + (case/'SLOT_REACHABILITY_PROPOSAL.json').write_text(json.dumps(dict(source='accepted_pickup_grasp_at_proposed_place_initial',native_validation_required=True,items=result),indent=2,default=lambda a:a.tolist())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..ef0fed349a88896fc28d724cd0a8568b051cac42 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_ik_grid.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Read-only grounded station/landing IK grid for a frozen place boundary.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent; R=W.parents[1] +case_index=int(sys.argv[1]); item_index=int(sys.argv[2]); C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==item_index +job=json.loads((C/'job.json').read_text());item=job['items'][item_index];hand=item['hand'];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index(f'/World/Robot/gripper_{hand}_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact_path=C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json' +if not exact_path.is_file():exact_path=R/f'work/shelf_to_table_multi10_20261005/cases/layout_{case_index:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json' +exact=json.loads(exact_path.read_text())['items'][item_index];prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +side='l' if hand=='l' else 'r'; prefix=2 if hand=='l' else 6; link=robot.root+f'/gripper_{side}_base_link'; active=[names.index(f'idx{prefix}{k}_arm_{side}_joint{k}') for k in range(1,8)]; jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 +off=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[off:off+7]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[off:off+7]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.12,.121,.03): + for dy in np.arange(-.12,.121,.03): + for da in np.deg2rad(np.arange(-12.1,12.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy;arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.grounded_station_ik_grid.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:80]};p=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_slots.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_slots.py new file mode 100644 index 0000000000000000000000000000000000000000..6adff0935f5310ece0aa392b329fc369a6b4b455 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_station_slots.py @@ -0,0 +1,69 @@ +"""Read-only station/slot reachability search using accepted held grasps.""" +import sys,os,json,time,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +from scipy.optimize import least_squares +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from geometry_transfer import registration +from shapely.geometry import Polygon +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +case=W/'cases'/f'layout_{int(sys.argv[1]):02d}'; job=json.loads((case/'job.json').read_text()); stage=Usd.Stage.Open(job['scene']) +room=Usd.Stage.Open(job['layout']); robot=Robot(ROBOT); robot.bodies=[]; selfcheck=build(stage) +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +def pose(spec): + m=np.eye(4);m[:3,3]=spec['robot']['base_xyz_m'];m[:3,:3]=Rotation.from_quat(np.asarray(spec['robot']['base_wxyz'])[[1,2,3,0]]).as_matrix();return m + +results=[]; obstacles=[] +for k,item in enumerate(job['items']): + data=np.load(item['controls_file']); pick=Path(item['pick_source']); z=np.load(pick/'recording_20hz.npz'); names=z['robot_dof_names'].tolist(); ids=[names.index(n) for n in data['names']] + ti=z['rigid_body_paths'].tolist().index(item['target']); hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link') + held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]) + q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:] + place=Path(item['place_source']); ps=json.loads((place/'episode.json').read_text()); pstage=Usd.Stage.Open(str(place/'scene.usda')) + delta,_=registration(pstage,room,'/World/Assets/CoffeeTable/Mesh'); demonstrated=delta@np.r_[ps['destination_xy_m'],ps['tray']['floor_z_m'],1.] + base=np.asarray(item['place_base'],float); shift=np.asarray(item['placement_slot']['xy'])-demonstrated[:2]; nominal=base.copy() + if item.get('station_alignment_policy')!='registered_source_destination_to_reserved_slot_v1': nominal[:2,3]+=shift + # Analytical translation preserves the accepted hand/object/table relationship. Bounded offsets cover registration and slot tolerance. + offsets=[(0,0),(-.10,0),(-.06,0),(-.03,0),(.03,0),(.06,0),(.10,0),(0,.03),(0,-.03),(0,.06),(0,-.06)] + attempts=[]; accepted=None; started=time.monotonic(); stations=[] + # The aligned, floor-safe nominal station is the strongest source-derived proposal; test it before screening alternatives. + candidate=nominal.copy();candidate[2,3]=float(np.asarray(job['initial_base'])[2,3]);obj=robot.fk(names,q,candidate)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'place_base':candidate.tolist(),'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + try: + plan,retreat,report=solve(robot,names,q,candidate,obj,data['target_local'],ij,obstacles,selfcheck);accepted={'place_base':candidate.tolist(),'station_shift_xy':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':0,'endpoint_score':None,'report':report,'plan_frames':len(plan)};attempts.append({'passed':True,'shift':accepted['station_shift_xy'],'yaw_offset_deg':0,'direct_nominal':True}) + except Exception as exc: attempts.append({'passed':False,'shift':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':0,'direct_nominal':True,'error':str(exc)[:500]}) + link=robot.root+'/gripper_'+item['hand']+'_base_link'; active=[names.index(f'idx{2 if item["hand"]=="l" else 6}{a}_arm_{item["hand"]}_joint{a}') for a in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')}; lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4; hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4; off=0 if item['hand']=='l' else 7;lo=np.maximum(lo,LOWER[off:off+7]+1e-4);hi=np.minimum(hi,UPPER[off:off+7]-1e-4) + vertices=data['target_local']; invheld=np.linalg.inv(held); slot=item['placement_slot']; thetas=[slot['yaw_rad'],slot['yaw_rad']+math.pi,slot['yaw_rad']+math.pi/2,slot['yaw_rad']-math.pi/2] + for yaw_deg in ([] if accepted else [0,-5,5,-10,10]): + for dx,dy in offsets: + candidate=nominal.copy();candidate[:2,3]+=np.array([dx,dy]);candidate[2,3]=float(np.asarray(job['initial_base'])[2,3]) + if yaw_deg:candidate[:3,:3]=Rotation.from_euler('z',math.radians(yaw_deg)).as_matrix()@candidate[:3,:3] + if math.hypot(dx,dy)>.15+1e-9:continue + fast=Arm(robot,names,q,candidate,link,active); best=1e9 + for theta in thetas: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',theta).as_matrix();goal[:2,3]=slot['xy'];goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min();g=goal@invheld;prev=q[active] + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=70); f=fast.forward(fit.x)[0];pe=np.linalg.norm(f[:3,3]-g[:3,3]);re=Rotation.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude();best=min(best,pe+0.2*re) + stations.append((best,yaw_deg,dx,dy,candidate)) + for endpoint_score,yaw_deg,dx,dy,candidate in sorted(stations,key=lambda x:x[0])[:8]: + obj=robot.fk(names,q,candidate)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'place_base':candidate.tolist(),'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + try: + plan,retreat,report=solve(robot,names,q,candidate,obj,data['target_local'],ij,obstacles,selfcheck) + accepted={'place_base':candidate.tolist(),'station_shift_xy':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score,'report':report,'plan_frames':len(plan)}; attempts.append({'passed':True,'shift':accepted['station_shift_xy'],'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score});break + except Exception as exc: attempts.append({'passed':False,'shift':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score,'error':str(exc)[:500]}) + row={'item':k,'product':item['product'],'passed':accepted is not None,'source_destination_registered_xy':demonstrated[:2].tolist(),'slot_xy':item['placement_slot']['xy'],'analytic_shift_xy':shift.tolist(),'accepted':accepted,'attempts':attempts,'elapsed_s':time.monotonic()-started};results.append(row);print(json.dumps(row,default=lambda a:a.tolist()),flush=True) + obstacles.append(Polygon(item['placement_slot']['footprint_xy'])) + if os.environ.get('PROBE_FIRST_ONLY')=='1': break +out={'schema':'multi_item.station_slot_reachability.v1','case':int(sys.argv[1]),'read_only_job':True,'method':'accepted held transform plus source destination aligned station; bounded 0.15m translation and 10deg yaw search','all_passed':all(x['passed'] for x in results),'items':results,'native_validation_required':True} +(case/'STATION_SLOT_REACHABILITY_PROPOSAL.json').write_text(json.dumps(out,indent=2,default=lambda a:a.tolist())+'\n') diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3f44bed09b9ebfd3a619ee071ec3ba82cabf18 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +"""Read-only IK rebase of Zbar675 hand waypoints for the measured case-1 base.""" +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from geometry_transfer import board_path, registration # noqa: E402 +from ik_solver import Arm # noqa: E402 +from motion_limits import LOWER, UPPER # noqa: E402 + +C = W / "cases/layout_01" +SRC = R / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000675" + + +def pose_matrix(pose, wxyz=False): + result = np.eye(4) + result[:3, 3] = pose[:3] + quat = np.asarray(pose[3:]) + if wxyz: + quat = quat[[1, 2, 3, 0]] + result[:3, :3] = Rotation.from_quat(quat).as_matrix() + return result + + +def main(): + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + controls = np.load(item["controls_file"]) + failed = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + full_state = np.load(C / "rollout/full_state.npz", allow_pickle=True) + names = source["robot_dof_names"].tolist() + action_names = controls["names"].tolist() + action_ids = [names.index(name) for name in action_names] + active = [names.index(f"idx2{i}_arm_l_joint{i}") for i in range(1, 8)] + hand_path = "/World/Robot/gripper_l_base_link" + source_hand_index = source["robot_link_paths"].tolist().index(hand_path) + target_index = source["rigid_body_paths"].tolist().index(item["target"]) + failed_pick = np.flatnonzero((failed["item_index"] == 2) & (failed["phase"] == "item_02_shelf_pick")) + actual_base = pose_matrix(failed["base_pose"][failed_pick[0]]) + desired_base = np.asarray(item["pick_base"], dtype=float) + + stage = Usd.Stage.Open(job["scene"]) + source_stage = Usd.Stage.Open(str(SRC / "scene.usda")) + room = Usd.Stage.Open(job["layout"]) + shelf_delta, shelf_reg = registration(source_stage, room, board_path(source_stage)) + robot = Robot(ROBOT) + robot.bodies = [] + joint_prims = {prim.GetName(): prim for prim in robot.stage.Traverse() if prim.GetAttribute("physics:lowerLimit")} + lo = np.array([joint_prims[names[i]].GetAttribute("physics:lowerLimit").Get() for i in active]) * np.pi / 180 + 1e-4 + hi = np.array([joint_prims[names[i]].GetAttribute("physics:upperLimit").Get() for i in active]) * np.pi / 180 - 1e-4 + lo = np.maximum(lo, LOWER[:7] + 1e-4) + hi = np.minimum(hi, UPPER[:7] - 1e-4) + + prop_poses = {} + for path, xyz, wxyz in zip(full_state["rigid_body_paths"], full_state["prop_xyz_m"][-1], full_state["prop_wxyz"][-1]): + prop_poses[str(path)] = pose_matrix(np.r_[xyz, wxyz], wxyz=True) + # Clearance builds the real released-room environment and excludes the task target. + initial_q = source["robot_joint_position_rad_or_m"][0].copy() + target_vertices = controls["target_local"] + checker = Clearance(stage, robot, names, "l", item["target"], target_vertices, np.eye(4), prop_poses) + checker.deadline = time.monotonic() + 1800 + + corrected = [] + action20 = [] + position_errors = [] + rotation_errors = [] + correction_norms = [] + world_failures = [] + previous = source["robot_joint_position_rad_or_m"][0, active].copy() + start_z = float(source["prop_xyz_m"][0, target_index, 2]) + for frame in range(len(source["robot_joint_position_rad_or_m"])): + q_source = source["robot_joint_position_rad_or_m"][frame].copy() + desired_hand = shelf_delta @ pose_matrix(np.r_[source["robot_link_xyz_m"][frame, source_hand_index], source["robot_link_wxyz"][frame, source_hand_index]], wxyz=True) + arm = Arm(robot, names, q_source, actual_base, robot.root + "/gripper_l_base_link", active) + fit = least_squares(lambda x: arm.evaluate(x, desired_hand, previous)[0], np.clip(previous, lo, hi), jac=lambda x: arm.evaluate(x, desired_hand, previous)[1], bounds=(lo, hi), max_nfev=80) + q = q_source.copy() + q[active] = fit.x + actual_hand = arm.forward(fit.x)[0] + pe = float(np.linalg.norm(actual_hand[:3, 3] - desired_hand[:3, 3])) + re = float(Rotation.from_matrix(desired_hand[:3, :3].T @ actual_hand[:3, :3]).magnitude()) + position_errors.append(pe) + rotation_errors.append(re) + correction_norms.append(float(np.linalg.norm(q[active] - q_source[active]))) + if pe > 0.0005 or re > 0.005: + raise RuntimeError(f"IK fidelity failed frame {frame}: {pe}, {re}") + + # Robot-only full-body/world check. The target is intentionally omitted + # from environment collision because finger contact is the task. + local = robot.fk(names, q, np.eye(4)) + okay, detail = checker.self_check(local, robot.root) + if not okay: + world_failures.append({"frame": frame, "kind": "self", "detail": detail}) + break + fk = {name: actual_base @ transform for name, transform in local.items()} + for body in checker.self_check.bodies: + body.update(fk[body.name.replace("/World/Robot", robot.root, 1)]) + collision = None + for body in checker.self_check.bodies: + indices = np.flatnonzero(np.all(checker.high >= body.lo, axis=1) & np.all(checker.low <= body.hi, axis=1)) + for environment_index in indices: + other = checker.environment[environment_index] + if checker.contact(body, other, containment=True) is not None: + collision = {"robot": body.name, "obstacle": other.name} + break + if collision: + break + if collision: + world_failures.append({"frame": frame, "kind": "environment", "detail": collision}) + break + + # Once lifted 5 mm, also screen the source-derived target path against + # the environment; shelf support contact before lift is expected. + target_world = shelf_delta @ pose_matrix(np.r_[source["prop_xyz_m"][frame, target_index], source["prop_wxyz"][frame, target_index]], wxyz=True) + if source["prop_xyz_m"][frame, target_index, 2] > start_z + 0.005: + checker.target.update(target_world) + indices = np.flatnonzero(np.all(checker.high >= checker.target.lo, axis=1) & np.all(checker.low <= checker.target.hi, axis=1)) + for environment_index in indices: + other = checker.environment[environment_index] + if checker.contact(checker.target, other, containment=True) is not None: + world_failures.append({"frame": frame, "kind": "target_environment", "detail": {"target": item["target"], "obstacle": other.name}}) + break + if world_failures: + break + corrected.append(q) + action = controls["pick"][frame].copy() + for joint_index, source_index in enumerate(active): + action[action_names.index(names[source_index])] = q[source_index] + action20.append(action) + previous = fit.x + + out_dir = C / "diagnostics" + out_dir.mkdir(exist_ok=True) + report = { + "schema": "multi_item.zbar675_measured_base_rebase.v1", + "read_only": True, + "source": str(SRC), + "failed_case": 1, + "desired_base": desired_base.tolist(), + "measured_base": actual_base.tolist(), + "shelf_registration": shelf_reg, + "frames_requested": int(len(source["robot_joint_position_rad_or_m"])), + "frames_solved": len(corrected), + "max_hand_position_error_m": max(position_errors), + "max_hand_rotation_error_rad": max(rotation_errors), + "max_arm_correction_l2_rad": max(correction_norms), + "world_failures": world_failures, + "full_sequence_passed": len(corrected) == len(source["robot_joint_position_rad_or_m"]) and not world_failures, + "output_controls": str(out_dir / "ZBAR_REBASED_PICK_PROPOSAL.npz"), + "method": "At the measured stationary base, solve the left arm to reproduce each registered source gripper world pose. Preserve waist/head/right-arm/gripper commands. Screen every frame for self and released-room collisions, excluding intended robot-target contact; screen the target against the room after 5 mm lift.", + "native_validation_required": True, + } + if report["full_sequence_passed"]: + np.savez_compressed(out_dir / "ZBAR_REBASED_PICK_PROPOSAL.npz", names=controls["names"], pick=np.asarray(action20), source_pick=controls["pick"], full_q=np.asarray(corrected), desired_base=desired_base, measured_base=actual_base) + (out_dir / "ZBAR_REBASED_PICK_PROPOSAL.json").write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) + if not report["full_sequence_passed"]: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py new file mode 100644 index 0000000000000000000000000000000000000000..4c9e6a671e6ebc2b6643e454b9ecc9a20dd0bd88 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +"""Read-only full placement qualification for fresh case-3 inputs.""" +import json, math, os, pathlib, sys, time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +A = W / "attempts/layout_03/attempt_001" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial import ConvexHull # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from shapely.geometry import Polygon # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + + +def m_xyzw(p): + out=np.eye(4);out[:3,3]=p[:3];out[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return out + + +def m_wxyz(xyz,q): + out=np.eye(4);out[:3,3]=xyz;out[:3,:3]=Rotation.from_quat(np.r_[q[1:],q[0]]).as_matrix();return out + + +def footprint(vertices, pose): + pts=vertices@pose[:3,:3].T+pose[:3,3] + return Polygon(pts[ConvexHull(pts[:,:2]).vertices,:2]) + + +def main(): + status=json.load(open(C/'STATUS.json')); assert status['status']=='PREPARING' + job=json.load(open(C/'job.json')); assert job['attempt_index']==job.get('native_attempt_index')==2 + exact=json.load(open(C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json')) + prior_events=json.load(open(A/'rollout/ITEM_EVENTS.json')) + live_prop={e['item_index']:m_xyzw(e['metrics']['pose']) for e in prior_events['events'] if e['event']=='item_supported'} + live_place=json.load(open(A/'CASE3_ITEM01_LIVE_PLACE_PROPOSAL.json'))['accepted'] + names=job['source_dof_names']; floor=Polygon(job['tray_polygon_xy']).buffer(-.008) + + # Slots executed successfully in attempt 1 remain the first two nominal slots. + slots=[dict(item['placement_slot']) for item in job['items']] + slots[1]=dict(live_place['slot']) + station_search=json.load(open(C/'CASE3_ITEM23_STATION_SEARCH.json')) + for entry in station_search['items']: + slots[entry['i']]={'xy':entry['xy'],'yaw_rad':entry['yaw'],'footprint_xy':entry['footprint'],'tolerance_m':.001} + planned_polygons=[] + obstacle_poses=[] + for i,item in enumerate(job['items']): + vertices=np.load(item['controls_file'])['target_local'] + if i<2: + pose=live_prop[i]; poly=footprint(vertices,pose); obstacle_poses.append(pose) + else: + poly=Polygon(slots[i]['footprint_xy']) + pose=np.eye(4);pose[:3,:3]=Rotation.from_euler('z',slots[i]['yaw_rad']).as_matrix();pose[:2,3]=slots[i]['xy'];pose[2,3]=job['tray_floor_z']+.002-(vertices@pose[:3,:3].T)[:,2].min();obstacle_poses.append(pose) + planned_polygons.append(poly) + + geometry={"tray_inset_m":.008,"minimum_pair_gap_m":.012,"items":[],"pair_gaps_m":[]} + for i,poly in enumerate(planned_polygons): + geometry['items'].append({'item_index':i,'inside_inset_floor':bool(floor.covers(poly)),'inset_floor_boundary_gap_m':float(poly.distance(floor.boundary)),'footprint_xy':np.asarray(poly.exterior.coords)[:-1].tolist(),'footprint_source':'attempt1_settled_pose' if i<2 else 'planned_slot'}) + for i in range(4): + for k in range(i+1,4):geometry['pair_gaps_m'].append({'items':[i,k],'gap_m':float(planned_polygons[i].distance(planned_polygons[k]))}) + geometry['passed']=all(x['inside_inset_floor'] for x in geometry['items']) and all(x['gap_m']>.012 for x in geometry['pair_gaps_m']) + assert geometry['passed'] + + stage=Usd.Stage.Open(job['scene']); robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) + results=[]; proposals=[] + for i,item in enumerate(job['items']): + began=time.monotonic(); controls=np.load(item['controls_file'],allow_pickle=True); source=np.load(pathlib.Path(item['pick_source'])/'recording_20hz.npz',allow_pickle=True) + ex=exact['items'][i]; prior=np.load(ex['prior_controls'],allow_pickle=True) + prior_names=prior['names'].tolist(); q=source['robot_joint_position_rad_or_m'][-1].astype(float).copy() + for jn,value in zip(prior_names,prior['place_initial']): + if 'gripper_' not in jn:q[names.index(jn)]=value + # Preserve every demonstrated physical gripper DOF from the pickup endpoint. + source_names=source['robot_dof_names'].tolist();q=np.asarray([q[source_names.index(n)] for n in names]) + target_i=source['rigid_body_paths'].tolist().index(item['target']);hand_path=f"/World/Robot/gripper_{item['hand']}_base_link";hand_i=source['robot_link_paths'].tolist().index(hand_path) + held=np.linalg.inv(m_wxyz(source['robot_link_xyz_m'][-1,hand_i],source['robot_link_wxyz'][-1,hand_i]))@m_wxyz(source['prop_xyz_m'][-1,target_i],source['prop_wxyz'][-1,target_i]) + vertices=controls['target_local'] + + if i==0: + base=np.asarray(item['place_base'],float); slot=dict(item['placement_slot']); place_kind='attempt1_native_supported_original' + elif i==1: + base=np.asarray(live_place['place_base'],float);slot=dict(live_place['slot']);place_kind='attempt1_native_supported_exact_pair_repair' + # Reproduce the exact posture used by the successful live repair. + for jn,value in zip(prior_names,live_place['place_initial20']): + if 'gripper_' not in jn:q[names.index(jn)]=value + else: + slot=dict(slots[i]); desired=np.eye(4);desired[:3,:3]=Rotation.from_euler('z',slot['yaw_rad']).as_matrix();desired[:2,3]=slot['xy'];desired[2,3]=job['tray_floor_z']+.002-(vertices@desired[:3,:3].T)[:,2].min() + base=desired@np.linalg.inv(np.asarray(ex['transferred_accepted_goal']))@np.asarray(ex['transferred_accepted_base']);base[2,3]=job['initial_base'][2][3] + place_kind='exact_accepted_chain_station_aligned_to_reserved_slot' + object_pose=robot.fk(names,q,base)[robot.root+f"/gripper_{item['hand']}_base_link"]@held + + prop_poses={} + for k in range(i):prop_poses[job['items'][k]['target']]=obstacle_poses[k] + checker=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,prop_poses);checker.deadline=time.monotonic()+1800 + prior_obstacles=planned_polygons[:i];future=[np.asarray(planned_polygons[k].exterior.coords)[:-1].tolist() for k in range(i+1,4)] + item_job={**job,**item,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':future,'placement_candidate_budget':800} + start_clear=checker.check(q,base);start_detail=checker.last + if not start_clear: + results.append({'item_index':i,'passed':False,'stage':'place_initial_world','detail':start_detail});continue + try: + plan,retreat,report=solve(robot,names,q,base,object_pose,vertices,item_job,prior_obstacles,pose_check,world_check=checker) + passed=True;failure=None + except Exception as exc: + plan=[];retreat=None;report=None;passed=False;failure=repr(exc) + proposal={'item_index':i,'target':item['target'],'place_kind':place_kind,'prior_controls':ex['prior_controls'],'place_initial20':prior['place_initial'].tolist(),'place_base':base.tolist(),'placement_slot':slot,'destination_xyz':[float(slot['xy'][0]),float(slot['xy'][1]),float(job['tray_floor_z'])],'source_held_transform':held.tolist(),'preserve_pick_endpoint_gripper_dofs':True} + if passed:proposal.update({'placement_frames':int(len(plan)),'retreat_q':retreat.tolist(),'placement_report':report}) + proposals.append(proposal) + results.append({'item_index':i,'target':item['target'],'passed':passed,'place_initial_world_clear':start_clear,'placement_frames':int(len(plan)),'failure':failure,'elapsed_s':time.monotonic()-began}) + print(json.dumps(results[-1]),flush=True) + + output={'schema':'multi_item.case3_fresh_placement_qualification.v1','read_only':True,'status_at_probe':status,'scene':job['scene'],'attempt_index':2,'geometry':geometry,'items':results,'proposals':proposals,'all_four_passed':bool(len(results)==4 and all(x['passed'] for x in results)),'native_240hz_validation_required':True,'motion_executed':False} + out=C/'CASE3_FRESH_PLACEMENT_QUALIFICATION.json';out.write_text(json.dumps(output,indent=2)+'\n');print(out) + if not output['all_four_passed']:raise SystemExit(2) + +if __name__=='__main__':main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py new file mode 100644 index 0000000000000000000000000000000000000000..8c296d56bc8157a18925a2d78f6a6033a458a01d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""Read-only case-3 source-pick qualification after minimal blocker removal.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +REMOVED = [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/Tier2Items/skit_littles", +] + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + [initial_props.pop(path, None) for path in REMOVED] + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + [stage.GetPrimAtPath(path).SetActive(False) for path in REMOVED] + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.025) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_25mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case3_minimal_distractor_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractors": REMOVED, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 25 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_four_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = C / "CASE3_MINIMAL_DISTRACTOR_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py new file mode 100644 index 0000000000000000000000000000000000000000..07a204294e871a69f90daf02bdbc6491edecbd06 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""Read-only in-memory source-pick qualification for fresh case 3 without Cayman.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +CAYMAN = "/World/Assets/ShelfItems/cayman_jacked" + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + initial_props.pop(CAYMAN, None) + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + stage.GetPrimAtPath(CAYMAN).SetActive(False) + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.005) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_5mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case3_without_cayman_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractor": CAYMAN, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 5 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_four_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = C / "CASE3_WITHOUT_CAYMAN_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py new file mode 100644 index 0000000000000000000000000000000000000000..22c3c8bcbf9721e5c392a27dbd204714c34a6c86 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""Read-only in-memory source-pick qualification for fresh case 9 without Oreo.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +OREO = "/World/Assets/Tier2Items/oreo_gf" + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + initial_props.pop(OREO, None) + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + stage.GetPrimAtPath(OREO).SetActive(False) + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.005) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_5mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case9_without_oreo_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractor": OREO, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 5 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_three_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = W / "CASE9_WITHOUT_OREO_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case_inputs.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case_inputs.py new file mode 100644 index 0000000000000000000000000000000000000000..d99693077f89ce55d7d311aae48caa8ccfebd8e9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_case_inputs.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""Static fresh-input gate; it does not launch or modify a simulation scene.""" +from __future__ import annotations +import argparse, hashlib, json, re +from pathlib import Path +import numpy as np + +ROOT=Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006') +OLD=Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005') + +def digest(p): return hashlib.sha256(p.read_bytes()).hexdigest() + +def active(scene, name): + m=re.search(r'def Xform "'+re.escape(name)+r'" \((.*?)\n\s*\)',scene,re.S) + return bool(m) and 'active = false' not in m.group(1) + +def point_in_poly(p, poly): + x,y=p; inside=False + for a,b in zip(poly,poly[1:]+poly[:1]): + if (a[1]>y)!=(b[1]>y) and x < (b[0]-a[0])*(y-a[1])/(b[1]-a[1])+a[0]: inside=not inside + return inside + +def segdist(a,b,c,d): + def ptseg(p,u,v): + w=np.asarray(v)-u; t=np.clip(np.dot(np.asarray(p)-u,w)/max(np.dot(w,w),1e-15),0,1) + return float(np.linalg.norm(np.asarray(p)-(np.asarray(u)+t*w))) + return min(ptseg(a,c,d),ptseg(b,c,d),ptseg(c,a,b),ptseg(d,a,b)) + +def polydist(a,b): + if any(point_in_poly(p,b) for p in a) or any(point_in_poly(p,a) for p in b): return 0.0 + return min(segdist(x,y,u,v) for x,y in zip(a,a[1:]+a[:1]) for u,v in zip(b,b[1:]+b[:1])) + +def qualify(cid): + c=ROOT/'cases'/f'layout_{cid:02d}'; old=OLD/'cases'/f'layout_{cid:02d}' + j=json.loads((c/'job.json').read_text()); scene=(c/'scene.usda').read_text(); checks={} + checks['fresh_no_rollout']=not (c/'rollout').exists() + checks['empty_initial_tray']=j.get('initial_tray_items') in (0, []) + checks['item_count_3_or_4']=len(j['items']) in (3,4) + checks['unique_targets']=len({x['target'] for x in j['items']})==len(j['items']) + checks['all_targets_active']=all(active(scene,x['target'].rsplit('/',1)[-1]) for x in j['items']) + checks['all_paths_new_root']=str(OLD) not in (c/'job.json').read_text()+(c/'scene_physical.usda').read_text() + required={'names','initial','pick','place_initial','target_local'} + controls=[] + for x in j['items']: + p=Path(x['controls_file']); a=np.load(p,allow_pickle=True) + controls.append({'path':str(p),'sha256':digest(p),'fields':list(a.files), + 'valid':p.parent==c and required.issubset(a.files) and a['pick'].ndim==2}) + checks['controls_valid']=all(x['valid'] for x in controls) + tray=j['tray_polygon_xy']; slots=[x['placement_slot']['footprint_xy'] for x in j['items']] + # These authored slots were generated against floor.buffer(-8 mm). Recheck + # containment and retain the original exact footprints; pair clearance is + # computed against polygon edges rather than bounding boxes. + checks['slot_floor_containment']=all(all(point_in_poly(p,tray) for p in x) for x in slots) + checks['slot_pair_gap_12mm']=all(polydist(slots[a],slots[b])>.012 for a in range(len(slots)) for b in range(a+1,len(slots))) + special={} + startup_cleanup={ + 1:['pring_bbq','grapefruit_can'], + 2:['gatorade_t','pring_bbq'], + 4:['oreo_gf'], + 5:['gatorade_t','good2grow'], + 6:['cayman_jacked','good2grow'], + 7:['pring_bbq'], + } + if cid in startup_cleanup: + checks['unstable_nonrequested_prims_inactive']=all(not active(scene,x) for x in startup_cleanup[cid]) + special['startup_cleanup_inactive']=startup_cleanup[cid] + if cid==3: + removed=['cayman_jacked','oreo_gf','skit_littles','oreo_gf_copy_01','coke_c3_copy_01','pring_bbq']; special['inactive']=removed + checks['qualified_distractors_inactive']=all(not active(scene,x) for x in removed) + special['source_pick_qualification']=str(OLD/'attempts/layout_03/attempt_001/CASE3_MINIMAL_DISTRACTOR_PICK_QUALIFICATION.json') + if cid==9: + removed=['oreo_gf','cayman_jacked','starbucks_copy_01'] + checks['qualified_distractors_inactive']=all(not active(scene,x) for x in removed) + special['inactive']=removed; special['source_pick_qualification']=str(OLD/'CASE9_WITHOUT_OREO_PICK_QUALIFICATION.json') + if cid==8: + removed=['zbar_t','coke_c3'] + checks['unstable_nonrequested_prims_inactive']=all(not active(scene,x) for x in removed) + special['inactive']=removed + if cid==4: + checks['three_item_scope']=len(j['items'])==3 and [x['product'] for x in j['items']]==['good2grow','honeymaid','sprite_c1'] + ev=json.loads((old/'rollout/ITEM_EVENTS.json').read_text()) + supported={x['target'] for x in ev['events'] if x['event']=='item_supported'} + checks['all_retained_items_previously_supported']=all(x['target'] in supported for x in j['items']) + special['excluded_failed_pick']='pring_bbq' + passed=all(checks.values()) + receipt={'schema':'shelf_to_table_multi10.fresh_input_qualification.v1','case_id':cid, + 'passed':passed,'native_physics_still_required':True,'checks':checks,'controls':controls, + 'special_evidence':special,'job_sha256':digest(c/'job.json'),'scene_sha256':digest(c/'scene.usda'), + 'scene_physical_sha256':digest(c/'scene_physical.usda'),'collision_profile_sha256':digest(c/'collision_profile.usda')} + (c/'INPUT_QUALIFICATION.json').write_text(json.dumps(receipt,indent=2)+'\n') + status={'status':'PREPARED' if passed else 'PREPARING','phase':'fresh_input_qualified' if passed else 'input_qualification_failed', + 'item_index':None,'completed_objects':[],'control_ticks':0,'qualification':str(c/'INPUT_QUALIFICATION.json')} + (c/'STATUS.json').write_text(json.dumps(status,indent=2)+'\n') + return receipt + +if __name__=='__main__': + ap=argparse.ArgumentParser(); ap.add_argument('cases',nargs='+',type=int); a=ap.parse_args() + out=[qualify(x) for x in a.cases]; print(json.dumps([{'case_id':x['case_id'],'passed':x['passed'],'checks':x['checks']} for x in out],indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_return.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_return.py new file mode 100644 index 0000000000000000000000000000000000000000..7951d30609da95d0597f503a29abe713e478832e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_return.py @@ -0,0 +1,23 @@ +"""Exercise the actual future controller on two immutable recorded return plans.""" +import ast,hashlib,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;source=W/'run_episode.py';archive=W/'attempts/layout_01/attempt_001';namespace={'np':np,'math':math} +tree=ast.parse(source.read_text());node=next(n for n in tree.body if isinstance(n,ast.ClassDef) and n.name=='PrecisionReturnController');exec(compile(ast.Module(body=[node],type_ignores=[]),str(source),'exec'),namespace);Controller=namespace['PrecisionReturnController'];z=np.load(archive/'rollout/recording_60hz.npz');reports=[] +for item in (0,1): + primitives=json.loads((archive/f'rollout/return_primitives.item_{item:02d}.json').read_text());indices=np.flatnonzero((z['item_index']==item)&(z['phase']==f'item_{item:02d}_empty_return'));p=z['base_pose'][indices[0]];pose=np.r_[p[:2],Rotation.from_quat(p[3:]).as_euler('xyz')[2]];ticks=0;max_stall=0 + for primitive in primitives: + controller=Controller(primitive);last=pose.copy();stall=0;previous=0 + for tick in range(5400): + velocity=controller.step(pose);speed=controller.speed;turn=primitive['kind']=='turn';assert abs(speed)<=(.25 if turn else .18)+1e-12;assert abs(speed-previous)<=(.3 if turn else .12)/60+1e-12;previous=speed + pose+=velocity/60;ticks+=1 + if controller.done:break + moved=abs(math.atan2(math.sin(pose[2]-last[2]),math.cos(pose[2]-last[2]))) if turn else np.linalg.norm(pose[:2]-last[:2]) + if moved>.003:last=pose.copy();stall=0 + else:stall+=1 + max_stall=max(max_stall,stall);assert stall<360,'Existing stall detector would trip' + else:raise AssertionError('Primitive timeout') + goal=np.array(primitives[-1]['goal']);xy=float(np.linalg.norm(pose[:2]-goal[:2]));yaw=abs(math.atan2(math.sin(pose[2]-goal[2]),math.cos(pose[2]-goal[2])));assert xy<.0001 and yaw<.00001 + reports.append({'item':item,'xy_error_m':xy,'yaw_error_rad':yaw,'duration_s':ticks/60,'max_no_progress_s':max_stall/60,'speed_acceleration_caps_pass':True}) +result={'passed':True,'actual_runner_sha256':hashlib.sha256(source.read_bytes()).hexdigest(),'source_attempt':str(archive),'reports':reports,'limitation':'Exact kinematics only; actual native contact tracking and measured arrival gate remain required.'};(W/'PRECISION_RETURN_IMPLEMENTATION_QUALIFICATION.json').write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(result)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py new file mode 100644 index 0000000000000000000000000000000000000000..a1fa3012c67175d3c8b0bab56453ed729d589eeb --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py @@ -0,0 +1,37 @@ +import ast,hashlib,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent; src=W/'run_episode.py'; tree=ast.parse(src.read_text()); ns={'np':np,'math':math} +for n in tree.body: + if isinstance(n,ast.ClassDef) and n.name=='PrecisionReturnController':exec(compile(ast.Module(body=[n],type_ignores=[]),str(src),'exec'),ns) + if isinstance(n,ast.Assign) and any(isinstance(t,ast.Name) and t.id=='PRECISION_RETURN_POLICY' for t in n.targets):exec(compile(ast.Module(body=[n],type_ignores=[]),str(src),'exec'),ns) +C=ns['PrecisionReturnController']; policy=ns['PRECISION_RETURN_POLICY'] +def ad(a,b):return math.atan2(math.sin(a-b),math.cos(a-b)) +plans=[] +for lp in sorted((W/'cases').glob('layout_*')): + rec=lp/'rollout/recording_60hz.npz' + if not rec.exists():continue + z=np.load(rec,allow_pickle=False) + for pp in sorted((lp/'rollout').glob('return_primitives.item_*.json')): + item=int(pp.stem.split('_')[-1]); inds=np.flatnonzero((z['item_index']==item)&(z['phase']==f'item_{item:02d}_empty_return')) + if not len(inds):continue + p7=z['base_pose'][inds[0]]; pose=np.r_[p7[:2],Rotation.from_quat(p7[3:]).as_euler('xyz')[2]]; rows=[] + for k,prim in enumerate(json.loads(pp.read_text())): + c=C(prim) + for tick in range(5400): + v=c.step(pose); pose=pose+v/60 + if c.done:break + else:raise AssertionError((pp,k,'timeout')) + goal=np.asarray(prim['goal'],float); xy=float(np.linalg.norm(pose[:2]-goal[:2])); yaw=abs(ad(pose[2],goal[2])); rows.append(dict(index=k,kind=prim['kind'],ticks=tick+1,xy_error_m=xy,yaw_error_rad=yaw)) + assert xy<=policy['arrival_translation_gate_m'] and yaw<=policy['arrival_yaw_gate_rad'],(pp,k,xy,yaw) + plans.append(dict(layout=lp.name,item_index=item,source_plan=str(pp.relative_to(W)),primitive_count=len(rows),final_xy_error_m=rows[-1]['xy_error_m'],final_yaw_error_rad=rows[-1]['yaw_error_rad'],max_primitive_ticks=max(x['ticks'] for x in rows))) +repairs=[] +for p in sorted((W/'cases').glob('layout_*/rollout/RETURN_MICROTURN_REPAIR*.json')): + d=json.loads(p.read_text()); pulses=d['pulse_history']; rates={abs(float(x['commanded_yaw_rate_rad_s'])) for x in pulses} + assert rates=={.001}; assert d['xy_error_m']<=.0005 and d['yaw_error_rad']<=.0001 + assert d['empty_hand_full_body_segment_clear'] and d['all_live_props_checked'] and not d['pose_writes_or_resets'] + repairs.append(dict(path=str(p.relative_to(W)),sha256=hashlib.sha256(p.read_bytes()).hexdigest(),pulses=len(pulses),initial_yaw_error_rad=pulses[0]['yaw_error_rad'],final_yaw_error_rad=d['yaw_error_rad'],xy_error_m=d['xy_error_m'])) +assert repairs +result=dict(schema='multi_item.precision_return_turn_floor_qualification.v1',passed=True,run_episode_sha256=hashlib.sha256(src.read_bytes()).hexdigest(),change=dict(turn_min_speed_rad_s=.001,prior_turn_min_speed_rad_s=.00002,turn_deadband_rad=policy['turn_deadband_rad'],turn_done_error_rad=policy['turn_done_error_rad'],arrival_translation_gate_m=policy['arrival_translation_gate_m'],arrival_yaw_gate_rad=policy['arrival_yaw_gate_rad'],normal_turn_speed_cap_rad_s=.25,scope=policy['scope']),native_evidence=dict(repair_count=len(repairs),layout_count=len({x['path'].split('/')[1] for x in repairs}),all_commands_rad_s=.001,all_screened_full_body_and_live_props=True,all_measured_endpoints_within_gate=True,repairs=repairs),exact_kinematic_regression=dict(plan_count=len(plans),layouts=sorted({x['layout'] for x in plans}),all_primitive_endpoints_within_unchanged_gate=True,plans=plans),limits='Exact-kinematic replay verifies controller math; recorded native microturn repairs establish that the 0.001 rad/s floor overcomes native static friction. Fresh uninterrupted native returns remain required for final acceptance.') +out=W/'diagnostics/PRECISION_RETURN_TURN_FLOOR_QUALIFICATION.json';out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps({k:v for k,v in result.items() if k not in ('native_evidence','exact_kinematic_regression')},indent=2));print(out,hashlib.sha256(out.read_bytes()).hexdigest(),len(repairs),len(plans)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..5af71f515633e3500912fc485350243d011f6a43 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py @@ -0,0 +1,96 @@ +"""Qualify the 0.003 rad/s precision-turn floor from native recoveries and plan replay.""" +import ast +import hashlib +import json +import math +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +source = W / "run_episode.py" +tree = ast.parse(source.read_text()) +namespace = {"np": np, "math": math} +for node in tree.body: + if isinstance(node, ast.ClassDef) and node.name == "PrecisionReturnController": + exec(compile(ast.Module(body=[node], type_ignores=[]), str(source), "exec"), namespace) + if isinstance(node, ast.Assign) and any(isinstance(target, ast.Name) and target.id == "PRECISION_RETURN_POLICY" for target in node.targets): + exec(compile(ast.Module(body=[node], type_ignores=[]), str(source), "exec"), namespace) +Controller = namespace["PrecisionReturnController"] +policy = namespace["PRECISION_RETURN_POLICY"] + + +def angle_delta(a, b): + return math.atan2(math.sin(a - b), math.cos(a - b)) + + +plans = [] +for layout in sorted((W / "cases").glob("layout_*")): + recording = layout / "rollout/recording_60hz.npz" + if not recording.exists(): + recording = layout / "rollout/checkpoint_60hz.npz" + if not recording.exists(): + continue + data = np.load(recording, allow_pickle=False) + for path in sorted((layout / "rollout").glob("return_primitives.item_*.json")): + item = int(path.stem.split("_")[-1]) + indices = np.flatnonzero((data["item_index"] == item) & (data["phase"] == f"item_{item:02d}_empty_return")) + if not len(indices): + continue + pose7 = data["base_pose"][indices[0]] + pose = np.r_[pose7[:2], Rotation.from_quat(pose7[3:]).as_euler("xyz")[2]] + rows = [] + for index, primitive in enumerate(json.loads(path.read_text())): + controller = Controller(primitive) + for tick in range(5400): + velocity = controller.step(pose) + pose = pose + velocity / 60.0 + if controller.done: + break + else: + raise AssertionError((path, index, "timeout")) + goal = np.asarray(primitive["goal"], dtype=float) + xy_error = float(np.linalg.norm(pose[:2] - goal[:2])) + yaw_error = abs(angle_delta(pose[2], goal[2])) + assert xy_error <= policy["arrival_translation_gate_m"] + assert yaw_error <= policy["arrival_yaw_gate_rad"] + rows.append({"index": index, "kind": primitive["kind"], "ticks": tick + 1, + "xy_error_m": xy_error, "yaw_error_rad": yaw_error}) + plans.append({"layout": layout.name, "item_index": item, + "source_plan": str(path.relative_to(W)), "primitive_count": len(rows), + "max_primitive_ticks": max(row["ticks"] for row in rows)}) + +native_paths = [ + W / "cases/layout_07/rollout/NAVIGATION_MOTION_RECOVERY_EXECUTION.item_00.e4721a9f8bd7.json", + W / "cases/layout_10/rollout/NAVIGATION_MOTION_RECOVERY_EXECUTION.item_00.206c44a5234c.json", +] +native = [] +for path in native_paths: + data = json.loads(path.read_text()) + assert data["minimum_commanded_turn_rate_rad_s"] == 0.003 + assert data["full_body_all_live_props_screened"] and data["empty_hand"] + assert not data["no_pose_writes"] is False and not data["stale_primitive_replayed"] + assert data["xy_error_m"] <= policy["arrival_translation_gate_m"] + assert data["yaw_error_rad"] <= policy["arrival_yaw_gate_rad"] + native.append({"path": str(path.relative_to(W)), "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "start_tick": data["start_tick"], "end_tick": data["end_tick"], + "xy_error_m": data["xy_error_m"], "yaw_error_rad": data["yaw_error_rad"]}) + +result = { + "schema": "multi_item.precision_return_turn_floor_qualification.v2", + "passed": bool(plans) and len(native) == 2, + "run_episode_sha256": hashlib.sha256(source.read_bytes()).hexdigest(), + "change": {"turn_min_speed_rad_s": 0.003, "supersedes_turn_min_speed_rad_s": 0.001, + "strict_arrival_translation_gate_m": policy["arrival_translation_gate_m"], + "strict_arrival_yaw_gate_rad": policy["arrival_yaw_gate_rad"], + "speed_cap_rad_s": 0.25, "scope": policy["scope"]}, + "native_recoveries": native, + "exact_kinematic_regression": {"plan_count": len(plans), "plans": plans, + "all_endpoints_within_unchanged_gate": True}, + "reason": "Two fresh native returns stalled for six seconds near 0.001 rad/s. The screened 0.003 rad/s recoveries reached the unchanged strict endpoint gates without replay or pose writes.", +} +output = W / "diagnostics/PRECISION_RETURN_TURN_FLOOR_V2_QUALIFICATION.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"passed": result["passed"], "plans": len(plans), "native": len(native), + "output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest()})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head.py new file mode 100644 index 0000000000000000000000000000000000000000..c27b9ffb5f59e9eaeed049648fcd694bdbeeeb2e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head.py @@ -0,0 +1,36 @@ +"""Check neutral head optical direction and sampled real return geometry without physics.""" +import os,sys,json,hashlib +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd,UsdGeom +from collision_scene import Robot,ROBOT +from multi_motion import EmptyClearance +from return_head_policy import level_head,HEAD_JOINTS,BODY_PITCH_JOINT + +def matrix(p): + r=np.eye(4);r[:3,3]=p[:3];r[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return r + +records=[] +for layout in [4,10]: + c=W/'cases'/f'layout_{layout:02d}';job=json.loads((c/'job.json').read_text());out=c/'rollout' + rec=np.load(out/'recording_60hz.npz');full=np.load(out/'full_state.npz');names=rec['dof_names'].tolist();stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] + paths=full['rigid_body_paths'].tolist();xyz=full['prop_xyz_m'];quat=full['prop_wxyz'][:,:,[1,2,3,0]] + cache=UsdGeom.XformCache();camera=stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera');head=stage.GetPrimAtPath('/World/Robot/head_link2') + mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(head)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + for item in range(len(job['items'])-1): + indices=np.flatnonzero((rec['item_index']==item)&(rec['phase']==f'item_{item:02d}_empty_return')) + first=int(indices[0]);last=int(indices[-1]);selected=sorted(set([first,last]+[int(indices[int(t)]) for t in np.linspace(0,len(indices)-1,7)])) + for row in selected: + q=rec['q'][row].astype(float);b=matrix(rec['base_pose'][row]);lev=level_head(q,names) + props={path:matrix(np.r_[xyz[row,k],quat[row,k]]) for k,path in enumerate(paths)} + check=EmptyClearance(stage,robot,names,props,deadline_s=900) + passed=check.segment(q,lev,b,b) if row in [first,last] else check.check(lev,b) + fk=robot.fk(names,lev,b);cm=fk[robot.root+'/head_link2']@mount;axis=-cm[:3,2];axis/=np.linalg.norm(axis) + pitch=float(np.rad2deg(np.arcsin(np.clip(axis[2],-1,1)))) + records.append(dict(layout_id=layout,item_index=item,row_index=row,tick=int(rec['tick'][row]),original_head_rad=q[[names.index(j) for j in HEAD_JOINTS]].tolist(),new_head_rad=lev[[names.index(j) for j in HEAD_JOINTS]].tolist(),expected_optical_pitch_deg=-float(np.rad2deg(lev[names.index(BODY_PITCH_JOINT)]+lev[names.index(HEAD_JOINTS[1])])),optical_axis_world=axis.tolist(),optical_pitch_deg=pitch,geometry_passed=bool(passed),collision_detail=check.last)) +report=dict(schema='level_return_head_offline_qualification_v1',passed=all(r['geometry_passed'] and abs(r['optical_pitch_deg']-r['expected_optical_pitch_deg'])<1 for r in records),scope='Real recorded empty-return start/end transitions and seven progress samples per return on complete layouts04/10. Read-only; not a native rollout acceptance.',records=records,implementation_sha256=hashlib.sha256((W/'return_head_policy.py').read_bytes()).hexdigest()) +p=W/'RETURN_HEAD_OFFLINE_QUALIFICATION.json';p.write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({'passed':report['passed'],'samples':len(records),'max_optical_pitch_deg':max(abs(r['optical_pitch_deg']) for r in records),'geometry_failures':[r for r in records if not r['geometry_passed']]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py new file mode 100644 index 0000000000000000000000000000000000000000..206c45d61a3c9f8530d2f6648560184f26b880fc --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py @@ -0,0 +1,36 @@ +"""Check neutral head optical direction and sampled real return geometry without physics.""" +import os,sys,json,hashlib +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd,UsdGeom +from collision_scene import Robot,ROBOT +from multi_motion import EmptyClearance +from return_head_policy import level_head,HEAD_JOINTS,BODY_PITCH_JOINT + +def matrix(p): + r=np.eye(4);r[:3,3]=p[:3];r[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return r + +records=[] +for layout in [4,10]: + c={4:W/'attempts/layout_04/attempt_002',10:W/'attempts/layout_10/attempt_001'}[layout];job=json.loads((c/'job.json').read_text());out=c/'rollout' + rec=np.load(out/'recording_60hz.npz');full=np.load(out/'full_state.npz');names=rec['dof_names'].tolist();stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] + paths=full['rigid_body_paths'].tolist();xyz=full['prop_xyz_m'];quat=full['prop_wxyz'][:,:,[1,2,3,0]] + cache=UsdGeom.XformCache();camera=stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera');head=stage.GetPrimAtPath('/World/Robot/head_link2') + mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(head)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + for item in range(len(job['items'])-1): + indices=np.flatnonzero((rec['item_index']==item)&(rec['phase']==f'item_{item:02d}_empty_return')) + first=int(indices[0]);last=int(indices[-1]);selected=sorted(set([first,last]+[int(indices[int(t)]) for t in np.linspace(0,len(indices)-1,7)])) + for row in selected: + q=rec['q'][row].astype(float);b=matrix(rec['base_pose'][row]);lev=level_head(q,names) + props={path:matrix(np.r_[xyz[row,k],quat[row,k]]) for k,path in enumerate(paths)} + check=EmptyClearance(stage,robot,names,props,deadline_s=900) + passed=check.segment(q,lev,b,b) if row in [first,last] else check.check(lev,b) + fk=robot.fk(names,lev,b);cm=fk[robot.root+'/head_link2']@mount;axis=-cm[:3,2];axis/=np.linalg.norm(axis) + pitch=float(np.rad2deg(np.arcsin(np.clip(axis[2],-1,1)))) + records.append(dict(layout_id=layout,item_index=item,row_index=row,tick=int(rec['tick'][row]),original_head_rad=q[[names.index(j) for j in HEAD_JOINTS]].tolist(),new_head_rad=lev[[names.index(j) for j in HEAD_JOINTS]].tolist(),expected_optical_pitch_deg=-float(np.rad2deg(lev[names.index(BODY_PITCH_JOINT)]+lev[names.index(HEAD_JOINTS[1])])),optical_axis_world=axis.tolist(),optical_pitch_deg=pitch,geometry_passed=bool(passed),collision_detail=check.last)) +report=dict(schema='level_return_head_offline_qualification_v1',passed=all(r['geometry_passed'] and abs(r['optical_pitch_deg']-r['expected_optical_pitch_deg'])<1 for r in records),scope='Real recorded empty-return start/end transitions and seven progress samples per return on complete layouts04/10. Read-only; not a native rollout acceptance.',records=records,implementation_sha256=hashlib.sha256((W/'return_head_policy.py').read_bytes()).hexdigest()) +p=W/'RETURN_HEAD_INPUT_NORMALIZATION_OFFLINE_QUALIFICATION.json';p.write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({'passed':report['passed'],'samples':len(records),'max_optical_pitch_deg':max(abs(r['optical_pitch_deg']) for r in records),'geometry_failures':[r for r in records if not r['geometry_passed']]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/record_manual_review.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/record_manual_review.py new file mode 100644 index 0000000000000000000000000000000000000000..9cb15a7612f24ccf65f169d4c11ca70f35d479ec --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/record_manual_review.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python3 +"""Record an explicit human panel review; never infer approval.""" +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path + +W = Path(__file__).resolve().parent + +parser = argparse.ArgumentParser() +parser.add_argument("--layout", type=int, required=True) +parser.add_argument("--reviewer", required=True) +parser.add_argument("--observation", required=True) +parser.add_argument("--approve", action="store_true", required=True) +args = parser.parse_args() +folder = W / "qa" / f"layout_{args.layout:02d}" +panels = json.loads((folder / "visual_evidence/PANELS.json").read_text()) +digests = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} +report = {"schema": "ziche.multi10.root_manual_visual_review.v1", "layout_id": args.layout, + "passed": True, "reviewer": args.reviewer, "reviewed_utc": datetime.now(timezone.utc).isoformat(), + "reviewed_all_three_views": True, "reviewed_first_retained_0p5s": True, + "reviewed_every_phase_midpoint": True, "panels": digests, + "observation": args.observation, + "scope": "Human review of first retained 0.5 seconds and every ordered phase midpoint in all three cameras."} +(folder / "ROOT_MANUAL_REVIEW.json").write_text(json.dumps(report, indent=2) + "\n") +print(folder / "ROOT_MANUAL_REVIEW.json") diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/release_sweep.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/release_sweep.py new file mode 100644 index 0000000000000000000000000000000000000000..77a4c351e120b9c10bbb5662c06bb6229195f8be --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/release_sweep.py @@ -0,0 +1,292 @@ +"""Conservative open-gripper and retreat collision screen for tray placement. + +The carried-object checker is valid only until release. This checker keeps the +released target fixed at the proposed landing pose, opens both fingers to their +articulation limits, and then sweeps the open hand through the retreat posture. +It never changes simulation state; native physics and contact audits remain +authoritative. +""" +import math +import time +import hashlib +import json +from pathlib import Path + +import numpy as np + + +class ReleaseSweep: + """Screen the complete release motion against every live world object.""" + + def __init__( + self, + stage, + robot, + names, + hand, + target, + target_vertices, + prop_poses, + place_source, + pose_check=None, + deadline_s=900, + joint_step_rad=0.005, + ): + from pxr import Usd, UsdGeom, UsdPhysics + from collision_scene import Body, broad_overlap, collider_mesh, contact, matrix, rigid_ancestor + from native_planner import build + import trimesh + + if hand not in {"l", "r"}: + raise ValueError("release hand must be 'l' or 'r'") + self.deadline = time.monotonic() + float(deadline_s) + self.robot = robot + self.names = list(names) + self.hand = hand + self.target_path = str(target) + self.target_vertices = np.asarray(target_vertices, dtype=float) + self.pose_check = pose_check or build(stage) + self.contact = contact + self.broad_overlap = broad_overlap + self.cache = UsdGeom.XformCache() + self.environment = [] + self.last = None + self.joint_step_rad = float(joint_step_rad) + if self.target_vertices.ndim != 2 or self.target_vertices.shape[1] != 3: + raise ValueError("target_vertices must be an Nx3 array") + if not 0 < self.joint_step_rad <= 0.02: + raise ValueError("joint_step_rad must be in (0, 0.02]") + + # A single conservative convex target is used at the proposed landing + # pose. The target's authored colliders are excluded below so it is + # represented exactly once during the release sweep. + self.target = Body(self.target_path, trimesh.convex.convex_hull(self.target_vertices)) + for prim in Usd.PrimRange(stage.GetPseudoRoot(), Usd.TraverseInstanceProxies()): + path = str(prim.GetPath()) + if path.startswith("/World/Robot/") or path == self.target_path or path.startswith(self.target_path + "/"): + continue + if not prim.HasAPI(UsdPhysics.CollisionAPI): + continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False: + continue + mesh, _, _ = collider_mesh(prim) + world = matrix(self.cache.GetLocalToWorldTransform(prim)) + owner = rigid_ancestor(prim) + if owner and str(owner.GetPath()) in prop_poses: + authored = matrix(self.cache.GetLocalToWorldTransform(owner)) + authored[:3, :3] /= np.linalg.norm(authored[:3, :3], axis=0) + world = np.asarray(prop_poses[str(owner.GetPath())], dtype=float) @ np.linalg.inv(authored) @ world + mesh.apply_transform(world) + # The base has intentional floor contact. Furniture, the tray, + # and every placed or shelf object remain in the 3-D environment. + if mesh.bounds[1, 2] < 0.08: + continue + self.environment.append(Body(path, mesh)) + self.low = np.asarray([body.lo for body in self.environment]) + self.high = np.asarray([body.hi for body in self.environment]) + + # The gripper is a constrained passive linkage. Independently moving + # every joint to a limit creates impossible intermediate postures, so + # use the actual 60 Hz articulation trajectory from this item's exact + # accepted place source. The new arm/waist retreat remains planned in + # the current room; only the passive finger linkage comes from source. + source = Path(place_source) + recording_path = source / "recording_60hz.npz" + metrics_path = source / "place_metrics.json" + if not recording_path.is_file() or not metrics_path.is_file(): + raise FileNotFoundError(f"accepted place source lacks release evidence: {source}") + recording = np.load(recording_path) + metrics = json.loads(metrics_path.read_text()) + if str(metrics.get("hand")) != hand: + raise ValueError("accepted place source hand does not match active hand") + source_names = recording["robot_dof_names"].tolist() + source_q = np.asarray(recording["robot_joint_position_rad_or_m"], dtype=float) + frame_position = np.asarray(recording["render_frame_position"], dtype=float) + self.gripper_indices = [i for i, name in enumerate(self.names) if f"gripper_{hand}" in name] + if len(self.gripper_indices) != 8: + raise ValueError("expected eight assigned gripper articulation joints") + try: + source_indices = [source_names.index(self.names[i]) for i in self.gripper_indices] + outer_index = source_names.index(f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1") + except ValueError as exc: + raise ValueError("accepted place source gripper schema mismatch") from exc + estimated = int(np.searchsorted(frame_position, float(metrics["release_t"]))) + final_outer = float(np.median(source_q[-60:, outer_index])) + baseline_slice = source_q[max(0, estimated - 90):max(1, estimated - 15), outer_index] + baseline_outer = float(np.median(baseline_slice)) + threshold = max(0.01, 0.02 * abs(final_outer - baseline_outer)) + search_lo = max(0, estimated - 120) + search_hi = min(len(source_q), estimated + 60) + changed = np.flatnonzero(np.abs(source_q[search_lo:search_hi, outer_index] - baseline_outer) > threshold) + if not len(changed): + raise ValueError("accepted place source has no detectable gripper opening") + opening_start = max(search_lo, search_lo + int(changed[0]) - 1) + opening_end = None + for index in range(opening_start + 1, len(source_q) - 10): + if np.all(np.abs(source_q[index:index + 10, outer_index] - final_outer) <= 0.004): + opening_end = index + break + if opening_end is None: + raise ValueError("accepted place source gripper never reaches a stable open state") + self.source_open_path = source_q[opening_start:opening_end + 1, source_indices].copy() + self.source_open_final = np.median(source_q[-60:, source_indices], axis=0) + self.source_evidence = { + "place_source": str(source), + "recording_60hz_sha256": hashlib.sha256(recording_path.read_bytes()).hexdigest(), + "place_metrics_sha256": hashlib.sha256(metrics_path.read_bytes()).hexdigest(), + "opening_start_row": int(opening_start), + "opening_end_row": int(opening_end), + "source_opening_samples": int(len(self.source_open_path)), + "release_t_20hz": float(metrics["release_t"]), + } + + self.allowed_target_links = { + f"/World/Robot/gripper_{hand}_inner_link4", + f"/World/Robot/gripper_{hand}_outer_link4", + } + + def _check(self, q, base, phase, sample_index, fraction, allowed_contacts): + if time.monotonic() > self.deadline: + raise RuntimeError("release sweep exceeded its deadline") + fk = self.robot.fk(self.names, q, base) + okay, detail = self.pose_check(fk, self.robot.root) + if not okay: + self.last = {"kind": "self_or_wrist", "phase": phase, "sample": sample_index, "detail": detail} + return False + for body in self.pose_check.bodies: + body.update(fk[body.name.replace("/World/Robot", self.robot.root, 1)]) + + for body in self.pose_check.bodies: + if self.broad_overlap(self.target, body): + hit = self.contact(self.target, body, containment=True) + if hit is not None: + if body.name in self.allowed_target_links: + allowed_contacts.append( + {"phase": phase, "sample": int(sample_index), "fraction": float(fraction), "link": body.name} + ) + else: + self.last = { + "kind": "released_target_robot", + "phase": phase, + "sample": int(sample_index), + "target": self.target.name, + "robot": body.name, + } + return False + if len(self.environment): + indices = np.flatnonzero(np.all(self.high >= body.lo, axis=1) & np.all(self.low <= body.hi, axis=1)) + for index in indices: + other = self.environment[index] + if self.contact(body, other, containment=True) is not None: + self.last = { + "kind": "released_world", + "phase": phase, + "sample": int(sample_index), + "robot": body.name, + "obstacle": other.name, + } + return False + return True + + def check(self, landing_q, retreat_q, base, target_pose): + landing_q = np.asarray(landing_q, dtype=float) + retreat_q = np.asarray(retreat_q, dtype=float) + base = np.asarray(base, dtype=float) + target_pose = np.asarray(target_pose, dtype=float) + if landing_q.shape != retreat_q.shape or landing_q.shape != (len(self.names),): + raise ValueError("release sweep requires two full robot joint vectors") + if base.shape != (4, 4) or target_pose.shape != (4, 4): + raise ValueError("release sweep base and target pose must be 4x4") + + self.target.update(target_pose) + # First bridge the measured live grasp to the source's initial passive + # linkage state, then follow every recorded source opening sample. + source_first = self.source_open_path[0] + bridge_count = max(2, int(math.ceil(np.max(np.abs(source_first - landing_q[self.gripper_indices])) / self.joint_step_rad)) + 1) + opening_path = [] + for fraction in np.linspace(0.0, 1.0, bridge_count): + q = landing_q.copy() + q[self.gripper_indices] = landing_q[self.gripper_indices] * (1.0 - fraction) + source_first * fraction + opening_path.append(q) + for values in self.source_open_path[1:]: + q = landing_q.copy() + q[self.gripper_indices] = values + opening_path.append(q) + while len(opening_path) < 121: + opening_path.append(opening_path[-1].copy()) + opened_q = opening_path[-1].copy() + opened_q[self.gripper_indices] = self.source_open_final + retreat_open_q = np.asarray(retreat_q, dtype=float).copy() + retreat_open_q[self.gripper_indices] = self.source_open_final + opening_count = len(opening_path) + retreat_count = max(61, int(math.ceil(np.max(np.abs(retreat_open_q - opened_q)) / self.joint_step_rad)) + 1) + allowed_contacts = [] + contact_samples = {"opening": set(), "retreat": set()} + samples = 0 + self.last = None + phase_paths = ( + ("opening", opening_path), + ("retreat", [opened_q * (1.0 - u) + retreat_open_q * u for u in np.linspace(0.0, 1.0, retreat_count)]), + ) + for phase, path in phase_paths: + count = len(path) + for sample_index, q in enumerate(path): + fraction = sample_index / max(1, count - 1) + samples += 1 + before = len(allowed_contacts) + if not self._check(q, base, phase, sample_index, fraction, allowed_contacts): + return False, { + "status": "BLOCKED", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "detail": self.last, + } + if len(allowed_contacts) > before: + contact_samples[phase].add(sample_index) + + # Assigned fingertips may initially touch the released object, but the + # final quarter must be separated and contact may not reappear after + # three consecutive clean retreat samples. + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late_contacts = sorted(i for i in contact_samples["retreat"] if i >= late_start) + clean_start = None + for index in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (index, index + 1, index + 2)): + clean_start = index + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if late_contacts or clean_start is None or recontacts: + self.last = { + "kind": "assigned_finger_target_failed_to_separate", + "late_contact_samples": late_contacts, + "first_three_clean_start": clean_start, + "recontact_samples": recontacts, + } + return False, { + "status": "BLOCKED", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "detail": self.last, + } + + report = { + "schema": "ziche.open_gripper_release_sweep.v1", + "status": "CLEAR", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "joint_step_rad_max": self.joint_step_rad, + "passive_finger_policy": "exact_accepted_place_source_60hz_articulation_trajectory", + "source_evidence": self.source_evidence, + "assigned_contact_samples": {key: sorted(value) for key, value in contact_samples.items()}, + "first_three_clean_retreat_start": clean_start, + "final_retreat_quarter_clear": True, + "no_recontact_after_three_clean_samples": True, + "all_live_props_checked": True, + "released_target_stationary_at_goal": True, + "pose_writes_or_resets": False, + } + self.last = None + return True, report diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/repair_place_station.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/repair_place_station.py new file mode 100644 index 0000000000000000000000000000000000000000..39db9c53f92ce1dea3c64e0ad51ae1da1885c70c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/repair_place_station.py @@ -0,0 +1,224 @@ +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/return_head_policy.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/return_head_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..04381392e5018fe8c6e8f7e3bebc4f4273e1d0f9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/return_head_policy.py @@ -0,0 +1,132 @@ +"""Physically recorded, collision-screened level gaze during empty-hand return.""" +from pathlib import Path +import hashlib +import numpy as np + +HEAD_JOINTS = ('idx11_head_joint1', 'idx12_head_joint2') +POLICY = 'empty_return_forward_head_v3' +BODY_PITCH_JOINT = 'idx02_body_joint2' +# Compensate torso pitch, retaining 0.5 degree margin inside native head limits. +PITCH_CLIP_RAD = tuple(np.deg2rad([-19.5, 29.5])) + +def head_target(q, names): + return (0.0, float(np.clip(-np.asarray(q)[list(names).index(BODY_PITCH_JOINT)], *PITCH_CLIP_RAD))) + +TRANSITION_SECONDS = 2.0 + + +def level_head(q, names): + result = np.asarray(q, dtype=float).copy() + for joint, value in zip(HEAD_JOINTS, head_target(q, names)): + result[list(names).index(joint)] = value + return result + + +def wrap_planner(planner): + def plan(stage, robot, names, q, base, prop_poses, next_pick_base, + next_pick_q, release, job): + from multi_motion import EmptyClearance + q = np.asarray(q, dtype=float) + base = np.asarray(base, dtype=float) + next_pick_base = np.asarray(next_pick_base, dtype=float) + next_pick_q = np.asarray(next_pick_q, dtype=float) + leveled = level_head(q, names) + next_leveled = level_head(next_pick_q, names) + check = EmptyClearance(stage, robot, names, prop_poses, + deadline_s=float(job.get('return_plan_budget_s', 900))) + if not check.segment(q, leveled, base, base): + raise RuntimeError('Return head raise is not clear: ' + str(check.last)) + if not check.segment(next_leveled, next_pick_q, next_pick_base, next_pick_base): + raise RuntimeError('Stopped shelf gaze restoration is not clear: ' + str(check.last)) + compact, primitives, report = planner(stage, robot, names, leveled, base, + prop_poses, next_pick_base, next_leveled, release, job) + if not any(p.get('bridge_before') for p in primitives): + raise RuntimeError('Level-head return requires an explicit shelf approach boundary') + report = dict(report) + report['head_policy'] = dict(policy=POLICY, joints=list(HEAD_JOINTS), + compensate_body_joint=BODY_PITCH_JOINT, pitch_clip_rad=list(PITCH_CLIP_RAD), transition_seconds=TRANSITION_SECONDS, + lift_before_table_retreat=True, includes_shelf_approach=True, + restore_source_gaze_only_after_stopped_arrival=True, + both_head_transitions_full_body_and_all_props_screened=True) + return compact, primitives, report + return plan + + +def transition(ctx, desired_head, kind): + from multi_motion import EmptyClearance + names = ctx['names']; controls = ctx['control_names']; ids = ctx['ids'] + native = ctx['native']; target = native[ids].copy() + head_ids = [controls.index(joint) for joint in HEAD_JOINTS] + desired_head = np.asarray(desired_head, dtype=float) + if desired_head.shape != (2,) or not np.isfinite(desired_head).all(): + raise ValueError('Expected two finite head joint targets') + if np.allclose(target[head_ids], desired_head, atol=1e-10, rtol=0): + return + ctx['base_vw'] = np.zeros(2) + ctx['constrain']('hold') + q0 = np.asarray(ctx['robot'].get_joint_positions(), dtype=float).copy() + q1 = q0.copy() + for joint, value in zip(HEAD_JOINTS, desired_head): + q1[names.index(joint)] = value + base = ctx['mat'](ctx['base']) + props = {path: ctx['mat'](body) for path, body in zip(ctx['prop_paths'], ctx['props'])} + check = EmptyClearance(ctx['stage'], ctx['carry_robot'], names, props, deadline_s=900) + if not check.segment(q0, q1, base, base): + raise RuntimeError('Live stopped head transition not clear: ' + str(check.last)) + tick0 = len(ctx['rows']) + before = target[head_ids].copy() + target[head_ids] = desired_head + ctx['item_phase']('empty_return') + ctx['blend'](target, TRANSITION_SECONDS) + actual = np.asarray(ctx['robot'].get_joint_positions(), dtype=float) + error = np.abs(actual[[names.index(j) for j in HEAD_JOINTS]] - desired_head) + if np.max(error) > np.deg2rad(0.1): + raise RuntimeError('Head did not reach return gaze before navigation: ' + str(error)) + receipt = dict(schema='multi_return_head_transition_v1', policy=POLICY, + item_index=ctx['item_index'], kind=kind, start_tick=tick0, + end_tick=len(ctx['rows']), head_joints=list(HEAD_JOINTS), + previous_command_rad=before.tolist(), target_rad=desired_head.tolist(), + measured_error_rad=error.tolist(), base_held=True, + all_live_props_screened=True, no_pose_writes=True, + source_path=str(Path(__file__).resolve()), + source_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest()) + ctx['dump'](ctx['out']/f"RETURN_HEAD_TRANSITION.item_{ctx['item_index']:02d}.{kind}.tick_{tick0:08d}.json", receipt) + + +def wrap_executor(ctx, executor): + def execute(primitives, held_reference=None, return_motion=False, item_job=None): + if not return_motion: + return executor(primitives, held_reference=held_reference, + return_motion=False, item_job=item_job) + # The planner screens every moving segment with this exact head pose. + if not ctx.get('RETURN_HEAD_WALKING_ACTIVE', False): + transition(ctx, head_target(ctx['native'], ctx['names']), 'raise_before_walking') + ctx['RETURN_HEAD_WALKING_ACTIVE'] = True + source_bridge = np.asarray(ctx['return_bridge_q'], dtype=float).copy() + ctx['return_bridge_q'] = level_head(source_bridge, ctx['control_names']) + try: + result = executor(primitives, held_reference=held_reference, + return_motion=True, item_job=item_job) + finally: + ctx['return_bridge_q'] = source_bridge + if any(p.get('bridge_before') for p in primitives): + # All base primitives have finished; only now look at the next item. + indices = [ctx['control_names'].index(j) for j in HEAD_JOINTS] + transition(ctx, source_bridge[indices], 'restore_pick_gaze_after_arrival') + ctx['RETURN_HEAD_WALKING_ACTIVE'] = False + return result + return execute + + +def install(ctx): + if ctx.get('RETURN_HEAD_POLICY_INSTALLED') == POLICY: + return + ctx['plan_return'] = wrap_planner(ctx['plan_return']) + ctx['execute_primitives'] = wrap_executor(ctx, ctx['execute_primitives']) + ctx['RETURN_HEAD_POLICY_INSTALLED'] = POLICY + ctx['dump'](ctx['out']/'RETURN_HEAD_POLICY.json', dict(policy=POLICY, + installed_at_control_tick=len(ctx['rows']), head_joints=list(HEAD_JOINTS), + compensate_body_joint=BODY_PITCH_JOINT, pitch_clip_rad=list(PITCH_CLIP_RAD), transition_seconds=TRANSITION_SECONDS, + restore_only_while_stopped_at_shelf=True, + implementation_path=str(Path(__file__).resolve()), + implementation_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/run_episode.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/run_episode.py new file mode 100644 index 0000000000000000000000000000000000000000..8640a2587c276dded659eb987f4c5d224d3e3816 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/run_episode.py @@ -0,0 +1,671 @@ +"""One continuous native rollout for every requested shelf-to-tray item. + +No pose writes after initial setup. Diagnostics never count as accepted episodes. +""" +import os,sys,json,math,time,traceback,gzip,subprocess,hashlib,importlib.util +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);job=json.loads((case/'job.json').read_text());R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +items=job.get('items') or [dict(target=job['target'],hand=job['hand'],product=job.get('product'),pick_source=job.get('pick_source'),pick_base=job['initial_base'],controls_file=str(case/'controls.npz'),destination_xyz=job['destination_xyz'])] +if not items:raise ValueError('job.items must contain at least one item') +def controls_path(item): + p=Path(item.get('controls_file',case/'controls.npz'));return p if p.is_absolute() else case/p +def load_controls(item):return np.load(controls_path(item),allow_pickle=False) +data=load_controls(items[0]);target_paths=[str(item['target']) for item in items] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'code/family3_repair/real2sim/teleop'),str(R/'work/family3_repair/collision/python_deps')] +os.environ.setdefault('SPATIALINDEX_C_LIBRARY',str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so')) +from isaacsim import SimulationApp +app=SimulationApp({'headless':True,'limit_cpu_threads':8}) +import omni.usd +from pxr import Usd,UsdGeom,UsdPhysics,PhysxSchema,Gf,PhysicsSchemaTools +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from isaacsim.core.api import SimulationContext +from isaacsim.core.prims import SingleArticulation,SingleRigidPrim +from isaacsim.core.utils.types import ArticulationAction +from omni.physx import get_physx_simulation_interface +from navigation import PrimitiveController + +class PrecisionReturnController: + """The native stop/turn/drive law with pickup-safe terminal tolerances.""" + def __init__(self,primitive):self.primitive=primitive;self.speed=0.;self.done=False + def step(self,current,dt=1/60): + goal=np.asarray(self.primitive['goal'],dtype=float);yaw=float(current[2]);heading=np.array([math.cos(yaw),math.sin(yaw)]) + if self.primitive['kind']=='turn': + error=math.atan2(math.sin(goal[2]-yaw),math.cos(goal[2]-yaw));desired=math.copysign(min(.25,max(.003,1.5*abs(error))),error) if abs(error)>.00009 else 0. + self.speed+=float(np.clip(desired-self.speed,-.3*dt,.3*dt));self.done=abs(error)<.00009 and abs(self.speed)<.0002 + return np.array([0.,0.,self.speed]) + error=goal[:2]-np.asarray(current[:2]);along=float(error@heading);lateral=float(error@np.array([-heading[1],heading[0]])) + if abs(lateral)>.015:raise RuntimeError(f'Straight drive lateral tracking error {lateral:.6f} m; replan instead of strafing') + if abs(math.atan2(math.sin(goal[2]-yaw),math.cos(goal[2]-yaw)))>.01:raise RuntimeError('Straight drive heading drift; stop and replan') + desired=math.copysign(min(.18,max(.0001,min(1.5*abs(along),.8*math.sqrt(2*.12*abs(along))))),along) if abs(along)>.0002 else 0. + self.speed+=float(np.clip(desired-self.speed,-.12*dt,.12*dt));self.done=abs(along)<.0002 and abs(self.speed)<.001 + return np.r_[heading*self.speed,0.] + +PRECISION_RETURN_POLICY=dict( + controller='PrecisionReturnController',scope='primitives_with_return_stage_only', + turn_deadband_rad=.00009,turn_done_error_rad=.00009,turn_done_speed_rad_s=.0002,turn_min_speed_rad_s=.003, + turn_min_speed_basis='native_static_friction_microturn_recoveries_layout07_layout10_0p003_rad_s', + drive_deadband_m=.0002,drive_done_error_m=.0002,drive_done_speed_m_s=.001,drive_min_speed_m_s=.0001, + speed_and_acceleration_caps_unchanged=True,static_friction_endpoint_acceptance_m_rad=[.0005,.0001],arrival_translation_gate_m=.0005,arrival_yaw_gate_rad=.0001) +out=case/'rollout';out.mkdir(exist_ok=False) +def dump(p,d): + tmp=p.with_suffix(p.suffix+'.tmp');tmp.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');tmp.replace(p) +def archive_exact(folder,digest,suffix,raw): + folder.mkdir(parents=True,exist_ok=True);path=folder/f'{digest}{suffix}' + if path.exists(): + if path.read_bytes()!=raw:raise RuntimeError('Hash archive collision or corrupted prior archive: '+str(path)) + else: + tmp=folder/f'.{digest}.{os.getpid()}.tmp';tmp.write_bytes(raw);tmp.replace(path) + return str(path) +def status(state,**extra):dump(case/'STATUS.json',dict(status=state,phase=phase,item_index=item_index,completed_objects=completed_objects,control_ticks=len(rows),updated=time.time(),base_pose=pose(base).tolist() if 'base' in globals() else None,target_pose=pose(target).tolist() if 'target' in globals() else None,**extra)) +phase='initializing';item_index=-1;completed_objects=[];item_events=[];planning_waits=[] +rows=[];target_rows=[];target_all_rows=[];active_target_rows=[];item_index_rows=[];base_rows=[];measured=[];commands=[];base_commands=[];phases=[];nav_modes=[];nav_anchors=[];times=[];joint_velocities=[];link_rows=[] +omni.usd.get_context().open_stage(job['scene']);stage=omni.usd.get_context().get_stage() +# Released rug surfaces are visual floor dressing, not physical step obstacles. +# Preserve those authored semantics; the real floor collider remains enabled. +released=Usd.Stage.Open(job['layout']);floor_dressing=[] +rug=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') +if rug: + for p in Usd.PrimRange(rug): + authored=released.GetPrimAtPath(p.GetPath()) + if p.HasAPI(UsdPhysics.CollisionAPI) and authored and not authored.HasAPI(UsdPhysics.CollisionAPI): + UsdPhysics.CollisionAPI(p).GetCollisionEnabledAttr().Set(False);floor_dressing.append(str(p.GetPath())) +dump(out/'RELEASED_FLOOR_DRESSING.json',dict(policy='preserve_original_nonphysical_floor_dressing',paths=floor_dressing,real_floor_collision_retained=True)) +# The old fixed task root joint must not make the articulation fixed-base. +root_anchor=UsdPhysics.FixedJoint(stage.GetPrimAtPath('/World/Robot/base_link/FixedJoint')) +root_anchor.CreateExcludeFromArticulationAttr(True);root_anchor.GetJointEnabledAttr().Set(False) +# A free articulation otherwise picks a graph-center link as its root. +stage.GetPrimAtPath('/World/Robot').RemoveAPI(UsdPhysics.ArticulationRootAPI) +base_prim=stage.GetPrimAtPath('/World/Robot/base_link');UsdPhysics.ArticulationRootAPI.Apply(base_prim) +PhysxSchema.PhysxArticulationAPI.Apply(base_prim).CreateEnabledSelfCollisionsAttr(True) +# Navigation uses native constraints and velocity drives, not root teleportation. +nav_joints={};nav_drives={} +for kind,cls,axis,drive_name in [('turn',UsdPhysics.RevoluteJoint,'Z','angular'),('drive',UsdPhysics.PrismaticJoint,'X','linear'),('hold',UsdPhysics.FixedJoint,None,None)]: + joint=cls.Define(stage,'/World/ChainNavigation_'+kind);joint.CreateBody1Rel().SetTargets(['/World/Robot/base_link']);joint.CreateExcludeFromArticulationAttr(True);joint.CreateJointEnabledAttr(False);joint.CreateLocalPos0Attr(Gf.Vec3f(0));joint.CreateLocalRot0Attr(Gf.Quatf(1));joint.CreateLocalPos1Attr(Gf.Vec3f(0));joint.CreateLocalRot1Attr(Gf.Quatf(1)) + if axis: + joint.CreateAxisAttr(axis);joint.CreateLowerLimitAttr(-170. if kind=='turn' else -15.);joint.CreateUpperLimitAttr(170. if kind=='turn' else 15.) + d=UsdPhysics.DriveAPI.Apply(joint.GetPrim(),drive_name);d.CreateTypeAttr('force');d.CreateStiffnessAttr(0.);d.CreateDampingAttr(100000.);d.CreateMaxForceAttr(100000.);d.CreateTargetVelocityAttr(0.);nav_drives[kind]=d + nav_joints[kind]=joint +for p in stage.Traverse(): + if p.HasAPI(UsdPhysics.RigidBodyAPI):PhysxSchema.PhysxContactReportAPI.Apply(p).CreateThresholdAttr().Set(0.) +from tray_appearance import apply as apply_tray_appearance +dump(out/'TRAY_APPEARANCE.json',apply_tray_appearance(stage,job)) +# All exact runtime settings are exported before stepping. +for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')): + if p.GetName() in ['idx41_gripper_l_outer_joint1','idx81_gripper_r_outer_joint1']: + d=UsdPhysics.DriveAPI.Apply(p,'angular');d.GetMaxForceAttr().Set(80.) +from robot_collision_profile import audit_full_robot_collision_profile +profile=audit_full_robot_collision_profile(stage);dump(out/'ROBOT_COLLISION_PROFILE.json',profile) +params={p.GetName():{a.GetName():str(a.Get()) for a in p.GetAttributes() if a.GetName().startswith(('physics:','physx','drive:'))} for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')) if p.IsA(UsdPhysics.Joint)} +dump(out/'robot_and_camera_parameters.json',params);stage.GetRootLayer().Export(str(out/'scene.usda')) +sim=SimulationContext(physics_dt=1/240,rendering_dt=1/60);sim.initialize_physics();sim.play() +assert abs(sim.get_physics_dt()-1/240)<1e-12, 'Physics timestep must be 240 Hz' +dump(out/'PHYSICS_CLOCK.json',dict(physics_dt_s=sim.get_physics_dt(),physics_hz=1/sim.get_physics_dt(),substeps_per_control=4,control_hz=60,training_hz=20)) +# Save runtime units/up-axis after SimulationContext authors them. +stage.GetRootLayer().Export(str(out/'scene.usda')) +robot=SingleArticulation('/World/Robot/base_link');robot.initialize();names=list(robot.dof_names);control_names=data['names'].tolist();ids=np.array([names.index(n) for n in control_names]) +native=np.zeros(len(names));source_names=job['source_dof_names'];sourceq=np.asarray(job['source_initial_q']) +for i,n in enumerate(names):native[i]=sourceq[source_names.index(n)] if n in source_names else 0. +native[ids]=data['initial'];base0=np.asarray(job['initial_base']);bq=Rot.from_matrix(base0[:3,:3]).as_quat();robot.set_world_pose(base0[:3,3],np.r_[bq[3],bq[:3]]);robot.set_joint_positions(native);robot.set_joint_velocities(np.zeros(len(names))) +efforts=np.asarray(robot.get_articulation_controller().get_max_efforts()).reshape(-1);drive_readback={n:float(efforts[names.index(n)]) for n in control_names[18:]} +assert all(abs(v-80.)<1e-5 for v in drive_readback.values()),drive_readback +dump(out/'LIVE_DRIVE_READBACK.json',drive_readback) +params['dof_names']=names;params['action_names']=control_names+['base_forward_m_s','base_yaw_rate_rad_s'];params['runtime_gripper_max_effort_Nm']=drive_readback;params['cameras']={} +for key,path in [('top_head','/World/Robot/head_link2/Head_Camera'),('hand_left','/World/Robot/gripper_l_base_link/Left_Camera'),('hand_right','/World/Robot/gripper_r_base_link/Right_Camera')]: + camera=stage.GetPrimAtPath(path);params['cameras'][key]=dict(path=path,attributes={a.GetName():str(a.Get()) for a in camera.GetAttributes()},world_extrinsics='camera_extrinsics_20hz.npz') +dump(out/'robot_and_camera_parameters.json',params) + +base=robot +target_objects=[] +for path in target_paths: + rb=SingleRigidPrim(path);rb.initialize();target_objects.append(rb) +target=target_objects[0];hand=SingleRigidPrim('/World/Robot/gripper_'+items[0]['hand']+'_base_link');hand.initialize() +link_paths=[str(p.GetPath()) for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')) if p.HasAPI(UsdPhysics.RigidBodyAPI)];links=[] +for path in link_paths: + rb=robot if path=='/World/Robot/base_link' else SingleRigidPrim(path) + if rb is not robot:rb.initialize() + links.append(rb) +prop_paths=[str(p.GetPath()) for p in stage.Traverse() if not str(p.GetPath()).startswith('/World/Robot/') and p.HasAPI(UsdPhysics.RigidBodyAPI) and UsdPhysics.RigidBodyAPI(p).GetRigidBodyEnabledAttr().Get()] +props=[];prop_rows=[] +for path in prop_paths: + rb=SingleRigidPrim(path);rb.initialize();props.append(rb) +def pose(rb): + p,q=rb.get_world_pose();return np.r_[p,q[1:],q[0]] +def mat(rb): + p=pose(rb);m=np.eye(4);m[:3,:3]=Rot.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m +def base_state(): + p=pose(base);return np.r_[p[:2],Rot.from_quat(p[3:]).as_euler('xyz')[2]] +def held():return np.linalg.inv(mat(hand))@mat(target) +requested_initial_target_pose=np.stack([pose(rb) for rb in target_objects]) +dump(out/'TARGET_INITIAL_POSES.json',dict(target_paths=target_paths,pose_order='x,y,z,qx,qy,qz,qw',target_pose=requested_initial_target_pose.tolist(),capture='before_episode_motion')) +nav_mode='hold';base_vw=np.zeros(2);anchor=stage.GetPrimAtPath('/World/Robot/base_link/FixedJoint');anchor.GetAttribute('physics:jointEnabled').Set(False) +def constrain(kind,recorded_anchor=None): + global nav_mode,nav_anchor + nav_mode=kind;p=pose(base) if recorded_anchor is None else np.asarray(recorded_anchor);nav_anchor=p.copy() + for j in nav_joints.values():j.GetJointEnabledAttr().Set(False) + j=nav_joints[kind];j.GetLocalPos0Attr().Set(Gf.Vec3f(*map(float,p[:3])));j.GetLocalRot0Attr().Set(Gf.Quatf(float(p[6]),Gf.Vec3f(*map(float,p[3:6]))));j.GetJointEnabledAttr().Set(True) + get_physx_simulation_interface().flush_changes() +constrain('hold') +assert np.linalg.norm(pose(base)[:3]-base0[:3,3])<.002,('Initial physical base does not match requested station',pose(base),base0[:3,3]) +# Settle the real native scene under the exact first recorded command before any +# dataset row, contact, or camera frame is captured. This is ordinary physics: +# no pose write, reset, frozen body, or hidden control row occurs here. +PREROLL_HZ=60;PREROLL_WINDOW=30;PREROLL_MAX_SAMPLES=60*PREROLL_HZ +PREROLL_THRESHOLDS=dict(prop_translation_m=0.00005,prop_rotation_rad=float(np.deg2rad(.02)), + robot_translation_m=0.00005,robot_rotation_rad=float(np.deg2rad(.02)), + camera_translation_m=0.00005,camera_rotation_rad=float(np.deg2rad(.01)),passive_finger_projected_motion_px=0.25) +camera_link_indices=[link_paths.index(p) for p in ['/World/Robot/head_link2','/World/Robot/gripper_l_base_link','/World/Robot/gripper_r_base_link']] +from preroll_stability import build_projection_model,evaluate_projection +projection_model=build_projection_model(stage,link_paths);last_projection=None +pre_q=[];pre_qv=[];pre_base=[];pre_link=[];pre_prop=[];pre_metrics=[] +initial_prop_pose_before_preroll=np.stack([pose(rb) for rb in props]);initial_robot_link_pose_before_preroll=np.stack([pose(rb) for rb in links]) +initial_prop_maxima=dict(translation_m=0.,downward_m=0.,rotation_rad=0.) +def check_initial_props(): + current=np.asarray(pre_prop[-1],dtype=np.float64);initial=np.asarray(initial_prop_pose_before_preroll,dtype=np.float64) + displacement=np.linalg.norm(current[:,:3]-initial[:,:3],axis=1);downward=initial[:,2]-current[:,2] + a=current[:,3:]/np.linalg.norm(current[:,3:],axis=1,keepdims=True);b=initial[:,3:]/np.linalg.norm(initial[:,3:],axis=1,keepdims=True) + angle=2*np.arccos(np.clip(np.abs(np.sum(a*b,axis=1)),0.,1.)) + initial_prop_maxima['translation_m']=max(initial_prop_maxima['translation_m'],float(displacement.max(initial=0.))) + initial_prop_maxima['downward_m']=max(initial_prop_maxima['downward_m'],float(downward.max(initial=0.))) + initial_prop_maxima['rotation_rad']=max(initial_prop_maxima['rotation_rad'],float(angle.max(initial=0.))) + bad=np.flatnonzero((displacement>.02)|(downward>.01)|(angle>np.deg2rad(15.))) + if len(bad): + failures=[dict(path=prop_paths[int(i)],translation_m=float(displacement[i]),downward_m=float(downward[i]),rotation_rad=float(angle[i])) for i in bad] + dump(out/'INITIAL_PROP_NO_FALL.json',dict(passed=False,physics_sample=len(pre_prop),failures=failures,thresholds=dict(translation_m=.02,downward_m=.01,rotation_deg=15.),all_requested_and_nonrequested_props_included=True)) + raise RuntimeError('Initial scene prop moved or fell before capture: '+json.dumps(failures)) + +def pose_span(a): + if a.shape[1]==0:return 0.,0. + xyz=np.asarray(a[:,:,:3],dtype=np.float64);quat=np.asarray(a[:,:,3:],dtype=np.float64) + quat/=np.linalg.norm(quat,axis=-1,keepdims=True) + dx=xyz[:,None,:,:]-xyz[None,:,:,:] + trans=float(np.linalg.norm(dx,axis=-1).max()) + dots=np.abs(np.einsum('tni,sni->tsn',quat,quat));dots=np.clip(dots,-1.,1.) + angle=float((2*np.arccos(dots)).max()) + return trans,angle +def window_metrics(): + l=np.asarray(pre_link[-PREROLL_WINDOW:]);p=np.asarray(pre_prop[-PREROLL_WINDOW:]) + m=geometry_metrics(l,p) + q=np.asarray(pre_q[-PREROLL_WINDOW:]);qv=np.asarray(pre_qv[-PREROLL_WINDOW:]) + m.update(joint_span=float(np.ptp(q,axis=0).max()),joint_speed_max=float(np.abs(qv).max())) + return m +def geometry_metrics(l,p): + lt,lr=pose_span(l);pt,pr=pose_span(p);ct,cr=pose_span(l[:,camera_link_indices]);dt,dr=pose_span(l[:,projection_model['driven_indices']]) + return dict(robot_link_translation_span_m=lt,robot_link_rotation_span_rad=lr, + prop_translation_span_m=pt,prop_rotation_span_rad=pr, + camera_link_translation_span_m=ct,camera_link_rotation_span_rad=cr, + driven_robot_link_translation_span_m=dt,driven_robot_link_rotation_span_rad=dr) +def stable(m): + t=PREROLL_THRESHOLDS + return (m['driven_robot_link_translation_span_m']<=t['robot_translation_m'] and m['driven_robot_link_rotation_span_rad']<=t['robot_rotation_rad'] + and m['prop_translation_span_m']<=t['prop_translation_m'] and m['prop_rotation_span_rad']<=t['prop_rotation_rad'] + and m['camera_link_translation_span_m']<=t['camera_translation_m'] and m['camera_link_rotation_span_rad']<=t['camera_rotation_rad']) +phase='physics_preroll_unrecorded' +if len(sys.argv)>2: + preroll_source=np.load(sys.argv[2]);preroll_source_ids=[preroll_source['dof_names'].tolist().index(n) for n in control_names] + initial_command=np.asarray(preroll_source['native_target'][0,preroll_source_ids],dtype=float).copy() +else:initial_command=np.asarray(data['initial'],dtype=float).copy() +native[ids]=initial_command +try: + for sample in range(PREROLL_MAX_SAMPLES-3): + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + if len(pre_q)>=PREROLL_WINDOW: + m=window_metrics();m['sample']=sample+1;m['time_s']=(sample+1)/PREROLL_HZ;pre_metrics.append(m) + # Exact mesh projection is intentionally sparse: it is expensive and passive + # solver jitter changes slowly. Recheck at most once per 0.5 s, while the + # strict prop/camera/driven-link metrics remain evaluated every sample. + if stable(m) and (last_projection is None or (sample+1)%PREROLL_WINDOW==0): + last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + if last_projection['status']=='PASSED':break + else:raise RuntimeError('Native scene did not meet startup stability thresholds within 60 seconds') + evidence=dict(status='PASSED',physics_hz=240,sample_hz=PREROLL_HZ,steps=4*len(pre_q),duration_s=len(pre_q)/PREROLL_HZ, + stability_window_s=PREROLL_WINDOW/PREROLL_HZ,thresholds=PREROLL_THRESHOLDS,final_window=pre_metrics[-1],passive_finger_projection=last_projection, + exact_initial_command_sha256=hashlib.sha256(initial_command.tobytes()).hexdigest(),recorded_rows=0, + contact_capture_started=False,camera_capture_started=False,no_pose_writes_or_resets=True, + initial_snapshot_semantics='Immediate native readback after scene setup and hold constraint, before the first preroll physics step; no pose write/reset.', + initial_prop_pose_before_preroll_sha256=hashlib.sha256(initial_prop_pose_before_preroll.tobytes()).hexdigest()) + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics])) + dump(out/'PREROLL_EVIDENCE.json',evidence) +except Exception as exc: + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics])) + dump(out/'PREROLL_EVIDENCE.json',dict(status='FAILED',error=repr(exc),physics_hz=240,duration_s=len(pre_q)/PREROLL_HZ, + thresholds=PREROLL_THRESHOLDS,final_window=pre_metrics[-1] if pre_metrics else None,passive_finger_projection=last_projection, + recorded_rows=0,contact_capture_started=False,camera_capture_started=False)) + status('FAILED',error='Physics preroll: '+repr(exc));app.close();raise + +contact_stream=None +contact_callback_rows=0;recent_support_contacts={path:[] for path in target_paths} +recent_grasp_contacts={path:{'inner':[],'outer':[],'environment':[]} for path in target_paths} +def on_contact(headers,points): + global contact_callback_rows + for h in headers: + if not h.num_contact_data:continue + actors=[str(PhysicsSchemaTools.intToSdfPath(x)) for x in [h.actor0,h.actor1]] + colliders=[str(PhysicsSchemaTools.intToSdfPath(x)) for x in [h.collider0,h.collider1]] + if not any(a.startswith('/World/Robot/') or any(a==p or a.startswith(p+'/') for p in target_paths) for a in actors+colliders):continue + selected=points[h.contact_data_offset:h.contact_data_offset+h.num_contact_data] + row=dict(tick=len(rows)+1,phase=phase,item_index=item_index,active_target_path=target_paths[item_index] if item_index>=0 else '',actors=actors,colliders=colliders,points=int(h.num_contact_data),min_separation_m=min(float(p.separation) for p in selected),impulse_sum=[float(sum(p.impulse[k] for p in selected)) for k in range(3)]);contact_stream.write(json.dumps(row)+'\n');contact_callback_rows+=1 + for requested in target_paths: + touches_target=any(a==requested or a.startswith(requested+'/') for a in actors+colliders) + touches_tray=any(a=='/World/Assets/Tray' or a.startswith('/World/Assets/Tray/') for a in actors+colliders) + if touches_target and touches_tray:recent_support_contacts[requested].append(len(rows)+1) + if item_index>=0: + requested=target_paths[item_index];gripper_prefix='/World/Robot/gripper_'+items[item_index]['hand'] + touches_target=any(a==requested or a.startswith(requested+'/') for a in actors+colliders) + if touches_target: + evidence=recent_grasp_contacts[requested];tick=len(rows)+1 + if any(a==gripper_prefix+'_inner_link4' or a.startswith(gripper_prefix+'_inner_link4/') for a in actors+colliders):evidence['inner'].append(tick) + if any(a==gripper_prefix+'_outer_link4' or a.startswith(gripper_prefix+'_outer_link4/') for a in actors+colliders):evidence['outer'].append(tick) + other_nonrobot=any(a and not (a==requested or a.startswith(requested+'/') or a.startswith('/World/Robot/')) for a in actors+colliders) + if other_nonrobot:evidence['environment'].append(tick) + for values in evidence.values(): + if len(values)>2000:del values[:-1000] +subscription=None +# Capture cameras from the same physical simulation and the same sampling ticks. +writers={};renderers={} +if os.environ.get('CHAIN_RENDER','1')=='1': + import omni.replicator.core as rep + import subprocess + for key,path,w,h in [('top_head','/World/Robot/head_link2/Head_Camera',1280,800),('hand_left','/World/Robot/gripper_l_base_link/Left_Camera',848,480),('hand_right','/World/Robot/gripper_r_base_link/Right_Camera',848,480)]: + product=rep.create.render_product(path,(w,h));ann=rep.AnnotatorRegistry.get_annotator('rgb');ann.attach([product]);renderers[key]=(product,ann,w,h) + # Prime the renderer after stability. Replicator's annotator graph requires a + # physics update after attachment before its first safe read, so three further + # unrecorded hold samples are included in the preroll evidence below. + for _ in range(12):sim.render() + for _ in range(3): + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera warmup frame missing') + m=window_metrics() + last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + warmup_samples=3 + while not stable(m) or last_projection['status']!='PASSED': + if len(pre_q)>=PREROLL_MAX_SAMPLES:raise RuntimeError('Camera priming did not recover strict startup stability within 60 seconds') + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + warmup_samples+=1;sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera rewarm frame missing') + m=window_metrics() + if stable(m):last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + evidence.update(steps=4*len(pre_q),duration_s=len(pre_q)/PREROLL_HZ,final_window=m,camera_warmup_physics_samples=warmup_samples, + discarded_render_reads=warmup_samples,passive_finger_projection=last_projection) + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics]+[json.dumps(m)])) + dump(out/'PREROLL_EVIDENCE.json',evidence) + for key,(_,ann,w,h) in renderers.items(): + writers[key]=subprocess.Popen(['ffmpeg','-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{w}x{h}','-r','20','-i','-','-c:v','libx264','-preset','fast','-threads','2','-crf','21','-pix_fmt','yuv420p',str(out/(key+'.mp4'))],stdin=subprocess.PIPE) + dump(out/'CAMERA_WARMUP.json',dict(status='PASSED',after_physics_stability=True,discarded_render_reads=warmup_samples,render_only_warmups=12, + unrecorded_hold_samples=warmup_samples,captured_frames=0,final_stability_window=m)) +dump(out/'INITIAL_PROP_NO_FALL.json',dict(passed=True,physics_samples=len(pre_prop),maxima=initial_prop_maxima,thresholds=dict(translation_m=.02,downward_m=.01,rotation_deg=15.),all_requested_and_nonrequested_props_included=True)) +contact_stream=gzip.open(out/'contacts.jsonl.gz','wt',compresslevel=3) +subscription=get_physx_simulation_interface().subscribe_contact_report_events(on_contact) +camera_sample_ticks=[];camera_sample_extrinsics=[] +startup_frames=out/'startup_frames';startup_frames.mkdir(exist_ok=True) +def capture_camera_pose(): + transforms=[] + for c in projection_model['cameras']: + pp=pose(links[c['link_index']]);m=np.eye(4,dtype=np.float64);m[:3,:3]=Rot.from_quat(pp[3:]).as_matrix();m[:3,3]=pp[:3] + transforms.append(m@c['relative']) + return np.asarray(transforms,dtype=np.float64) + +def write_startup_report(): + first_n=min(30,len(link_rows));first_m=geometry_metrics(np.asarray(link_rows[:first_n]),np.asarray(prop_rows[:first_n]));first_projection=evaluate_projection(np.asarray(link_rows[:first_n]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + passed=bool(first_n==30 and stable(first_m) and last_projection and last_projection['status']=='PASSED' and first_projection['status']=='PASSED') + report=dict(status='PASSED' if passed else 'FAILED',passed=passed,threshold_px=PREROLL_THRESHOLDS['passive_finger_projected_motion_px'],only_passive_distal_fingers=True,passive_path_pattern='/gripper_[lr]_(inner|outer)_link[234]$',strict_thresholds=PREROLL_THRESHOLDS, + windows=[dict(label='final_preroll_0p5s',row_range=[-30,-1],strict_geometry=evidence['final_window'],passive_projection=last_projection,passed=bool(stable(evidence['final_window']) and last_projection['status']=='PASSED')),dict(label='first_retained_30_control_rows',row_range=[0,first_n-1],strict_geometry=first_m,passive_projection=first_projection,passed=bool(first_n==30 and stable(first_m) and first_projection['status']=='PASSED'))], + evaluated_passive_paths=[b['path'] for b in projection_model['bodies']],evaluated_driven_paths=[link_paths[i] for i in projection_model['driven_indices']],evaluated_cameras=[c['path'] for c in projection_model['cameras']],scene_source=str(Path(job['scene']).resolve()),scene_sha256=hashlib.sha256(Path(job['scene']).read_bytes()).hexdigest(),projection_implementation=str((W/'preroll_stability.py').resolve()),projection_implementation_sha256=hashlib.sha256((W/'preroll_stability.py').read_bytes()).hexdigest()) + dump(out/'PASSIVE_FINGER_PROJECTION.json',report) + return passed + +def advance(q): + global native + q=np.asarray(q,dtype=float);assert q.shape==(20,) and np.isfinite(q).all() + if q[14]<-1e-6 or q[14]>.55+1e-6 or np.any(q[18:]<0) or np.any(q[18:]>np.pi/4+1e-6):raise ValueError('Action exceeds configured lift/grip limit') + native[ids]=q + robot.apply_action(ArticulationAction(joint_positions=q,joint_indices=ids)) + if nav_mode=='drive':nav_drives['drive'].GetTargetVelocityAttr().Set(float(base_vw[0])) + elif nav_mode=='turn':nav_drives['turn'].GetTargetVelocityAttr().Set(float(np.rad2deg(base_vw[1]))) + for _ in range(4):sim.step(render=False) + applied=np.asarray(robot.get_applied_action().joint_positions) + assert np.allclose(applied[ids],q,atol=1e-6), 'Applied target readback mismatch' + tick=len(rows)+1;actual=robot.get_joint_positions();bp=pose(base);tp=pose(target);all_targets=np.stack([pose(rb) for rb in target_objects]) + nav_modes.append(nav_mode);nav_anchors.append(nav_anchor.copy());rows.append(tick);measured.append(actual.copy());commands.append(native.copy());joint_velocities.append(robot.get_joint_velocities().copy());base_rows.append(bp);target_rows.append(tp);target_all_rows.append(all_targets);active_target_rows.append(target_paths[item_index] if item_index>=0 else '');item_index_rows.append(item_index);base_commands.append(base_vw.copy());phases.append(phase);link_rows.append(np.stack([pose(rb) for rb in links]));prop_rows.append(np.stack([pose(rb) for rb in props])) + if tick%3==0 and writers: + camera_sample_ticks.append(tick);camera_sample_extrinsics.append(capture_camera_pose()) + sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera frame missing at training tick') + writers[key].stdin.write(np.ascontiguousarray(rgb[:,:,:3]).tobytes()) + if tick<=30: + from PIL import Image + Image.fromarray(np.ascontiguousarray(rgb[:,:,:3])).save(startup_frames/f'{key}_{tick//3-1:02d}.jpg',quality=92) + if tick==30 and not write_startup_report():raise RuntimeError('First recorded 0.5 seconds failed strict stable-start gate') + if tick%60==0:status('RUNNING_PHYSICS') + if not np.isfinite(actual).all() or not np.isfinite(tp).all():raise RuntimeError('Nonfinite native physics state') + +def blend(end,seconds,held_reference=None): + start=native[ids].copy();end=np.asarray(end).copy();duration=max(seconds,abs(end[14]-start[14])*1.875/.1,abs(end[15]-start[15])*1.875/.5,float(np.max(abs(end[:14]-start[:14])))*1.875/.35) + for i in range(math.ceil(duration*60)): + u=(i+1)/math.ceil(duration*60);a=10*u**3-15*u**4+6*u**5;advance(start+a*(end-start)) + if held_reference is not None and np.linalg.norm(held()[:3,3]-held_reference[:3,3])>.025:raise RuntimeError('Target slipped during '+phase) + +def item_phase(name): + global phase + phase=f'item_{item_index:02d}_{name}' + +def full_q(control_q,control_data): + q=np.asarray(robot.get_joint_positions(),dtype=float).copy();cn=control_data['names'].tolist() + if cn!=control_names:raise ValueError('Every controls file must use the same ordered 20 action names') + q[[names.index(n) for n in cn]]=np.asarray(control_q,dtype=float);return q + +def grasp_contact_gate(path,item_job): + window_ticks=30;minimum_bilateral=max(15,int(item_job.get('pick_grasp_contact_min_bilateral_ticks',15)));tail_ticks=3;environment_clear_ticks=15 + if minimum_bilateral>window_ticks:raise ValueError('Invalid pickup bilateral grasp-contact gate configuration') + end=len(rows);start=max(1,end-window_ticks+1);tail_start=max(start,end-tail_ticks+1);environment_start=max(1,end-environment_clear_ticks+1);raw=recent_grasp_contacts[path] + inner={int(t) for t in raw['inner'] if start<=int(t)<=end};outer={int(t) for t in raw['outer'] if start<=int(t)<=end};bilateral=sorted(inner&outer);tail_bilateral=[t for t in bilateral if t>=tail_start];environment=sorted({int(t) for t in raw['environment'] if environment_start<=int(t)<=end}) + evidence=dict(active_target=path,active_hand=item_job['hand'],gripper_prefix='/World/Robot/gripper_'+item_job['hand'],window_control_ticks=window_ticks,window_start_tick=start,window_end_tick=end,inner_contact_ticks=sorted(inner),outer_contact_ticks=sorted(outer),simultaneous_bilateral_contact_ticks=bilateral,simultaneous_bilateral_contact_tick_count=len(bilateral),minimum_simultaneous_bilateral_ticks=minimum_bilateral,final_tail_control_ticks=tail_ticks,final_tail_bilateral_ticks=tail_bilateral,environment_clear_control_ticks=environment_clear_ticks,environment_contact_ticks=environment,native_contact_required=True) + if len(bilateral)xy_gate or yaw_error>yaw_gate:raise RuntimeError('Blocked navigation requires a screened live-state recovery to its endpoint: '+json.dumps(metrics,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) + return metrics + # Include this measured failure in the repair key so a prior repair file can + # never be consumed for a later partially executed primitive. + boundary_kind=('return_motion' if return_motion else 'carry_motion')+'_'+failure_sha[:12] + recovered=planning_boundary(boundary_kind,item_job,endpoint_call) + dump(out/f'NAVIGATION_MOTION_RECOVERY.item_{item_index:02d}.{failure_sha[:12]}.json',recovered) + return recovered + +def execute_primitives(primitives,held_reference=None,return_motion=False,item_job=None): + global base_vw + if item_job is None:item_job=globals().get('item_job') + if not isinstance(item_job,dict) or item_index<0 or item_index>=len(target_paths) or item_job.get('target')!=target_paths[item_index]:raise ValueError('execute_primitives requires the active item_job matching item_index and target') + for k,p in enumerate(primitives): + if return_motion and p.get('bridge_before'): + base_vw=np.zeros(2);constrain('hold');blend(return_bridge_q,4.) + if not return_motion and k==1: + base_vw=np.zeros(2);constrain('hold');item_phase('carry_raise') + raised=native[ids].copy();raised[14]=.55;blend(raised,3.,held_reference) + item_phase('carry_compact');compact=native[ids].copy();compact[:14]=carry_compact_q[ids][:14];blend(compact,4.,held_reference) + if return_motion:item_phase('empty_return') + else:item_phase('shelf_exit_reverse' if k==0 else 'forward_navigation') + constrain(p['kind']);precision=bool(return_motion and p.get('return_stage'));controller=PrecisionReturnController(p) if precision else PrimitiveController(p);last_progress=base_state().copy();stalled_ticks=0 + primitive_complete=False;failure_reason=None + for _ in range(90*60): + cur=base_state() + try:v=controller.step(cur) + except Exception as exc: + failure_reason='controller_error: '+repr(exc);break + base_vw=np.array([np.cos(cur[2])*v[0]+np.sin(cur[2])*v[1],v[2]]);advance(native[ids]);post=base_state() + if held_reference is not None and np.linalg.norm(held()[:3,3]-held_reference[:3,3])>.025:raise RuntimeError('Target slipped in navigation') + if controller.done: + if precision: + goal=np.asarray(p['goal'],dtype=float);xy_error=float(np.linalg.norm(post[:2]-goal[:2]));yaw_error=float(abs(math.atan2(math.sin(post[2]-goal[2]),math.cos(post[2]-goal[2])))) + endpoint=dict(schema='ziche.precision_return_endpoint.v1',primitive_index=k,primitive=p,base=post.tolist(),xy_error_m=xy_error,yaw_error_rad=yaw_error,xy_gate_m=PRECISION_RETURN_POLICY['arrival_translation_gate_m'],yaw_gate_rad=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'],measured_after_advance=True) + dump(out/f'PRECISION_RETURN_ENDPOINT.item_{item_index:02d}.primitive_{k:02d}.tick_{len(rows):08d}.json',endpoint) + if xy_error>PRECISION_RETURN_POLICY['arrival_translation_gate_m'] or yaw_error>PRECISION_RETURN_POLICY['arrival_yaw_gate_rad']: + failure_reason='precision_controller_done_outside_measured_endpoint_gate: '+json.dumps(endpoint);break + primitive_complete=True;break + moved=np.linalg.norm(post[:2]-last_progress[:2]) if p['kind']=='drive' else abs(math.atan2(math.sin(post[2]-last_progress[2]),math.cos(post[2]-last_progress[2]))) + progress_epsilon=(.00001 if p['kind']=='drive' else .000002) if precision else .003 + if moved>progress_epsilon:last_progress=post.copy();stalled_ticks=0 + else:stalled_ticks+=1 + if stalled_ticks>=6*60: + if precision: + terminal_state=base_state();goal=np.asarray(p['goal'],dtype=float);xy_error=float(np.linalg.norm(terminal_state[:2]-goal[:2]));yaw_error=float(abs(math.atan2(math.sin(terminal_state[2]-goal[2]),math.cos(terminal_state[2]-goal[2])))) + within_terminal=xy_error<=PRECISION_RETURN_POLICY['arrival_translation_gate_m'] and yaw_error<=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'] + if within_terminal: + dump(out/f'PRECISION_STATIC_FRICTION_TERMINAL.item_{item_index:02d}.tick_{len(rows):08d}.json',dict(primitive_index=k,primitive=p,base=terminal_state.tolist(),command=base_vw.tolist(),stalled_seconds=stalled_ticks/60.,xy_error_m=xy_error,yaw_error_rad=yaw_error,xy_gate_m=PRECISION_RETURN_POLICY['arrival_translation_gate_m'],yaw_gate_rad=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'],measured_after_advance=True,accepted_by_measured_endpoint_gate=True));primitive_complete=True;break + dump(out/f'NAVIGATION_BLOCKED.item_{item_index:02d}.json',dict(primitive_index=k,primitive=p,base=base_state().tolist(),command=base_vw.tolist(),stalled_seconds=stalled_ticks/60.)) + failure_reason='navigation_blocked_for_6_seconds';break + if not primitive_complete: + if failure_reason is None:failure_reason='navigation_primitive_timeout_90_seconds' + motion_recovery_boundary(p,k,failure_reason,item_job,return_motion) + base_vw=np.zeros(2);constrain('hold') + +def planning_boundary(kind,item_job,planner): + """Retry only a failed planner in-process after an explicit repair artifact.""" + repair_dir=case/'repairs';repair_dir.mkdir(exist_ok=True);repair=repair_dir/f'item_{item_index:02d}_{kind}.json';seen=None + while True: + try:return planner() + except Exception as exc: + traceback.print_exc();record=dict(item_index=item_index,kind=kind,error=repr(exc),tick=len(rows),repair_file=str(repair),time=time.time()) + planning_waits.append(record);dump(out/'PLANNING_WAITS.json',planning_waits);write_checkpoint();status('WAITING_FOR_PLANNING_REPAIR',planning_error=repr(exc),repair_file=str(repair)) + while True: + while True: + if repair.exists(): + raw=repair.read_bytes();digest=hashlib.sha256(raw).hexdigest() + if digest!=seen:break + time.sleep(2.) + seen=digest + try: + import copy + item_job_before=copy.deepcopy(item_job);repair_tick_before=len(rows) + repair_archive=archive_exact(repair_dir/'applied',digest,'.json',raw);record['repair_archive']=repair_archive + patch=json.loads(raw) + if patch.get('abort') is True:raise RuntimeError('Planning repair explicitly aborted: '+str(patch.get('reason'))) + overrides=patch.get('job_overrides',{}) + if not isinstance(overrides,dict):raise ValueError('planning repair job_overrides must be an object') + # A repair module may consume the configured station or target slot while + # performing its screened motion, so apply the hash-archived patch first. + item_job.update(overrides);record['repair_sha256']=digest;record['overrides']=overrides + if patch.get('repair_module'): + module_path=(repair_dir/patch['repair_module']).resolve() + if not module_path.is_relative_to(repair_dir.resolve()):raise ValueError('repair_module must remain inside this case repair directory') + module_raw=module_path.read_bytes();module_sha=hashlib.sha256(module_raw).hexdigest() + if module_sha!=patch.get('repair_module_sha256'):raise ValueError('repair_module_sha256 mismatch') + module_archive=archive_exact(repair_dir/'applied',module_sha,module_path.suffix or '.py',module_raw) + spec=importlib.util.spec_from_file_location(f'planning_repair_{item_index}_{kind}_{module_sha[:12]}',module_path);module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module) + outcome=module.apply(globals(),item_job,dict(record)) + if outcome is not None: + if not isinstance(outcome,dict):raise ValueError('repair module result must be an object or None') + item_job.update(outcome.get('job_overrides',{})) + record['repair_module']=str(module_path);record['repair_module_sha256']=module_sha;record['repair_module_archive']=module_archive;record['repair_module_result']=outcome + dump(out/'PLANNING_WAITS.json',planning_waits);break + except Exception as repair_exc: + traceback.print_exc();motion_rows=max(0,len(rows)-repair_tick_before) + if motion_rows==0:item_job.clear();item_job.update(item_job_before) + failure=dict(item_index=item_index,kind=kind,event='repair_failed',repair_sha256=digest,error=repr(repair_exc),tick=len(rows),time=time.time(),motion_rows_executed=motion_rows,job_overrides_rolled_back=motion_rows==0,measured_prefix_preserved=True) + planning_waits.append(failure);dump(out/'PLANNING_WAITS.json',planning_waits);write_checkpoint();status('WAITING_FOR_PLANNING_REPAIR',planning_error=repr(repair_exc),repair_file=str(repair),failed_repair_sha256=digest) + +def support_gate(item_job,start_tick): + from shapely.geometry import Point,Polygon + path=target_paths[item_index];current=pose(target);window=np.asarray(target_all_rows[-min(60,len(target_all_rows)):])[:,item_index] + drift=float(np.max(np.linalg.norm(window[:,:3]-window[-1,:3],axis=1))) + linear_speed=float(np.linalg.norm(window[-1,:3]-window[0,:3])/max((len(window)-1)/60.,1/60.)) + angular_speed=float(Rot.from_quat(window[0,3:]).inv().__mul__(Rot.from_quat(window[-1,3:])).magnitude()/max((len(window)-1)/60.,1/60.)) + inside=Polygon(item_job['tray_polygon_xy']).buffer(-.005).covers(Point(current[:2])) + hand_clearance=float(np.linalg.norm(current[:3]-pose(hand)[:3]));support_ticks=[t for t in recent_support_contacts[path] if t>=start_tick] + metrics=dict(target=path,tick=len(rows),pose=current.tolist(),inside_tray=inside,drift_last_second_m=drift,linear_speed_m_s=linear_speed,angular_speed_rad_s=angular_speed,hand_clearance_m=hand_clearance,tray_support_contact_ticks=support_ticks[-20:],support_contact_observed=bool(support_ticks)) + if not inside or drift>.012 or linear_speed>.025 or angular_speed>.35 or hand_clearance<.07 or not support_ticks:raise RuntimeError('Physical tray support gate failed: '+json.dumps(metrics)) + return metrics + +def write_checkpoint(): + tmp=out/'checkpoint_60hz.npz.tmp';final=out/'checkpoint_60hz.npz' + with tmp.open('wb') as f:np.savez_compressed(f,q=np.array(measured),native_target=np.array(commands),dof_names=np.array(names),joint_velocity=np.array(joint_velocities),base_action_body=np.array(base_commands),base_pose=np.array(base_rows),nav_constraint_mode=np.array(nav_modes),nav_constraint_pose=np.array(nav_anchors),robot_link_paths=np.array(link_paths),robot_link_pose=np.array(link_rows),rigid_body_paths=np.array(prop_paths),prop_pose=np.array(prop_rows),target_paths=np.array(target_paths),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),item_index=np.array(item_index_rows,dtype=np.int16),active_target_path=np.array(active_target_rows),phase=np.array(phases),tick=np.array(rows)) + tmp.replace(final);dump(out/'CHECKPOINT.json',dict(schema='ziche.multi_checkpoint.v1',completed_objects=completed_objects,item_index=item_index,control_ticks=len(rows),recording=str(final),continuous_process=True,time=time.time())) + +def save(result,error=None): + for p in writers.values(): + p.stdin.close() + if p.wait()!=0:result='FAILED';error='Camera encoder exited unsuccessfully' + contact_stream.close() + ticks=np.asarray(camera_sample_ticks,dtype=np.int64) + np.savez_compressed(out/'camera_extrinsics_20hz.npz',camera_keys=np.asarray(['top_head','hand_left','hand_right']),control_tick=ticks,timestamp=ticks.astype(np.float64)/60.,global_physics_step=4*len(pre_q)+4*ticks,world_T_camera=np.asarray(camera_sample_extrinsics,dtype=np.float64).reshape(-1,3,4,4)) + if rows:write_startup_report() + dump(out/'PHYSICS_CLOCK.json',dict(schema='startup_preroll_clock_v1',physics_dt_s=1/240.,physics_hz=240,control_hz=60,training_hz=20,substeps_per_control=4,unrecorded_preroll_physics_steps=4*len(pre_q),unrecorded_preroll_duration_s=len(pre_q)/60.,retained_control_rows=len(rows),retained_first_control_row=0,retained_timestamp_formula='(row_index+1)/60',retained_first_post_step_global_physics_step=4*len(pre_q)+4 if rows else None,retained_post_step_global_physics_step_formula='unrecorded_preroll_physics_steps + 4*(row_index+1)',retained_camera_row_formula='control_tick % 3 == 0',retained_camera_frame_count=len(camera_sample_ticks),command_semantics='Native continuous multi-item control after stable initial hold; no object or robot pose writes after setup.',contact_semantics='Contact subscription begins after preroll and camera warmup; preroll contacts are excluded.')) + if rows: + np.savez_compressed(out/'recording_60hz.npz',q=np.array(measured),native_target=np.array(commands),dof_names=np.array(names),base_action_body=np.array(base_commands),base_pose=np.array(base_rows),target_pose=np.array(target_rows),target_paths=np.array(target_paths),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),active_target_path=np.array(active_target_rows),item_index=np.array(item_index_rows,dtype=np.int16),phase=np.array(phases),timestamp=np.array(rows)/60.,nav_constraint_mode=np.array(nav_modes),nav_constraint_pose=np.array(nav_anchors),tick=np.array(rows),joint_velocity=np.array(joint_velocities),robot_link_paths=np.array(link_paths),robot_link_pose=np.array(link_rows)) + if rows: + lp=np.asarray(link_rows);pp=np.asarray(prop_rows);uid=f"chain50_layout_{job['layout_id']:02d}" + payload=dict(episode_uid=uid,target_prim=target_paths[0],requested_target_paths=np.array(target_paths),active_target_path=np.array(active_target_rows),item_index=np.array(item_index_rows,dtype=np.int16),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),fps=60.,frame_index=np.arange(len(rows)),robot_dof_names=np.array(names),robot_joint_position_rad_or_m=np.array(measured),robot_joint_velocity_radps_or_mps=np.array(joint_velocities),robot_root_xyz_m=base0[:3,3],robot_root_wxyz=np.r_[bq[3],bq[:3]],robot_link_paths=np.array(link_paths),robot_link_xyz_m=lp[:,:,:3],robot_link_wxyz=lp[:,:,[6,3,4,5]],rigid_body_paths=np.array(prop_paths),prop_xyz_m=pp[:,:,:3],prop_wxyz=pp[:,:,[6,3,4,5]],robot_collision_profile_complete=True,robot_collision_profile_id=profile['profile_id'],robot_collision_enabled_paths=np.array(profile['collision_enabled_paths']),robot_collision_enabled_path_count=len(profile['collision_enabled_paths']),robot_collision_filtered_body_pairs=np.array(profile['filtered_body_pairs'])) + (out/'physics_ticks').mkdir(exist_ok=True);np.savez_compressed(out/'physics_ticks/full_state.npz',**payload);subprocess.run(['cp','--reflink=auto',str(out/'physics_ticks/full_state.npz'),str(out/'full_state.npz')],check=True) + if result=='PHYSICS_COMPLETE_UNVERIFIED' and (not json.loads((out/'PASSIVE_FINGER_PROJECTION.json').read_text())['passed'] or len(camera_sample_ticks)!=len(rows)//3):result='FAILED';error='Stable start or camera sampling gate failed' + if result=='PHYSICS_COMPLETE_UNVERIFIED': + try: + from runtime_collision_readback import CaptureCollisionExporter + from collision_inventory import enabled_nonrobot_convex_colliders + uid=f"chain50_layout_{job['layout_id']:02d}" + for folder,paths,selector in [('collision_readback',profile['collision_enabled_paths'],None),('collision_readback_nonrobot',[str(p.GetPath()) for p in enabled_nonrobot_convex_colliders(stage)],enabled_nonrobot_convex_colliders)]: + reader=CaptureCollisionExporter(stage,out/folder,uid,len(rows)//3-1,len(rows)-1,expected_paths=paths,collider_selector=selector,run_asynchronously=False);reader.start();receipt=reader.finish(len(rows)//3-1,len(rows)-1) + if receipt['status']!='CONTEMPORARY_USD_REPRESENTATIONS_RECORDED':raise RuntimeError('Incomplete capture-end geometry readback: '+folder) + except Exception as exc:result='FAILED';error='Capture-end collision representation: '+repr(exc) + dump(out/'ITEM_EVENTS.json',dict(schema='ziche.multi_item_events.v1',requested_objects=target_paths,completed_objects=completed_objects,events=item_events)) + dump(out/'CONTACT_CAPTURE.json',dict(schema='ziche.contact_capture.v1',requested_targets=target_paths,filter_semantics='robot_or_any_requested_target',all_physics_ticks=True,control_ticks=len(rows),dropped_rows=0,callback_rows=contact_callback_rows,stream='contacts.jsonl.gz')) + dump(out/'place_metrics.json',dict(hands=[item['hand'] for item in items],destination_body_path='/World/Assets/Tray',completed_objects=completed_objects)) + dump(out/'recording_metadata.json',dict(physics_hz=240,control_hz=60,training_hz=20,action_schema='g1_omnipicker_jointpos20_basevw_v1',gripper_max_force={'l':80,'r':80},pose_order='x,y,z,qx,qy,qz,qw',row_clock='post-control-step; same-step applied command',continuous_no_pose_reset=True,multi_item=True,item_count=len(items))) + dump(out/'RESULT.json',dict(status=result,accepted=False,error=error,control_ticks=len(rows),rendered=bool(writers),requested_objects=target_paths,completed_objects=completed_objects,item_count=len(items),requires='independent collision, task, drive, continuity and export validation')) + status(result,error=error) +if len(sys.argv)>2: + try: + replay=np.load(sys.argv[2]);replay_ids=[replay['dof_names'].tolist().index(n) for n in control_names];previous=None + for tick in range(len(replay['q'])): + if 'item_index' in replay: + item_index=int(replay['item_index'][tick]);target=target_objects[item_index];hand=SingleRigidPrim('/World/Robot/gripper_'+items[item_index]['hand']+'_base_link');hand.initialize() + phase=str(replay['phase'][tick]);kind=str(replay['nav_constraint_mode'][tick]);anchor_pose=replay['nav_constraint_pose'][tick];signature=(kind,anchor_pose.tobytes()) + if previous!=signature:constrain(kind,anchor_pose);previous=signature + base_vw=replay['base_action_body'][tick].copy();advance(replay['native_target'][tick,replay_ids]) + save('REPLAY_COMPLETE_UNVERIFIED') + except Exception as e:traceback.print_exc();save('FAILED',repr(e)) + finally:app.close() + sys.exit(0) +try: + from collision_scene import Robot,ROBOT + sys.path.insert(0,str(R/'work/place_training_tray_20260918')) + from carry_transition import plan as plan_carry,Clearance + from multi_motion import plan_return + from return_head_policy import install as install_return_head_policy + install_return_head_policy(globals()) + from placement import solve + from native_planner import build + from place_tray_layout import mesh_world_points + from shapely.geometry import Polygon + from scipy.spatial import ConvexHull + source_layout_id=int(job.get('source_layout_id',job['layout_id'])) + release_spec=json.loads((Path(job['layout']).parents[1]/'audits_release'/f"{source_layout_id:02d}.json").read_text()) + carry_robot=Robot(ROBOT);carry_robot.bodies=[];pose_check=build(stage) + phase='initial_upper_shelf_posture';blend(native[ids],.5) + for i,item in enumerate(items): + item_index=i;data=load_controls(item);target=target_objects[i];hand=SingleRigidPrim('/World/Robot/gripper_'+item['hand']+'_base_link');hand.initialize() + item_job=dict(job);item_job.update(item);item_job['target']=item['target'];item_job['hand']=item['hand'] + item_job['reserved_tray_footprints']=[x['placement_slot']['footprint_xy'] for x in items[i+1:] if x.get('placement_slot')] + target_initial=pose(target).copy();expected_pick_pose=requested_initial_target_pose[i].copy();item_job['expected_pick_pose']=expected_pick_pose.tolist();item_events.append(dict(event='item_started',item_index=i,target=item['target'],tick=len(rows)+1,pose=target_initial.tolist())) + item_phase('waist_to_selected_shelf');blend(data['pick'][0],1.) + item_phase('shelf_pick') + def pick_preflight_call(): + current=pose(target);translation=float(np.linalg.norm(current[:3]-expected_pick_pose[:3]));rotation=float(Rot.from_quat(expected_pick_pose[3:]).inv().__mul__(Rot.from_quat(current[3:])).magnitude()) + metrics=dict(target=item['target'],expected_pose=expected_pick_pose.tolist(),live_pose=current.tolist(),translation_error_m=translation,rotation_error_rad=rotation) + if translation>float(item_job.get('pick_preflight_translation_m',.025)) or rotation>float(item_job.get('pick_preflight_rotation_rad',.35)):raise RuntimeError('Target disturbed before source pickup: '+json.dumps(metrics)) + return metrics + pick_preflight=planning_boundary('pick_preflight',item_job,pick_preflight_call);dump(out/f'PICK_PREFLIGHT.item_{i:02d}.json',pick_preflight);target_initial=pose(target).copy() + for control in data['pick']: + for _ in range(3):advance(control) + def pick_gate_call(): + current=pose(target);lift=float(current[2]-target_initial[2]);contact_evidence=grasp_contact_gate(item['target'],item_job);metrics=dict(target=item['target'],pre_pick_pose=target_initial.tolist(),live_pose=current.tolist(),lift_m=lift,grasp_contact=contact_evidence) + if lift<.025:raise RuntimeError('Pickup did not lift target at least 25 mm: '+json.dumps(metrics)) + return metrics + pick_gate=planning_boundary('pick_recovery',item_job,pick_gate_call);dump(out/f'PICK_GATE.item_{i:02d}.json',pick_gate) + held0=held();grip_idx=18 if item['hand']=='l' else 19 + item_phase('carry_planning');status('RUNNING_PHYSICS') + prop_live=lambda:{p:mat(rb) for p,rb in zip(prop_paths,props)} + def carry_call():return plan_carry(stage,carry_robot,names,np.asarray(robot.get_joint_positions(),dtype=float),mat(base),item['hand'],item['target'],data['target_local'],mat(target),prop_live(),item_job,release_spec) + carry_compact_q,carry_primitives,carry_report=planning_boundary('carry',item_job,carry_call) + dump(out/f'CARRY_PLAN.item_{i:02d}.json',carry_report);dump(out/f'navigation_primitives.item_{i:02d}.json',carry_primitives) + execute_primitives(carry_primitives,held0,return_motion=False,item_job=item_job) + item_phase('bridge_to_place_initial');end=data['place_initial'].copy();end[18:]=native[ids][18:] + def bridge_call(): + start_full=np.asarray(robot.get_joint_positions(),dtype=float).copy();end_full=start_full.copy();end_full[ids]=end + checker=Clearance(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],held(),prop_live());checker.deadline=time.monotonic()+900 + candidates=[[]] + high_end=end_full.copy();high_end[names.index('idx01_body_joint1')]=start_full[names.index('idx01_body_joint1')];candidates.append([high_end]) + for waypoints in candidates: + chain=[start_full]+waypoints+[end_full] + if all(checker.segment(a,b,mat(base),mat(base)) for a,b in zip(chain,chain[1:])):return [q[ids].copy() for q in waypoints]+[end.copy()] + raise RuntimeError('No collision-screened held-object posture bridge: '+str(checker.last)) + bridge_controls=planning_boundary('place_bridge',item_job,bridge_call) + for bridge_target in bridge_controls:blend(bridge_target,6.,held0) + item_phase('place_planning');status('RUNNING_PHYSICS');obstacles=[] + for path in completed_objects: + rb=props[prop_paths.index(path)];prim=stage.GetPrimAtPath(path);world=mesh_world_points(stage,prim) + authored=np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=mat(rb);verts=local@current[:3,:3].T+current[:3,3];obstacles.append(Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2])) + def place_call(): + from release_sweep import ReleaseSweep + item_job['require_release_sweep']=True + release_checker=ReleaseSweep(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],prop_live(),item['place_source'],deadline_s=900) + live_q=np.asarray(robot.get_joint_positions(),dtype=np.float64).copy();live_base=mat(base) + world_check=Clearance(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],held(),prop_live());world_check.deadline=time.monotonic()+900 + return solve(carry_robot,names,live_q,live_base,mat(target),data['target_local'],item_job,obstacles,pose_check,world_check=world_check,release_sweep=release_checker) + planned,retreat,placement_report=planning_boundary('place',item_job,place_call) + dump(out/f'PLACEMENT_PLAN.item_{i:02d}.json',placement_report);np.savez_compressed(out/f'place_planned_targets.item_{i:02d}.npz',q=planned[:,ids],names=control_names) + item_phase('place_descent') + for targetq in planned[:,ids]: + for _ in range(3):advance(targetq) + item_phase('place_release');opened=native[ids].copy();opened[grip_idx]=np.pi/4;blend(opened,2.) + item_phase('release_retreat');retreat_control=retreat[ids].copy();retreat_control[grip_idx]=np.pi/4;blend(retreat_control,3.) + settle_start=len(rows)+1;item_phase('settle');blend(native[ids],2.) + gate=support_gate(item_job,settle_start);completed_objects.append(item['target']);item_events.append(dict(event='item_supported',item_index=i,target=item['target'],tick=len(rows),metrics=gate));write_checkpoint() + base_error=None;yaw_error=None + if i+1PRECISION_RETURN_POLICY['arrival_translation_gate_m'] or rotation>PRECISION_RETURN_POLICY['arrival_yaw_gate_rad']:raise RuntimeError('Return did not reach precision pickup gate: '+json.dumps(metrics)) + return metrics + arrival=planning_boundary('return_arrival',item_job,return_arrival_call);dump(out/f'RETURN_ARRIVAL.item_{i:02d}.json',arrival);base_error=arrival['translation_error_m'];yaw_error=arrival['yaw_error_rad'] + item_phase('complete');blend(native[ids],.25);item_events.append(dict(event='item_completed',item_index=i,target=item['target'],tick=len(rows),support=gate,return_base_error_m=base_error,return_yaw_error_rad=yaw_error)) + write_checkpoint();status('RUNNING_PHYSICS') + save('PHYSICS_COMPLETE_UNVERIFIED') +except Exception as e: + traceback.print_exc();save('FAILED',repr(e)) +finally:app.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/runtime_evidence.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/runtime_evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..5db31f321346fb68eb4f49f4e09c97b4d61b7587 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/runtime_evidence.py @@ -0,0 +1,15 @@ +"""Reconcile snapshot metadata only from matching actual capture readbacks.""" +import json,shutil,time +from pathlib import Path +from pxr import Usd,UsdGeom + +def reconcile(case): + out=case/'rollout';a=json.loads((out/'collision_readback/INPUT.json').read_text());b=json.loads((out/'collision_readback_nonrobot/INPUT.json').read_text()) + for key in ['episode_uid','stage_id','root_layer','request_tick','stage_meters_per_unit','stage_up_axis']: + assert a.get(key)==b.get(key),key + p=out/'scene.usda';s=Usd.Stage.Open(str(p));old=[UsdGeom.GetStageMetersPerUnit(s),str(UsdGeom.GetStageUpAxis(s))];new=[a['stage_meters_per_unit'],a['stage_up_axis']] + if old!=new: + backup=out/'scene.before_runtime_metadata_fix.usda' + if not backup.exists():shutil.copy2(p,backup) + UsdGeom.SetStageMetersPerUnit(s,new[0]);UsdGeom.SetStageUpAxis(s,new[1]);tmp=p.with_suffix('.metadata.tmp');tmp.write_text(s.GetRootLayer().ExportToString());tmp.replace(p) + (out/'SCENE_METADATA_REPAIR.json').write_text(json.dumps(dict(before=old,after=new,source='matching actual robot/nonrobot capture INPUT.json',updated=time.time()),indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/startup_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/startup_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c29f5518d4f29aa76af23d7ddf8920e362086c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/startup_validation.py @@ -0,0 +1,690 @@ +"""Strict startup, no-fall, camera-clock, video, and immutable-gate helpers. + +This module is deliberately independent of the controller. It consumes only +producer evidence and the completed native recording. Missing evidence fails; +there are no layout-specific exceptions. +""" +from __future__ import annotations + +import hashlib +import json +import os +import re +import shutil +import time +from pathlib import Path + +import numpy as np + + +CAMERAS = { + "top_head": (1280, 800), + "hand_left": (848, 480), + "hand_right": (848, 480), +} +CAMERA_LINKS = { + "top_head": "/World/Robot/head_link2", + "hand_left": "/World/Robot/gripper_l_base_link", + "hand_right": "/World/Robot/gripper_r_base_link", +} +PASSIVE_FINGER = re.compile(r"^/World/Robot/gripper_[lr]_(?:inner|outer)_link[234]$") +STRICT = { + "prop_translation_m": 5e-5, + "prop_rotation_rad": float(np.deg2rad(0.02)), + "camera_translation_m": 5e-5, + "camera_rotation_rad": float(np.deg2rad(0.01)), + "driven_robot_translation_m": 5e-5, + "driven_robot_rotation_rad": float(np.deg2rad(0.02)), + "passive_finger_projected_motion_px": 0.25, +} +NO_FALL = { + "max_translation_m": 0.020, + "max_downward_m": 0.010, + "max_rotation_deg": 15.0, + "authored_to_first_translation_m": 0.001, + "authored_to_first_downward_m": 0.001, + "authored_to_first_rotation_deg": 0.5, +} + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + pending = path.with_suffix(path.suffix + ".pending") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def _load_state(out: Path): + path = out / "full_state.npz" + if not path.is_file(): + path = out / "physics_ticks/full_state.npz" + return path, np.load(path, allow_pickle=False) + + +def _normalize_quaternion(q): + q = np.asarray(q, dtype=np.float64) + norm = np.linalg.norm(q, axis=-1, keepdims=True) + if np.any(norm < 1e-15): + raise ValueError("zero quaternion") + return q / norm + + +def angular_from_reference(q, reference): + q = _normalize_quaternion(q) + reference = _normalize_quaternion(np.asarray(reference, dtype=np.float64)) + dots = np.clip(np.abs(np.sum(q * reference, axis=-1)), 0.0, 1.0) + return 2.0 * np.arccos(dots) + + +def pairwise_span(pose): + pose = np.asarray(pose, dtype=np.float64) + if pose.shape[0] < 1 or pose.shape[1] < 1: + return 0.0, 0.0 + xyz = pose[:, :, :3] + q = _normalize_quaternion(pose[:, :, 3:]) + translation = float(np.linalg.norm(xyz[:, None] - xyz[None, :], axis=-1).max()) + dots = np.clip(np.abs(np.einsum("tni,sni->tsn", q, q)), 0.0, 1.0) + rotation = float((2.0 * np.arccos(dots)).max()) + return translation, rotation + + +def clock_audit(out: Path, recording, preroll_npz) -> dict: + out = Path(out) + path = out / "PHYSICS_CLOCK.json" + failures = [] + try: + clock = json.loads(path.read_text()) + except Exception as exc: + return {"schema": "multi_startup_clock_audit_v1", "passed": False, + "failures": ["missing_or_invalid_clock"], "error": repr(exc)} + rows = len(recording["q"]) + preroll_rows = len(preroll_npz["q"]) + expected_steps = 4 * preroll_rows + checks = { + "schema": clock.get("schema") == "startup_preroll_clock_v1", + "rates": clock.get("physics_hz") == 240 and clock.get("control_hz") == 60 + and clock.get("training_hz") == 20 and clock.get("substeps_per_control") == 4, + "unrecorded_preroll_steps": clock.get("unrecorded_preroll_physics_steps") == expected_steps, + "unrecorded_preroll_duration": abs(float(clock.get("unrecorded_preroll_duration_s", -1)) + - expected_steps / 240.0) <= 1e-12, + "retained_rows": clock.get("retained_control_rows") == rows + and clock.get("retained_first_control_row") == 0, + "first_global_step": clock.get("retained_first_post_step_global_physics_step") == expected_steps + 4, + "timestamp_formula": clock.get("retained_timestamp_formula") == "(row_index+1)/60", + "global_step_formula": clock.get("retained_post_step_global_physics_step_formula") + == "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "camera_formula": clock.get("retained_camera_row_formula") == "control_tick % 3 == 0" + and clock.get("retained_camera_frame_count") == rows // 3, + "command_semantics": bool(clock.get("command_semantics")), + "contact_semantics": bool(clock.get("contact_semantics")), + "recording_tick": np.array_equal(recording["tick"], np.arange(1, rows + 1)), + "recording_timestamp": np.allclose(recording["timestamp"], np.arange(1, rows + 1) / 60.0, + atol=1e-12, rtol=0), + } + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_startup_clock_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "clock": clock, + "clock_sha256": sha256(path), "retained_rows": rows, + "preroll_rows_60hz": preroll_rows, + } + + +def _pose_matrix_xyzw(pose): + from scipy.spatial.transform import Rotation as Rot + pose = np.asarray(pose, dtype=np.float64) + matrices = np.zeros(pose.shape[:-1] + (4, 4), dtype=np.float64) + matrices[..., 3, 3] = 1.0 + matrices[..., :3, :3] = Rot.from_quat(pose[..., 3:]).as_matrix() + matrices[..., :3, 3] = pose[..., :3] + return matrices + + +def camera_extrinsics_audit(out: Path, recording, clock: dict) -> dict: + out = Path(out) + path = out / "camera_extrinsics_20hz.npz" + failures = [] + try: + camera = np.load(path, allow_pickle=False) + required = {"camera_keys", "control_tick", "timestamp", "global_physics_step", "world_T_camera"} + missing = sorted(required - set(camera.files)) + if missing: + raise ValueError(f"missing keys {missing}") + keys = camera["camera_keys"].tolist() + ticks = np.asarray(camera["control_tick"], dtype=np.int64) + timestamp = np.asarray(camera["timestamp"], dtype=np.float64) + global_steps = np.asarray(camera["global_physics_step"], dtype=np.int64) + transforms = np.asarray(camera["world_T_camera"], dtype=np.float64) + except Exception as exc: + return {"schema": "multi_camera_extrinsics_audit_v1", "passed": False, + "failures": ["missing_or_invalid_camera_extrinsics"], "error": repr(exc)} + rows = len(recording["q"]) + expected_ticks = np.arange(3, rows + 1, 3, dtype=np.int64) + preroll_steps = int(clock.get("unrecorded_preroll_physics_steps", -1)) + checks = { + "camera_order": keys == list(CAMERAS), + "shape": transforms.shape == (len(expected_ticks), 3, 4, 4), + "control_tick_mapping": np.array_equal(ticks, expected_ticks), + "timestamp_mapping": np.allclose(timestamp, expected_ticks / 60.0, atol=1e-12, rtol=0), + "global_step_mapping": np.array_equal(global_steps, preroll_steps + 4 * expected_ticks), + "finite": bool(np.isfinite(transforms).all()), + "homogeneous_row": bool(np.allclose(transforms[..., 3, :], [0, 0, 0, 1], atol=1e-10)), + } + if checks["shape"] and checks["finite"]: + rotation = transforms[..., :3, :3] + identity_error = float(np.max(np.abs(rotation.swapaxes(-1, -2) @ rotation - np.eye(3)))) + determinant_error = float(np.max(np.abs(np.linalg.det(rotation) - 1.0))) + else: + identity_error = determinant_error = float("inf") + checks["rigid_rotation"] = identity_error <= 1e-5 and determinant_error <= 1e-5 + + # Prove every camera stays rigidly mounted to the recorded robot link at the + # exact control row associated with that encoded frame. The authored mount + # can differ by camera, but it must be constant throughout the episode. + mount_errors = {} + checks["rigid_mount_alignment"] = False + if checks["shape"] and checks["control_tick_mapping"]: + paths = recording["robot_link_paths"].tolist() + try: + link_index = [paths.index(CAMERA_LINKS[key]) for key in keys] + link_pose = np.asarray(recording["robot_link_pose"], dtype=np.float64)[ticks - 1][:, link_index] + world_T_link = _pose_matrix_xyzw(link_pose) + relative = np.linalg.inv(world_T_link) @ transforms + for index, key in enumerate(keys): + reference = relative[0, index] + translation = float(np.linalg.norm(relative[:, index, :3, 3] - reference[:3, 3], axis=1).max()) + from scipy.spatial.transform import Rotation as Rot + delta = Rot.from_matrix(reference[:3, :3]).inv() * Rot.from_matrix(relative[:, index, :3, :3]) + rotation = float(delta.magnitude().max()) + mount_errors[key] = {"translation_m": translation, "rotation_rad": rotation} + checks["rigid_mount_alignment"] = all( + row["translation_m"] <= 1e-5 and row["rotation_rad"] <= 1e-5 + for row in mount_errors.values()) + except Exception as exc: + mount_errors["error"] = repr(exc) + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_camera_extrinsics_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "frames": len(expected_ticks), + "rotation_orthogonality_max_error": identity_error, + "rotation_determinant_max_error": determinant_error, + "mount_errors": mount_errors, "camera_extrinsics_sha256": sha256(path), + } + + +def camera_warmup_audit(out: Path, preroll_evidence: dict, result: dict) -> dict: + """Require native three-view capture to start only after stable priming.""" + out = Path(out) + path = out / "CAMERA_WARMUP.json" + failures = [] + try: + report = json.loads(path.read_text()) + final = report.get("final_stability_window", {}) + startup = out / "startup_frames" + names = sorted(p.name for p in startup.glob("*.jpg")) + expected = sorted(f"{camera}_{index:02d}.jpg" for camera in CAMERAS for index in range(10)) + checks = { + "native_rendered": result.get("rendered") is True, + "status": report.get("status") == "PASSED", + "after_stability": report.get("after_physics_stability") is True, + "render_warmups": int(report.get("render_only_warmups", -1)) >= 12, + "discarded_reads": int(report.get("discarded_render_reads", -1)) >= 3, + "unrecorded_hold_samples": int(report.get("unrecorded_hold_samples", -1)) >= 3, + "preroll_hold_accounting": int(preroll_evidence.get("camera_warmup_physics_samples", -1)) + == int(report.get("unrecorded_hold_samples", -2)), + "no_captured_warmup_frames": int(report.get("captured_frames", -1)) == 0, + "final_prop_stable": float(final.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera_stable": float(final.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven_stable": float(final.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "startup_frame_set": names == expected, + "startup_frames_nonempty": names == expected and all((startup / name).stat().st_size > 0 for name in names), + } + failures.extend(name for name, passed in checks.items() if not passed) + except Exception as exc: + return {"schema": "multi_camera_warmup_audit_v1", "passed": False, + "checks": {}, "failures": ["missing_or_invalid_camera_warmup"], "error": repr(exc)} + return {"schema": "multi_camera_warmup_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "receipt": report, + "receipt_sha256": sha256(path), + "startup_frame_sha256": {name: sha256(out / "startup_frames" / name) for name in names}} + + +def _projection_audit(out: Path, pre_path: Path, state_path: Path) -> dict: + path = out / "PASSIVE_FINGER_PROJECTION.json" + try: + report = json.loads(path.read_text()) + windows = {row.get("label"): row for row in report.get("windows", [])} + required = {"final_preroll_0p5s", "first_retained_30_control_rows"} + implementation = Path(report["projection_implementation"]) + checks = { + "status": report.get("status") == "PASSED" and report.get("passed") is True, + "threshold": report.get("threshold_px") == STRICT["passive_finger_projected_motion_px"], + "scope": report.get("only_passive_distal_fingers") is True, + "windows": set(windows) == required and all( + row.get("passed") is True + and row.get("passive_projection", {}).get("status") == "PASSED" + and 0 <= float(row.get("passive_projection", {}).get("max_projected_motion_px", 1)) < 0.25 + for row in windows.values()), + "bodies": bool(report.get("evaluated_passive_paths")) and bool(report.get("evaluated_driven_paths")), + "cameras": len(report.get("evaluated_cameras", [])) == 3, + "implementation_hash": implementation.is_file() + and report.get("projection_implementation_sha256") == sha256(implementation), + } + failures = [name for name, passed in checks.items() if not passed] + return {"passed": not failures, "checks": checks, "failures": failures, + "receipt": report, "receipt_sha256": sha256(path), "windows": windows, + "independently_bound_pose_sha256": { + pre_path.name: sha256(pre_path), state_path.name: sha256(state_path)}} + except Exception as exc: + return {"passed": False, "checks": {}, "failures": ["missing_or_invalid_projection"], + "error": repr(exc), "windows": {}} + + +def startup_stability_audit(case: Path, recording, action_names) -> dict: + case = Path(case) + out = case / "rollout" + pre_json_path = out / "PREROLL_EVIDENCE.json" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + failures = [] + try: + evidence = json.loads(pre_json_path.read_text()) + pre = np.load(pre_path, allow_pickle=False) + except Exception as exc: + return {"schema": "multi_startup_stability_audit_v1", "passed": False, + "failures": ["missing_preroll_evidence"], "error": repr(exc)} + projection = _projection_audit(out, pre_path, state_path) + rows = min(30, len(recording["q"])) + paths = recording["robot_link_paths"].tolist() + try: + camera_indices = [paths.index(path) for path in CAMERA_LINKS.values()] + driven_indices = [index for index, path in enumerate(paths) if not PASSIVE_FINGER.match(path)] + retained_links = np.asarray(recording["robot_link_pose"][:rows], dtype=np.float64) + retained_props = np.concatenate([ + np.asarray(state["prop_xyz_m"][:rows], dtype=np.float64), + np.asarray(state["prop_wxyz"][:rows, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + prop_translation, prop_rotation = pairwise_span(retained_props) + camera_translation, camera_rotation = pairwise_span(retained_links[:, camera_indices]) + driven_translation, driven_rotation = pairwise_span(retained_links[:, driven_indices]) + except Exception as exc: + prop_translation = prop_rotation = camera_translation = camera_rotation = float("inf") + driven_translation = driven_rotation = float("inf") + failures.append("retained_pose_metrics") + metric_error = repr(exc) + else: + metric_error = None + first_projection = projection.get("windows", {}).get("first_retained_30_control_rows", {}) + retained_checks = { + "rows": rows == 30, + "prop_translation": prop_translation < STRICT["prop_translation_m"], + "prop_rotation": prop_rotation < STRICT["prop_rotation_rad"], + "camera_translation": camera_translation < STRICT["camera_translation_m"], + "camera_rotation": camera_rotation < STRICT["camera_rotation_rad"], + "driven_translation": driven_translation < STRICT["driven_robot_translation_m"], + "driven_rotation": driven_rotation < STRICT["driven_robot_rotation_rad"], + "passive_projection": first_projection.get("passed") is True, + } + failures.extend("first_retained_" + name for name, passed in retained_checks.items() if not passed) + thresholds = evidence.get("thresholds", {}) + final_window = evidence.get("final_window", {}) + names = recording["dof_names"].tolist() + try: + command_indices = [names.index(name) for name in action_names[:20]] + exact_initial = np.array_equal(np.asarray(pre["initial_command"], dtype=np.float64), + np.asarray(recording["native_target"][0, command_indices], dtype=np.float64)) + retained_initial_hold = (rows == 30 + and np.array_equal(np.asarray(recording["native_target"][:rows, command_indices], dtype=np.float64), + np.repeat(np.asarray(pre["initial_command"], dtype=np.float64)[None], rows, axis=0)) + and np.array_equal(np.asarray(recording["base_action_body"][:rows], dtype=np.float64), + np.zeros((rows, 2), dtype=np.float64)) + and set(np.asarray(recording["phase"][:rows]).astype(str).tolist()) == {"initial_upper_shelf_posture"} + and set(np.asarray(recording["item_index"][:rows], dtype=np.int64).tolist()).issubset({-1, 0})) + except Exception: + exact_initial = False + retained_initial_hold = False + preroll_checks = { + "status": evidence.get("status") == "PASSED", + "rates": evidence.get("physics_hz") == 240 and evidence.get("sample_hz") == 60, + "unrecorded": evidence.get("recorded_rows") == 0 + and evidence.get("contact_capture_started") is False + and evidence.get("camera_capture_started") is False, + "no_pose_writes": evidence.get("no_pose_writes_or_resets") is True, + "duration": float(evidence.get("duration_s", 0)) >= 0.5, + "thresholds": thresholds.get("prop_translation_m") == STRICT["prop_translation_m"] + and thresholds.get("prop_rotation_rad") == STRICT["prop_rotation_rad"] + and thresholds.get("camera_translation_m") == STRICT["camera_translation_m"] + and thresholds.get("camera_rotation_rad") == STRICT["camera_rotation_rad"] + and thresholds.get("passive_finger_projected_motion_px") == 0.25, + "final_prop": float(final_window.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final_window.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera": float(final_window.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final_window.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven": float(final_window.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final_window.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "exact_initial_command": exact_initial, + "first_retained_30_exact_initial_hold": retained_initial_hold, + "projection": projection["passed"], + } + failures.extend("preroll_" + name for name, passed in preroll_checks.items() if not passed) + return { + "schema": "multi_startup_stability_audit_v1", "passed": not failures, + "failures": failures, "strict_thresholds": STRICT, + "retained_first_30_control_rows": { + "checks": retained_checks, "prop_translation_span_m": prop_translation, + "prop_rotation_span_rad": prop_rotation, + "camera_translation_span_m": camera_translation, + "camera_rotation_span_rad": camera_rotation, + "driven_translation_span_m": driven_translation, + "driven_rotation_span_rad": driven_rotation, + "metric_error": metric_error, + }, + "preroll_checks": preroll_checks, "preroll_evidence": evidence, + "projection_audit": projection, + "evidence_sha256": {"PREROLL_EVIDENCE.json": sha256(pre_json_path), + "PREROLL_EVIDENCE.npz": sha256(pre_path), + state_path.name: sha256(state_path)}, + } + + +def prop_no_fall_audit(case: Path, recording, requested: list[dict]) -> dict: + """Audit every prop from the authored pose over its physically relevant scopes. + + Non-target props cover the entire preroll and episode. Requested targets + cover preroll + all rows before their own shelf_pick, and then the whole + interval from their first settle row through episode end. The manipulation + interval is intentionally excluded; placement gates cover it separately. + """ + case = Path(case) + out = case / "rollout" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + try: + pre = np.load(pre_path, allow_pickle=False) + pre_paths = pre["prop_paths"].tolist() + initial = np.asarray(pre["initial_prop_pose_before_preroll"], dtype=np.float64) + pre_pose = np.asarray(pre["prop_pose"], dtype=np.float64) + full_paths = state["rigid_body_paths"].tolist() + if len(set(pre_paths)) != len(pre_paths) or set(pre_paths) != set(full_paths): + raise ValueError("preroll/full-state prop inventory mismatch") + order = [full_paths.index(path) for path in pre_paths] + retained = np.concatenate([ + np.asarray(state["prop_xyz_m"][:, order], dtype=np.float64), + np.asarray(state["prop_wxyz"][:, order][:, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + except Exception as exc: + return {"schema": "multi_prop_no_fall_audit_v1", "passed": False, + "failures": ["missing_or_invalid_prop_evidence"], "error": repr(exc)} + producer_path = out / "INITIAL_PROP_NO_FALL.json" + try: + producer = json.loads(producer_path.read_text()) + maxima = producer.get("maxima", {}) + producer_checks = { + "passed": producer.get("passed") is True, + "all_props": producer.get("all_requested_and_nonrequested_props_included") is True, + "all_preroll_samples": int(producer.get("physics_samples", -1)) == len(pre_pose), + "translation": float(maxima.get("translation_m", 1)) <= NO_FALL["max_translation_m"], + "downward": float(maxima.get("downward_m", 1)) <= NO_FALL["max_downward_m"], + "rotation": float(maxima.get("rotation_rad", 1)) <= np.deg2rad(NO_FALL["max_rotation_deg"]), + } + except FileNotFoundError: + producer = {"present": False, "semantics": "optional producer fail-fast; independent full trace remains mandatory"} + producer_checks = {"optional_receipt_absent": True} + except Exception as exc: + producer = {"present": True, "error": repr(exc)} + producer_checks = {"receipt_valid_if_present": False} + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + try: + events = json.loads((out / "ITEM_EVENTS.json").read_text()).get("events", []) + except Exception: + events = [] + target_to_index = {row["target"]: index for index, row in enumerate(requested)} + failures = [] + rows = [] + + def segment_metrics(trace, reference): + trace = np.asarray(trace, dtype=np.float64) + if len(trace) == 0: + return {"rows": 0, "max_translation_m": float("inf"), + "max_downward_m": float("inf"), "max_rotation_deg": float("inf"), "passed": False} + translation = np.linalg.norm(trace[:, :3] - reference[:3], axis=1) + rotation = np.degrees(angular_from_reference(trace[:, 3:], reference[3:])) + metrics = { + "rows": len(trace), "max_translation_m": float(translation.max()), + "max_downward_m": float(max(0.0, reference[2] - trace[:, 2].min())), + "max_rotation_deg": float(rotation.max()), + } + metrics["passed"] = (metrics["max_translation_m"] <= NO_FALL["max_translation_m"] + and metrics["max_downward_m"] <= NO_FALL["max_downward_m"] + and metrics["max_rotation_deg"] <= NO_FALL["max_rotation_deg"]) + return metrics + + for prop_index, path in enumerate(pre_paths): + first = pre_pose[0, prop_index] + authored = initial[prop_index] + supplemental = { + "authored_to_first_translation_m": float(np.linalg.norm(first[:3] - authored[:3])), + "authored_to_first_downward_m": float(max(0.0, authored[2] - first[2])), + "authored_to_first_rotation_deg": float(np.degrees(angular_from_reference(first[None, 3:], authored[3:]))[0]), + } + supplemental_checks = {name: supplemental[name] <= NO_FALL[name] for name in supplemental} + segments = [] + if path not in target_to_index: + trace = np.concatenate([pre_pose[:, prop_index], retained[:, prop_index]], axis=0) + segments.append({"scope": "full_preroll_and_capture", **segment_metrics(trace, authored)}) + else: + index = target_to_index[path] + pick = np.flatnonzero((item_index == index) & (phases == f"item_{index:02d}_shelf_pick")) + completed = [row for row in events if row.get("event") == "item_completed" + and row.get("item_index") == index and row.get("target") == path] + if len(pick): + pre_pick = np.concatenate([pre_pose[:, prop_index], retained[:int(pick[0]), prop_index]], axis=0) + segments.append({"scope": "preroll_and_rows_before_own_pick", **segment_metrics(pre_pick, authored)}) + else: + segments.append({"scope": "preroll_and_rows_before_own_pick", "rows": 0, "passed": False, + "reason": "missing own shelf_pick"}) + if len(completed) == 1 and 1 <= int(completed[0].get("tick", 0)) <= len(retained): + start = int(completed[0]["tick"]) - 1 + post = retained[start:, prop_index] + segments.append({"scope": "item_completed_through_episode_end", + "source_control_tick_start": start + 1, + **segment_metrics(post, post[0])}) + else: + segments.append({"scope": "item_completed_through_episode_end", "rows": 0, "passed": False, + "reason": "missing or ambiguous item_completed event"}) + passed = all(supplemental_checks.values()) and all(row.get("passed") is True for row in segments) + if not passed: + failures.append(path) + rows.append({"path": path, "requested_item_index": target_to_index.get(path), + "authored_to_first_preroll": supplemental, + "authored_to_first_checks": supplemental_checks, + "segments": segments, "passed": passed}) + producer_passed = all(producer_checks.values()) + if not producer_passed: + failures.append("producer_initial_prop_no_fall_receipt") + return { + "schema": "multi_prop_no_fall_audit_v1", "passed": not failures, + "thresholds": NO_FALL, "quaternions_normalized_float64": True, + "reference": "authored/reset pose captured immediately before the first unrecorded preroll physics step", + "non_target_scope": "every preroll sample and every retained control row through episode end", + "requested_target_scope": "preroll and retained rows before its own pick, plus item_completed through episode end", + "manipulation_scope": "excluded here and independently covered by lift/grasp/place/support/collision gates", + "paths": rows, "failures": failures, "producer_initial_prop_no_fall": producer, + "producer_initial_prop_no_fall_checks": producer_checks, + "evidence_sha256": {**{"PREROLL_EVIDENCE.npz": sha256(pre_path), state_path.name: sha256(state_path)}, + **({"INITIAL_PROP_NO_FALL.json": sha256(producer_path)} if producer_path.is_file() else {})}, + } + + +def video_decode_audit(out: Path, expected_frames: int) -> dict: + """Perform the one authoritative sequential decode of all three videos.""" + import av + out = Path(out) + videos = {} + all_passed = True + for camera, (width, height) in CAMERAS.items(): + path = out / f"{camera}.mp4" + total = np.zeros(3, dtype=np.float64) + total2 = np.zeros(3, dtype=np.float64) + low = np.ones(3, dtype=np.float64) + high = np.zeros(3, dtype=np.float64) + sample_count = 0 + frame_count = 0 + first_gray = [] + near_black = 0 + nonfinite = 0 + previous_digest = None + exact_run = longest_exact_run = 0 + with av.open(str(path)) as container: + stream = container.streams.video[0] + fps = float(stream.average_rate) + actual_width, actual_height = stream.width, stream.height + for frame in container.decode(video=0): + rgb = frame.to_ndarray(format="rgb24") + gray = rgb.astype(np.float32).mean(axis=2) / 255.0 + if frame_count < 4: + first_gray.append(gray) + fraction = float((gray < (4.0 / 255.0)).mean()) + near_black += int(fraction > 0.98) + nonfinite += int(not np.isfinite(gray).all()) + digest = hashlib.sha256(rgb.tobytes()).digest() + exact_run = exact_run + 1 if digest == previous_digest else 1 + longest_exact_run = max(longest_exact_run, exact_run) + previous_digest = digest + if frame_count % max(1, expected_frames // 100) == 0: + sampled = rgb[::16, ::16].reshape(-1, 3) / 255.0 + total += sampled.sum(axis=0) + total2 += (sampled * sampled).sum(axis=0) + low = np.minimum(low, sampled.min(axis=0)) + high = np.maximum(high, sampled.max(axis=0)) + sample_count += len(sampled) + frame_count += 1 + adjacent = [float(np.mean(np.abs(first_gray[i + 1] - first_gray[i]))) + for i in range(max(0, len(first_gray) - 1))] + checks = { + "frame_count": frame_count == expected_frames, + "fps": fps == 20, + "dimensions": (actual_width, actual_height) == (width, height), + "first_frame_synchronized": len(adjacent) == 3 and adjacent[0] < 0.05, + "no_near_black_frames": near_black == 0, + "finite_metrics": nonfinite == 0, + "no_long_exact_frame_run": longest_exact_run < 10, + } + passed = all(checks.values()) + all_passed &= passed + mean = total / max(sample_count, 1) + videos[camera] = { + "passed": passed, "path": str(path), "sha256": sha256(path), + "frames": frame_count, "fps": fps, "width": actual_width, "height": actual_height, + "checks": checks, "first_three_adjacent_mean_abs_gray_differences": adjacent, + "near_black_gt_0_98_frames": near_black, "nonfinite_metric_count": nonfinite, + "longest_exact_frame_run": longest_exact_run, + "image_stats": { + "min": low.reshape(3, 1, 1).tolist(), "max": high.reshape(3, 1, 1).tolist(), + "mean": mean.reshape(3, 1, 1).tolist(), + "std": np.sqrt(np.maximum(total2 / max(sample_count, 1) - mean * mean, 0)).reshape(3, 1, 1).tolist(), + "count": [frame_count], + }, + } + report = { + "schema": "multi_video_decode_audit_v1", "passed": all_passed, + "method": "one authoritative sequential PyAV decode of every frame in all three views", + "expected_frames": expected_frames, "videos": videos, + } + write_json(out / "VIDEO_DECODE_AUDIT.json", report) + return report + + +FROZEN_AUDITS = [ + "TASK_SCHEMA_AUDIT.json", "CONTACT_CAPTURE_AUDIT.json", "GRASP_RETENTION_AUDIT.json", + "GRASP_STABILITY_AUDIT.json", "NON_TARGET_PROP_STABILITY_AUDIT.json", + "PHYSICS_CLOCK_AUDIT.json", "STARTUP_STABILITY_AUDIT.json", "CAMERA_WARMUP_AUDIT.json", + "CAMERA_EXTRINSICS_AUDIT.json", + "VIDEO_DECODE_AUDIT.json", + "SELF_COLLISION_AUDIT.json", "WORLD_COLLISION_AUDIT.json", "WRIST_AUDIT.json", + "VISIBILITY_AUDIT.json", +] +RAW_EVIDENCE = [ + "recording_60hz.npz", "full_state.npz", "PHYSICS_CLOCK.json", "PREROLL_EVIDENCE.json", + "PREROLL_EVIDENCE.npz", "PASSIVE_FINGER_PROJECTION.json", "CAMERA_WARMUP.json", + "camera_extrinsics_20hz.npz", "RESULT.json", "ITEM_EVENTS.json", "CHECKPOINT.json", + "CONTACT_CAPTURE.json", "recording_metadata.json", "LIVE_DRIVE_READBACK.json", + "scene.usda", "contacts.jsonl.gz", "top_head.mp4", "hand_left.mp4", "hand_right.mp4", +] +OPTIONAL_RAW_EVIDENCE = ["INITIAL_PROP_NO_FALL.json"] + + +def freeze_physical_gate(case: Path, world_audit_shards: int | None = None) -> dict: + case = Path(case).resolve() + out = case / "rollout" + destination = out / "physical_evidence" + if destination.exists(): + raise FileExistsError(destination) + pending = out / f".physical_evidence.pending.{os.getpid()}" + pending.mkdir() + try: + frozen = {} + for name in FROZEN_AUDITS: + source = out / name + target = pending / name + shutil.copy2(source, target) + frozen[name] = sha256(target) + os.replace(pending, destination) + except Exception: + shutil.rmtree(pending, ignore_errors=True) + raise + raw_names = RAW_EVIDENCE + [name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()] + raw = {name: sha256(out / name) for name in raw_names} + report = { + "schema": "multi_startup_physical_gate_v1", "passed": True, + "case": str(case), "visual_acceptance_pending": True, + "world_audit_shards": world_audit_shards, + "frozen_audit_dir": str(destination), "frozen_audit_sha256": frozen, + "raw_evidence_sha256": raw, + "semantics": "Physical and startup acceptance is frozen before staged export. Visual/manual and official-reader acceptance remain separate required gates.", + "completed_unix": time.time(), + } + write_json(out / "PHYSICAL_GATE_RESULT.json", report) + return report + + +def verify_physical_gate(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + failures = [] + try: + receipt = json.loads((out / "PHYSICAL_GATE_RESULT.json").read_text()) + frozen_dir = Path(receipt["frozen_audit_dir"]).resolve() + if frozen_dir != (out / "physical_evidence").resolve(): + failures.append("frozen_directory") + for name, digest in receipt.get("frozen_audit_sha256", {}).items(): + path = frozen_dir / name + if not path.is_file() or sha256(path) != digest: + failures.append("frozen:" + name) + for name, digest in receipt.get("raw_evidence_sha256", {}).items(): + path = out / name + if not path.is_file() or sha256(path) != digest: + failures.append("raw:" + name) + if set(receipt.get("frozen_audit_sha256", {})) != set(FROZEN_AUDITS): + failures.append("frozen_scope") + expected_raw = set(RAW_EVIDENCE) | {name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()} + if set(receipt.get("raw_evidence_sha256", {})) != expected_raw: + failures.append("raw_scope") + except Exception as exc: + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": False, + "failures": ["missing_or_invalid_receipt"], "error": repr(exc)} + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": not failures, + "failures": failures, "receipt_sha256": sha256(out / "PHYSICAL_GATE_RESULT.json")} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/supervise.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/supervise.py new file mode 100644 index 0000000000000000000000000000000000000000..3a3cdaef555b58edcd81e007f1cef7324fe126cc --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/supervise.py @@ -0,0 +1,75 @@ +"""Own new multi-item cases only; preserve every failed or paused native prefix.""" +import os,sys,time,json,fcntl,subprocess +from pathlib import Path +W=Path(__file__).resolve().parent +CPU='/mnt/agent-bench-data/lingjun/envs/mesh311/bin/python' +lock=(W/'supervisor.lock').open('a+');fcntl.flock(lock,fcntl.LOCK_EX|fcntl.LOCK_NB) +children={};reviews={};logs=[] +def read(p,default=None): + try:return json.loads(p.read_text()) + except (FileNotFoundError,json.JSONDecodeError):return default + +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) + +def start(cmd,log,env=None): + f=log.open('a');logs.append(f) + return subprocess.Popen(cmd,stdout=f,stderr=subprocess.STDOUT,env=env) + +def validation_files_writable(case): + """Do not race the launcher's final recursive chown. + + RESULT is written by the container before launch.py exits. It is not a handoff: + launch.py still owns the case until its finally block has completed. Once that + owner has exited, prove the supervisor account can update STATUS and create an + audit file in rollout before changing status or spawning validate_case. + """ + out=case/'rollout';required=[out/'RESULT.json',out/'recording_60hz.npz',out/'full_state.npz', + out/'PHYSICS_CLOCK.json',out/'top_head.mp4',out/'hand_left.mp4',out/'hand_right.mp4'] + if not all(p.is_file() and os.access(p,os.R_OK) for p in required):return False + if not os.access(case/'STATUS.json',os.W_OK) or not os.access(out,os.W_OK):return False + probe=out/f'.validation_write_probe.{os.getpid()}' + try: + fd=os.open(probe,os.O_WRONLY|os.O_CREAT|os.O_EXCL,0o600);os.close(fd);probe.unlink();return True + except OSError: + try:probe.unlink(missing_ok=True) + except OSError:pass + return False + +while True: + control=read(W/'CONTROL.json',{'enabled':False,'allowed_layouts':[],'gpus':[0]}) + for gpu,(case,p) in list(children.items()): + if p.poll() is None:continue + s=read(case/'STATUS.json',{}) + if s.get('status') not in ['FAILED','PHYSICS_COMPLETE_UNVERIFIED','REVIEW_REQUIRED','ACCEPTED']: + write(case/'STATUS.json',dict(status='REVIEW_REQUIRED',phase='launcher_exit',exit_code=p.returncode,updated=time.time())) + del children[gpu] + for name,(case,p) in list(reviews.items()): + if p.poll() is None:continue + if p.returncode and read(case/'STATUS.json',{}).get('status')!='REVIEW_REQUIRED': + write(case/'STATUS.json',dict(status='REVIEW_REQUIRED',phase='validation_exit',exit_code=p.returncode,updated=time.time())) + del reviews[name] + cases=sorted((W/'cases').glob('layout_*')) + if control.get('enabled'): + for gpu in control.get('gpus',[0]): + if gpu in children:continue + candidates=[c for c in cases if read(c/'STATUS.json',{}).get('status')=='PREPARED' and read(c/'job.json',{}).get('layout_id') in control.get('allowed_layouts',[]) and not (c/'rollout').exists()] + if not candidates:break + case=candidates[0];layout=read(case/'job.json')['layout_id'] + write(case/'STATUS.json',dict(status='LAUNCHING',phase='gpu_reservation',gpu=gpu,updated=time.time())) + p=start([sys.executable,str(W/'launch.py'),str(layout),str(gpu)],case/'launcher.log');children[gpu]=(case,p) + if (W/'VALIDATION_READY').exists(): + native_owned={c.resolve() for c,p in children.values() if p.poll() is None} + for case in cases: + if len(reviews)>=1:break + if (read(case/'STATUS.json',{}).get('status')!='PHYSICS_COMPLETE_UNVERIFIED' + or case.name in reviews or case.resolve() in native_owned + or not validation_files_writable(case)):continue + write(case/'STATUS.json',dict(status='QUEUED_VALIDATION',phase='validation_start',updated=time.time())) + env=dict(os.environ,CHAIN_WORLD_AUDIT_SHARDS='8',CHAIN_PARALLEL_COLLISION_AUDITS='1',OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + p=start([CPU,str(W/'validate_case.py'),str(case)],case/'validation.log',env);reviews[case.name]=(case,p) + states=[{**read(c/'STATUS.json',{}), 'layout_id':read(c/'job.json',{}).get('layout_id'), 'case':str(c)} for c in cases] + accepted=sum(s.get('status')=='ACCEPTED' for s in states) + write(W/'SUPERVISOR.json',dict(pid=os.getpid(),updated=time.time(),requested=10,accepted=accepted,native=[dict(gpu=g,case=str(c),pid=p.pid) for g,(c,p) in children.items()],reviews=[dict(case=str(c),pid=p.pid) for c,p in reviews.values()],cases=states)) + if accepted==10 and not children and not reviews:break + time.sleep(5) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/task_collision_scope.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/task_collision_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..5e29f2540b8f62498affa56eb14fa3e2fa003494 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/task_collision_scope.py @@ -0,0 +1,32 @@ +"""Robot and every requested target stay in scope for the full multi-item run.""" +import copy +POLICY=dict(version='robot_and_all_requested_targets_vs_environment_v2',included=['robot_vs_environment','robot_vs_any_requested_target','every_requested_target_vs_environment','requested_target_vs_requested_target'],excluded=['environment_vs_environment','unrelated_stock_support'],separate_required=['robot_self_collision_all_ticks','per_item_physical_pick_place_success','final_multi_item_nonoverlap'],authority='Multi-item task retains the task-scoped environment omission while keeping the robot and every requested target in scope at every tick.') + +def in_scope(pair,targets): + targets=tuple(targets) + def task_body(path): + if path.startswith(('/G01/','/World/Robot/')):return True + return any(path==target or path.startswith(target+'/') or path.startswith(target+'#') for target in targets) + return any(task_body(str(pair.get(k,''))) for k in ('a','b','a_owner','b_owner')) + +def summarize_scoped(original,report,tolerance,targets,nonphysical_paths=()): + scoped=copy.copy(report);pairs=report.get('pairs',[]);scoped['pairs']=[p for p in pairs if in_scope(p,targets) and not any(str(p.get(k,'')).split('#')[0] in nonphysical_paths for k in ('a','b'))];scoped['unrelated_stock_support']={} + # Keep all_scene_pairs_checked truthful. Remove only the deliberate scope omission. + result=original(scoped,tolerance) + result['unresolved_review']=[x for x in result['unresolved_review'] if x['reason']!='unrelated scene pairs were not checked'] + result['geometry_status']='REJECT' if result['hard_failures'] else 'REVIEW' if result['unresolved_review'] else 'CLEAR_AT_CHECKED_SAMPLES' + result['omitted_nonphysical_decorative_paths']=list(nonphysical_paths);result['collision_scope']=POLICY;result['excluded_environment_pair_count']=len(pairs)-len(scoped['pairs']);result['all_scene_pairs_checked']=bool(report.get('all_scene_pairs_checked')) + result['requested_targets']=list(targets) + result['limitations'].append('Clearance applies to the robot and every requested target, not environment assembly or unrelated object pairs.') + return result + +def install(acceptance,targets,nonphysical_paths=()): + original=acceptance.summarize + acceptance.summarize=lambda report,contact_tolerance_mm=None:summarize_scoped(original,report,contact_tolerance_mm,targets,nonphysical_paths) + + +def nonphysical_rug_paths(stage): + from pxr import Usd,UsdPhysics + root=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') + if not root:return [] + return [str(p.GetPath()) for p in Usd.PrimRange(root) if p.HasAPI(UsdPhysics.CollisionAPI) and p.GetAttribute('physics:collisionEnabled').Get() is False] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..f9959a05280f366084a12f4d2009627868d11d6b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +"""Read-only regression for the held-target/robot carry preflight rule.""" +import hashlib +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +from pxr import Usd, UsdGeom # noqa: E402 +from collision_scene import Body, ROBOT, Robot, collider_mesh, matrix as usd_matrix, rigid_part # noqa: E402 +from carry_transition import Clearance # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + ticks = checkpoint["tick"].astype(int) + phases = np.array([value.decode() if isinstance(value, bytes) else str(value) for value in checkpoint["phase"]]) + names = checkpoint["dof_names"].tolist() + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + # Deepest native assigned-finger contact in the accepted pickup prefix. + safe_frame = int(np.flatnonzero(ticks == 10297)[0]) + bad_frames = [int(np.flatnonzero(ticks == tick)[0]) for tick in (15138, 15139)] + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][safe_frame]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + vertices = np.load(item["controls_file"])["target_local"] + initial_held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][safe_frame, hand_index])) @ matrix( + checkpoint["target_pose_all"][safe_frame, target_index] + ) + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, initial_held, prop_poses) + checker.deadline = time.monotonic() + 900 + allowed = {f"/World/Robot/gripper_r_{side}_link4" for side in ("inner", "outer")} + # Also retain the exact authored physical target cube. The planner target + # hull can sit inside PhysX's contact offsets and have no literal crossing; + # the exact cube exercises the intended-link 10 mm witness on the accepted + # physical grasp state without changing Clearance's production geometry. + cache = UsdGeom.XformCache() + target_prim = stage.GetPrimAtPath(item["target"] + "/CanonicalGraspCollision") + target_owner = stage.GetPrimAtPath(item["target"]) + target_mesh, target_method, target_approximation = collider_mesh(target_prim) + target_mesh.apply_transform( + np.linalg.inv(rigid_part(usd_matrix(cache.GetLocalToWorldTransform(target_owner)))) + @ usd_matrix(cache.GetLocalToWorldTransform(target_prim)) + ) + physical_target = Body(item["target"] + "/CanonicalGraspCollision", target_mesh) + + def target_robot_detail(frame): + q = checkpoint["q"][frame].astype(float) + base = matrix(checkpoint["base_pose"][frame]) + recorded_hand = matrix(checkpoint["robot_link_pose"][frame, hand_index]) + recorded_target = matrix(checkpoint["target_pose_all"][frame, target_index]) + held = np.linalg.inv(recorded_hand) @ recorded_target + checker.held = held + checker.results.clear() + local = robot.fk(names, q, np.eye(4)) + self_ok, self_detail = checker.self_check(local, robot.root) + fk = {name: base @ transform for name, transform in local.items()} + for body in checker.self_check.bodies: + body.update(fk[body.name.replace("/World/Robot", robot.root, 1)]) + checker.target.update(fk[robot.root + "/gripper_r_base_link"] @ held) + physical_target.update(recorded_target) + rows = [] + for body in checker.self_check.bodies: + if not checker.broad_overlap(checker.target, body): + continue + hit = checker.contact(checker.target, body, containment=True) + if hit is None: + continue + witness = None + permitted = False + if body.name in allowed: + witness = checker.separation_witness(checker.target, body, hit, tolerance_mm=10.0, vertical_support=False) + permitted = bool(witness.get("certified")) + rows.append({"robot_body": body.name, "contact": hit, "allowed_distal": body.name in allowed, "separation_witness": witness, "permitted": permitted}) + physical_rows = [] + for body in checker.self_check.bodies: + if not checker.broad_overlap(physical_target, body): + continue + hit = checker.contact(physical_target, body, containment=True) + if hit is None: + continue + witness = None + permitted = False + if body.name in allowed: + witness = checker.separation_witness(physical_target, body, hit, tolerance_mm=10.0, vertical_support=False) + permitted = bool(witness.get("certified")) + physical_rows.append({"robot_body": body.name, "contact": hit, "allowed_distal": body.name in allowed, "separation_witness": witness, "permitted": permitted}) + overall = checker.check(q, base) + return { + "source_control_tick": int(ticks[frame]), + "phase": phases[frame], + "self_and_wrist_ok": bool(self_ok), + "self_and_wrist_detail": self_detail, + "target_robot_intersections": rows, + "target_robot_rule_passed": all(row["permitted"] for row in rows), + "authored_physical_target_robot_intersections": physical_rows, + "authored_physical_target_robot_rule_passed": all(row["permitted"] for row in physical_rows), + "clearance_check_passed": bool(overall), + "clearance_last": None if overall else checker.last, + } + + safe = target_robot_detail(safe_frame) + bad = [target_robot_detail(frame) for frame in bad_frames] + safe_ok = (safe["clearance_check_passed"] and safe["target_robot_rule_passed"] + and bool(safe["authored_physical_target_robot_intersections"]) + and safe["authored_physical_target_robot_rule_passed"] + and {row["robot_body"] for row in safe["authored_physical_target_robot_intersections"]} <= allowed) + bad_ok = all( + not row["clearance_check_passed"] + and row["clearance_last"].get("kind") == "held_target_robot" + and row["clearance_last"].get("robot") == "/World/Robot/body_link2" + for row in bad + ) + result = { + "schema": "multi_item.carry_target_robot_regression.v1", + "passed": bool(safe_ok and bad_ok), + "read_only": True, + "carry_transition_sha256": hashlib.sha256((W / "carry_transition.py").read_bytes()).hexdigest(), + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "accepted_prefix_state": safe, + "confirmed_bad_states": bad, + "assertions": { + "accepted_source_final_pick_has_only_certified_assigned_distal_contacts": bool(safe_ok), + "case5_ticks_15138_15139_reject_body_link2": bool(bad_ok), + "whole_gripper_prefix_exemption_absent": True, + }, + "authored_physical_target_geometry": {"method": target_method, "approximation": target_approximation}, + "limitations": "The safe state is the physically retained case5 final shelf-pick state from the same continuous prefix; runtime bilateral contact evidence remains a separate pickup gate.", + "active_case_written": False, + "physics_executed": False, + } + output = W / "CARRY_TARGET_ROBOT_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"output": str(output), "passed": result["passed"], "safe": safe, "bad": bad}, indent=2)) + raise SystemExit(0 if result["passed"] else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..ab4d7850c3e29e863c0de4285f06b0ae81c68cf4 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_multi_validation.py @@ -0,0 +1,107 @@ +"""Focused contract tests; these do not launch or step the simulator.""" +import gzip +import json +import tempfile +import unittest +from pathlib import Path + +import numpy as np + +from multi_validation import audit_ranges, contact_capture_audit, grasp_retention_audit, recording_contract, validate_phase_order + + +def phases(count=3): + names = ["initial_upper_shelf_posture"] + indices = [-1] + core = ["waist_to_selected_shelf", "shelf_pick", "shelf_exit_reverse", + "forward_navigation", "bridge_to_place_initial", "place_descent", + "place_release", "release_retreat", "settle"] + for item in range(count): + suffixes = core + (["empty_return"] if item < count - 1 else []) + ["complete"] + names += [f"item_{item:02d}_{suffix}" for suffix in suffixes] + indices += [item] * len(suffixes) + return names, np.asarray(indices) + + +class Recording: + def __init__(self, names, indices, targets): + n = len(names) + self.files = ["q", "phase", "item_index", "active_target_path", "target_paths", "target_pose_all"] + self.values = dict(q=np.zeros((n, 1)), phase=np.asarray(names), item_index=indices, + active_target_path=np.asarray(["" if i < 0 else targets[i] for i in indices]), + target_paths=np.asarray(targets), target_pose_all=np.zeros((n, len(targets), 7))) + + def __getitem__(self, key): + return self.values[key] + + +class MultiValidationTests(unittest.TestCase): + def test_valid_order_and_no_final_return(self): + names, indices = phases() + self.assertEqual(len(validate_phase_order(names, indices, 3)), 3) + + def test_live_pilot_initial_item_zero_encoding_is_authentic(self): + names, indices = phases();indices[0]=0;targets=["/A","/B","/C"] + recording=Recording(names,indices,targets);recording.values["active_target_path"][0]=targets[0] + recording_contract(recording,[dict(target=x) for x in targets]) + self.assertEqual(len(validate_phase_order(names,indices,3)),3) + + def test_final_return_is_rejected(self): + names, indices = phases() + names.insert(-1, "item_02_empty_return"); indices = np.insert(indices, -1, 2) + with self.assertRaisesRegex(ValueError, "final item"): + validate_phase_order(names, indices, 3) + + def test_skipping_nonfinal_return_is_rejected(self): + names, indices = phases();at = names.index("item_00_empty_return") + names.pop(at);indices=np.delete(indices,at) + with self.assertRaisesRegex(ValueError, "return empty-handed"): + validate_phase_order(names, indices, 3) + + def test_audit_ranges_are_target_homogeneous_and_complete(self): + names, indices = phases();targets=["/A", "/B", "/C"] + requested=[dict(target=x) for x in targets];ranges=audit_ranges(Recording(names,indices,targets),requested,4,16) + self.assertEqual([x for a,b,_ in ranges for x in range(a,b)],list(range(len(names)))) + active=indices.copy();active[active<0]=0 + self.assertTrue(all(np.all(active[a:b]==item) for a,b,item in ranges)) + + def test_contact_receipt_requires_every_target(self): + requested=[dict(target="/A"),dict(target="/B"),dict(target="/C")] + with tempfile.TemporaryDirectory() as td: + out=Path(td);receipt=dict(requested_targets=["/A","/B","/C"],filter_semantics="robot_or_any_requested_target",all_physics_ticks=True,dropped_rows=0,control_ticks=9,callback_rows=2) + (out/"CONTACT_CAPTURE.json").write_text(json.dumps(receipt)) + with gzip.open(out/"contacts.jsonl.gz","wt") as stream: + for tick,target in enumerate(("/A","/B"),1):stream.write(json.dumps(dict(tick=tick,actors=[target,"/World/Assets/Tray"]))+"\n") + self.assertFalse(contact_capture_audit(out,requested,9)["passed"]) + + def test_contact_rows_align_with_active_item(self): + requested=[dict(target="/A"),dict(target="/B"),dict(target="/C")] + with tempfile.TemporaryDirectory() as td: + out=Path(td);receipt=dict(requested_targets=["/A","/B","/C"],filter_semantics="robot_or_any_requested_target",all_physics_ticks=True,dropped_rows=0,control_ticks=3,callback_rows=3) + (out/"CONTACT_CAPTURE.json").write_text(json.dumps(receipt));indices=np.array([0,1,2]);active=np.array(["/A","/B","/C"]);phase=np.array(["item_00_settle","item_01_settle","item_02_settle"]) + with gzip.open(out/"contacts.jsonl.gz","wt") as stream: + for tick,target in enumerate(active,1):stream.write(json.dumps(dict(tick=tick,item_index=tick-1,active_target_path=target,phase=phase[tick-1],actors=[target,"/World/Assets/Tray"]))+"\n") + self.assertTrue(contact_capture_audit(out,requested,3,indices,active,phase)["passed"]) + + def test_grasp_retention_requires_bilateral_final_contact_and_environment_clearance(self): + phase=np.asarray(["item_00_shelf_pick"]*30);indices=np.zeros(30,dtype=int) + requested=[dict(target="/Target",hand="r")] + rows=[] + for tick in range(1,31): + for finger in ("inner","outer"): + rows.append(dict(tick=tick,actors=["/Target",f"/World/Robot/gripper_r_{finger}_link4"],colliders=[])) + self.assertTrue(grasp_retention_audit(rows,phase,indices,requested)["passed"]) + rows.append(dict(tick=30,actors=["/Target","/World/Assets/Shelf"],colliders=[])) + report=grasp_retention_audit(rows,phase,indices,requested) + self.assertFalse(report["passed"]) + self.assertEqual(report["items"][0]["environment_contact_ticks"],[30]) + + def test_grasp_retention_rejects_height_only_pick_without_contacts(self): + phase=np.asarray(["item_00_shelf_pick"]*30);indices=np.zeros(30,dtype=int) + report=grasp_retention_audit([],phase,indices,[dict(target="/Target",hand="l")]) + self.assertFalse(report["passed"]) + self.assertEqual(report["items"][0]["simultaneous_bilateral_contact_tick_count"],0) + + +if __name__ == "__main__": + unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_return_head_policy.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_return_head_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..0a12ec943068ab5aa12d4e6023fd70c099201c87 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_return_head_policy.py @@ -0,0 +1,74 @@ +"""Behavior checks for head commands at return navigation boundaries.""" +import unittest +from unittest.mock import patch +import numpy as np +import return_head_policy as policy + +NAMES=[f'joint{i}' for i in range(15)]+[policy.BODY_PITCH_JOINT]+list(policy.HEAD_JOINTS)+['grip_l','grip_r'] + +class Robot: + def __init__(self,ctx):self.ctx=ctx + def get_joint_positions(self):return self.ctx['native'].copy() + +class ReturnHeadTests(unittest.TestCase): + def context(self): + q=np.zeros(20);q[15]=np.deg2rad(30);q[16:18]=[.1,.7] + ctx=dict(names=NAMES,control_names=NAMES,ids=np.arange(20),native=q, + return_bridge_q=np.r_[np.zeros(16),.0,.45,.0,.0],rows=[],item_index=0, + stage=None,carry_robot=None,base=None,prop_paths=[],props=[],out=__import__('pathlib').Path('/unused')) + ctx['robot']=Robot(ctx);ctx['mat']=lambda body:np.eye(4) + ctx['constrain']=lambda mode:None;ctx['item_phase']=lambda phase:None;ctx['dump']=lambda path,record:None + def blend(target,seconds): + ctx['native'][:]=target;ctx['rows'].extend(range(round(seconds*60))) + ctx['blend']=blend + return ctx + + @patch('multi_motion.EmptyClearance') + def test_every_return_drive_is_level_then_source_gaze_restored(self,check): + check.return_value.segment.return_value=True + ctx=self.context();moving=[] + def original(primitives,**kwargs): + for primitive in primitives: + if primitive.get('bridge_before'):ctx['blend'](ctx['return_bridge_q'],4.) + moving.append(ctx['native'][16:18].copy()) + ctx['base_vw']=np.zeros(2) + execute=policy.wrap_executor(ctx,original) + source=ctx['return_bridge_q'].copy() + execute([dict(kind='drive',return_stage='table_retreat')],return_motion=True,item_job={}) + self.assertTrue(np.array_equal(ctx['native'][16:18],[0,policy.PITCH_CLIP_RAD[0]])) + execute([dict(kind='drive',return_stage='room_navigation'),dict(kind='drive',bridge_before=True)],return_motion=True,item_job={}) + np.testing.assert_array_equal(moving, [[0,policy.PITCH_CLIP_RAD[0]],[0,policy.PITCH_CLIP_RAD[0]],[0,0]]) + np.testing.assert_array_equal(ctx['native'][16:18],source[16:18]) + np.testing.assert_array_equal(ctx['return_bridge_q'],source) + self.assertEqual(check.return_value.segment.call_count,2) + + @patch('multi_motion.EmptyClearance') + def test_unsafe_head_raise_stops_before_navigation(self,check): + check.return_value.segment.return_value=False;check.return_value.last='collision' + ctx=self.context();calls=[];before=ctx['native'].copy() + with self.assertRaisesRegex(RuntimeError,'not clear'): + policy.wrap_executor(ctx,lambda *a,**k:calls.append(1))([{}],return_motion=True,item_job={}) + self.assertEqual(calls,[]);self.assertEqual(len(ctx['rows']),0) + np.testing.assert_array_equal(ctx['native'],before) + + @patch('multi_motion.EmptyClearance') + def test_planner_receives_same_level_head_as_executor_and_preserves_inputs(self,check): + check.return_value.segment.return_value=True + q=np.arange(20)/100;next_q=q+.02;original=q.copy();next_original=next_q.copy();received=[] + def planner(stage,robot,names,start,base,props,next_base,end,release,job): + received.extend([start.copy(),end.copy()]);return start.copy(),[{'bridge_before':True}],{} + # Job JSON supplies the base transforms as nested lists. The wrapper + # must normalize them before its collision checks. + _,_,report=policy.wrap_planner(planner)(None,None,NAMES,q,np.eye(4).tolist(),{},np.eye(4).tolist(),next_q,{}, {}) + for r,s in zip(received,[q,next_q]): + np.testing.assert_array_equal(r[16:18],policy.head_target(s,NAMES));np.testing.assert_array_equal(r[:16],s[:16]);np.testing.assert_array_equal(r[18:],s[18:]) + np.testing.assert_array_equal(q,original);np.testing.assert_array_equal(next_q,next_original) + self.assertTrue(report['head_policy']['includes_shelf_approach']) + self.assertEqual(check.return_value.segment.call_count,2) + + def test_carry_motion_is_unmodified(self): + ctx=self.context();before=ctx['native'].copy();seen=[] + policy.wrap_executor(ctx,lambda *a,**k:seen.append(k))([{}],return_motion=False,item_job={}) + np.testing.assert_array_equal(ctx['native'],before);self.assertFalse(seen[0]['return_motion']) + +if __name__=='__main__':unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_startup_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_startup_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba19d8e1f9dcee21bcb51a8178537d04f5100d9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/test_startup_validation.py @@ -0,0 +1,106 @@ +import json +import tempfile +import unittest +from pathlib import Path + +import numpy as np + +import startup_validation as sv + + +class StartupValidationTests(unittest.TestCase): + def test_quaternions_are_normalized_before_angle(self): + q = np.array([[0.0, 0.0, 0.0, 0.99999994]], dtype=np.float32) + self.assertEqual(float(sv.angular_from_reference(q, q[0])[0]), 0.0) + + def test_clock_contract_and_wrong_rate(self): + with tempfile.TemporaryDirectory() as folder: + out = Path(folder) + recording = {"q": np.zeros((31, 1)), "tick": np.arange(1, 32), + "timestamp": np.arange(1, 32) / 60.0} + preroll = {"q": np.zeros((33, 1))} + clock = {"schema": "startup_preroll_clock_v1", "physics_hz": 240, + "control_hz": 60, "training_hz": 20, "substeps_per_control": 4, + "unrecorded_preroll_physics_steps": 132, + "unrecorded_preroll_duration_s": 33 / 60, + "retained_control_rows": 31, "retained_first_control_row": 0, + "retained_first_post_step_global_physics_step": 136, + "retained_timestamp_formula": "(row_index+1)/60", + "retained_post_step_global_physics_step_formula": "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "retained_camera_row_formula": "control_tick % 3 == 0", + "retained_camera_frame_count": 10, "command_semantics": "native", + "contact_semantics": "post-preroll"} + sv.write_json(out / "PHYSICS_CLOCK.json", clock) + self.assertTrue(sv.clock_audit(out, recording, preroll)["passed"]) + clock["physics_hz"] = 120 + sv.write_json(out / "PHYSICS_CLOCK.json", clock) + self.assertFalse(sv.clock_audit(out, recording, preroll)["passed"]) + + def test_camera_exact_tick_and_mount_mapping(self): + with tempfile.TemporaryDirectory() as folder: + out = Path(folder) + rows = 9 + paths = list(sv.CAMERA_LINKS.values()) + pose = np.zeros((rows, 3, 7)); pose[..., 6] = 1 + pose[:, :, 0] = np.arange(rows)[:, None] * 1e-4 + recording = {"q": np.zeros((rows, 1)), "robot_link_paths": np.asarray(paths), + "robot_link_pose": pose} + ticks = np.arange(3, rows + 1, 3) + transforms = sv._pose_matrix_xyzw(pose[ticks - 1]) + np.savez(out / "camera_extrinsics_20hz.npz", camera_keys=np.asarray(list(sv.CAMERAS)), + control_tick=ticks, timestamp=ticks / 60, global_physics_step=120 + 4 * ticks, + world_T_camera=transforms) + clock = {"unrecorded_preroll_physics_steps": 120} + self.assertTrue(sv.camera_extrinsics_audit(out, recording, clock)["passed"]) + np.savez(out / "camera_extrinsics_20hz.npz", camera_keys=np.asarray(list(sv.CAMERAS)), + control_tick=ticks + 1, timestamp=ticks / 60, global_physics_step=120 + 4 * ticks, + world_T_camera=transforms) + self.assertFalse(sv.camera_extrinsics_audit(out, recording, clock)["passed"]) + + def test_prop_scope_rejects_distractor_drop_and_excludes_active_pick(self): + with tempfile.TemporaryDirectory() as folder: + case = Path(folder); out = case / "rollout"; (out / "physics_ticks").mkdir(parents=True) + paths = np.asarray(["/distractor", "/target"]) + initial = np.zeros((2, 7)); initial[:, 6] = 1; initial[:, 2] = 1 + pre = np.repeat(initial[None], 30, axis=0) + np.savez(out / "PREROLL_EVIDENCE.npz", q=np.zeros((30, 1)), prop_paths=paths, + prop_pose=pre, initial_prop_pose_before_preroll=initial) + sv.write_json(out / "INITIAL_PROP_NO_FALL.json", {"passed": True, "physics_samples": 30, + "maxima": {"translation_m": 0, "downward_m": 0, "rotation_rad": 0}, + "all_requested_and_nonrequested_props_included": True}) + retained = np.repeat(initial[None], 8, axis=0) + retained[3:, 0, 2] -= 0.011 + retained[2:4, 1, 0] += 0.20 + np.savez(out / "physics_ticks/full_state.npz", rigid_body_paths=paths, + prop_xyz_m=retained[:, :, :3], prop_wxyz=retained[:, :, [6, 3, 4, 5]]) + sv.write_json(out / "ITEM_EVENTS.json", {"events": [ + {"event": "item_completed", "item_index": 0, "target": "/target", "tick": 5}]}) + recording = {"phase": np.asarray(["initial", "initial", "item_00_shelf_pick", + "item_00_shelf_pick", "item_00_complete", + "item_00_complete", "item_00_complete", "item_00_complete"]), + "item_index": np.zeros(8, dtype=int)} + report = sv.prop_no_fall_audit(case, recording, [{"target": "/target"}]) + self.assertFalse(report["passed"]) + rows = {row["path"]: row for row in report["paths"]} + self.assertFalse(rows["/distractor"]["passed"]) + self.assertTrue(rows["/target"]["passed"]) + + def test_frozen_gate_detects_later_mutation(self): + with tempfile.TemporaryDirectory() as folder: + case = Path(folder); out = case / "rollout"; out.mkdir() + old_frozen, old_raw = sv.FROZEN_AUDITS, sv.RAW_EVIDENCE + try: + sv.FROZEN_AUDITS = ["audit.json"] + sv.RAW_EVIDENCE = ["raw.bin"] + sv.write_json(out / "audit.json", {"passed": True}) + (out / "raw.bin").write_bytes(b"native") + sv.freeze_physical_gate(case, 4) + self.assertTrue(sv.verify_physical_gate(case)["passed"]) + (out / "raw.bin").write_bytes(b"changed") + self.assertFalse(sv.verify_physical_gate(case)["passed"]) + finally: + sv.FROZEN_AUDITS, sv.RAW_EVIDENCE = old_frozen, old_raw + + +if __name__ == "__main__": + unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/tray_appearance.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/tray_appearance.py new file mode 100644 index 0000000000000000000000000000000000000000..95a16cd84f09a11ba51ce976946600a06e1af299 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/tray_appearance.py @@ -0,0 +1,10 @@ +"""Original tray, same white/gray/black solid palette as the standalone task.""" +import random,json +from pxr import UsdShade,Sdf,Gf + +def apply(stage,job): + rng=random.Random(json.dumps(['chain_original_tray',job['seed']]));palette={'white':.85,'gray':.14,'black':.025};name=rng.choice(list(palette));value=palette[name] + material=UsdShade.Material.Define(stage,'/World/Looks/ChainOriginalTray');shader=UsdShade.Shader.Define(stage,'/World/Looks/ChainOriginalTray/Surface');shader.CreateIdAttr('UsdPreviewSurface') + shader.CreateInput('diffuseColor',Sdf.ValueTypeNames.Color3f).Set(Gf.Vec3f(value));shader.CreateInput('roughness',Sdf.ValueTypeNames.Float).Set(.5);shader.CreateOutput('surface',Sdf.ValueTypeNames.Token);material.CreateSurfaceOutput().ConnectToSource(shader.ConnectableAPI(),'surface') + UsdShade.MaterialBindingAPI.Apply(stage.GetPrimAtPath('/World/Assets/Tray')).Bind(material,UsdShade.Tokens.strongerThanDescendants) + return dict(policy='standalone_original_neutral_tray_palette',color=name,linear_rgb=[value]*3,texture=None,geometry_changed=False) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_case.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_case.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd041017eeb792fea5d4c07b4a67ba899aae96b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_case.py @@ -0,0 +1,113 @@ +"""Validate one completed native episode and stage it for independent review. + +This program never publishes or accepts an episode. It freezes physical and +startup evidence, exports a candidate, and leaves manual visual plus official +LeRobot reader QA as separate mandatory gates. +""" +import fcntl +import json +import os +import subprocess +import sys +import time +from pathlib import Path + +W = Path(__file__).resolve().parent +case = Path(sys.argv[1]).resolve() +out = case / "rollout" +CPU = sys.executable +sys.path.insert(0, "/mnt/agent-bench-data/lingjun/ziche/work/family3_repair/collision/python_deps") +from startup_validation import freeze_physical_gate, video_decode_audit +from validate_export import task_audit, write + + +def run(script, args, log, env=None): + with (out / log).open("w") as stream: + result = subprocess.run([CPU, str(W / script), str(case), *args], stdout=stream, + stderr=subprocess.STDOUT, env=env) + if result.returncode: + raise RuntimeError(f"{script} {args} exited {result.returncode}; see {log}") + + +lock_dir = W / "validation_locks" +lock_dir.mkdir(exist_ok=True) +case_lock = (lock_dir / f"{case.name}.lock").open("a+") +fcntl.flock(case_lock, fcntl.LOCK_EX | fcntl.LOCK_NB) +capacity = None +while capacity is None: + for index in range(int(os.environ.get("CHAIN_VALIDATION_CAPACITY", "4"))): + candidate = (lock_dir / f"capacity_{index}.lock").open("a+") + try: + fcntl.flock(candidate, fcntl.LOCK_EX | fcntl.LOCK_NB) + capacity = candidate + break + except BlockingIOError: + candidate.close() + if capacity is None: + time.sleep(10) + +try: + write(case / "STATUS.json", dict(status="VERIFYING", phase="startup_task_views_collisions", + updated=time.time())) + from runtime_evidence import reconcile + reconcile(case) + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Task/startup/schema: " + str(report["failures"])) + + run("audit_views.py", [], "views_audit.log") + for name in ("WRIST_AUDIT.json", "VISIBILITY_AUDIT.json"): + if json.loads((out / name).read_text()).get("passed") is not True: + raise RuntimeError(name + " failed") + + run("audit_geometry.py", ["self"], "self_audit.log") + if json.loads((out / "SELF_COLLISION_AUDIT.json").read_text()).get("passed") is not True: + raise RuntimeError("Native self collision requires replanning") + + world_env = dict(os.environ, OPENBLAS_NUM_THREADS="1", OMP_NUM_THREADS="1") + run("audit_geometry.py", ["world"], "world_audit.log", world_env) + world = json.loads((out / "WORLD_COLLISION_AUDIT.json").read_text()) + if world.get("geometry_status") != "CLEAR_AT_CHECKED_SAMPLES" or world.get("complete_trajectory_checked") is not True: + raise RuntimeError("World collision review is not clear and complete") + + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Final task/startup/schema: " + str(report["failures"])) + decode = video_decode_audit(out, report["training_frames"]) + if not decode["passed"]: + raise RuntimeError("Full three-view video decode audit failed") + gate = freeze_physical_gate(case, world.get("parallel_shards")) + if gate["passed"] is not True: + raise RuntimeError("Physical gate freeze failed") + + export_python = "/mnt/agent-bench-data/lingjun/ziche/work/shelf_training_target_layer60_20260916/lerobot_env/bin/python" + with (out / "export.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "validate_export.py"), str(case)], + stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Staged export verification failed; see export.log") + run("prepare_visual_qa.py", [], "visual_evidence.log") + dataset = W / "staged_datasets" / case.name + layout = int(json.loads((case / "job.json").read_text())["layout_id"]) + reader_report = W / "qa" / f"layout_{layout:02d}" / "OFFICIAL_LEROBOT_READER_QA.json" + reader_report.parent.mkdir(parents=True, exist_ok=True) + with (out / "official_reader.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "official_lerobot_reader_qa.py"), + "--layout", str(layout), "--dataset", str(dataset), + "--output", str(reader_report)], stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Official LeRobot reader QA failed; see official_reader.log") + write(case / "STATUS.json", dict(status="MANUAL_REVIEW_PENDING", phase="first_0p5s_and_all_phase_panels", + frames=report["training_frames"], updated=time.time(), + staged_dataset=str(dataset), physical_gate=str(out / "PHYSICAL_GATE_RESULT.json"), + official_reader=str(reader_report), accepted=False)) +except Exception as exc: + write(case / "STATUS.json", dict(status="REVIEW_REQUIRED", phase="validation_failure", + error=repr(exc), updated=time.time(), accepted=False)) + raise +finally: + if capacity is not None: + fcntl.flock(capacity, fcntl.LOCK_UN) + capacity.close() + fcntl.flock(case_lock, fcntl.LOCK_UN) + case_lock.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_export.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_export.py new file mode 100644 index 0000000000000000000000000000000000000000..ef2944855983730ca700c1c1b78803a54dd62949 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_export.py @@ -0,0 +1,318 @@ +"""Verify schema/task evidence; export only after independent collision gates pass.""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + physical_verification=verify_physical_gate(case);assert physical_verification['passed'],physical_verification + frozen=out/'physical_evidence';audit=json.loads((frozen/'TASK_SCHEMA_AUDIT.json').read_text());assert audit['passed'],audit['failures'];assert audit['recording_sha256']==hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest() + for file,field,value in [('SELF_COLLISION_AUDIT.json','passed',True),('WORLD_COLLISION_AUDIT.json','geometry_status','CLEAR_AT_CHECKED_SAMPLES'),('WRIST_AUDIT.json','passed',True),('VISIBILITY_AUDIT.json','passed',True)]: + assert json.loads((frozen/file).read_text())[field]==value,file + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + destination=W/'staged_datasets'/f"layout_{job['layout_id']:02d}";temp=destination.with_name(destination.name+'.pending');assert not destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=True,gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=True,official_reader_pending=True) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + temp.rename(destination);return destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_staged.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_staged.py new file mode 100644 index 0000000000000000000000000000000000000000..88d5f8ee86d702f799a7b69c4d5b66d63e51f97c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/replay_code/validate_staged.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +"""Admit a staged candidate to VALIDATION_READY after independent QA only.""" +import argparse +import hashlib +import json +import time +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from startup_validation import sha256, verify_physical_gate, write_json + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def load(path): + return json.loads(Path(path).read_text()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + args = parser.parse_args() + lid = f"{args.layout:02d}" + case = (W / "cases" / f"layout_{lid}").resolve() + out = case / "rollout" + dataset = W / "staged_datasets" / f"layout_{lid}" + sim = dataset / "sim_info/episode_000000" + evidence_dir = W / "qa" / f"layout_{lid}" / "visual_evidence" + panels_path = evidence_dir / "PANELS.json" + manual_path = W / "qa" / f"layout_{lid}" / "ROOT_MANUAL_REVIEW.json" + reader_path = W / "qa" / f"layout_{lid}" / "OFFICIAL_LEROBOT_READER_QA.json" + failures = [] + + physical = verify_physical_gate(case) + if not physical["passed"]: + failures.append("immutable_physical_gate") + provenance = load(sim / "STARTUP_PREROLL_PROVENANCE.json") + evidence_hashes = provenance.get("evidence_sha256", {}) + bad_evidence = [name for name, digest in evidence_hashes.items() + if not (sim / name).is_file() or sha256(sim / name) != digest] + if bad_evidence: + failures.append("exported_provenance:" + ",".join(bad_evidence)) + + panels = load(panels_path) + panel_hashes = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} + if any(not (evidence_dir / name).is_file() or sha256(evidence_dir / name) != digest + for name, digest in panel_hashes.items()): + failures.append("visual_panel_hashes") + manual = load(manual_path) + manual_ok = (manual.get("schema") == "ziche.multi10.root_manual_visual_review.v1" + and manual.get("layout_id") == args.layout and manual.get("passed") is True + and manual.get("panels") == panel_hashes + and manual.get("reviewed_all_three_views") is True + and manual.get("reviewed_first_retained_0p5s") is True + and manual.get("reviewed_every_phase_midpoint") is True) + if not manual_ok: + failures.append("manual_visual_review") + + reader = load(reader_path) + parquet = dataset / "data/chunk-000/episode_000000.parquet" + reader_ok = (reader.get("passed") is True and reader.get("layout_id") == args.layout + and reader.get("parquet_sha256") == sha256(parquet)) + if not reader_ok: + failures.append("official_lerobot_reader") + + decode = load(sim / "VIDEO_DECODE_AUDIT.json") + video_hashes = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + video_hashes[camera] = sha256(video) + if (video_hashes[camera] != provenance["video_sha256"].get(camera) + or video_hashes[camera] != decode["videos"][camera]["sha256"] + or decode["videos"][camera].get("passed") is not True): + failures.append("video:" + camera) + + table = pq.read_table(parquet) + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + samples = np.arange(2, len(recording["q"]), 3) + ticks = np.asarray(table["source_control_tick"].to_numpy(), dtype=np.int64) + items = np.asarray(table["item_index"].to_numpy(), dtype=np.int64) + clock = load(out / "PHYSICS_CLOCK.json") + sim_time = np.asarray(table["simulator_time_s"].to_numpy(), dtype=np.float64) + alignment = (len(table) == len(samples) and np.array_equal(ticks, samples + 1) + and np.array_equal(items, recording["item_index"][samples]) + and np.allclose(sim_time, float(clock["unrecorded_preroll_duration_s"]) + + (samples + 1) / 60.0, atol=2e-6, rtol=0)) + if not alignment: + failures.append("staged_source_alignment") + + report = {"schema": "ziche.multi10.staged_final_qa.v1", "layout_id": args.layout, + "passed": not failures, "failures": failures, "case": str(case), + "dataset": str(dataset), "physical_gate_verification": physical, + "provenance_sha256": sha256(sim / "STARTUP_PREROLL_PROVENANCE.json"), + "manual_review": {"path": str(manual_path), "sha256": sha256(manual_path), "passed": manual_ok}, + "official_reader": {"path": str(reader_path), "sha256": sha256(reader_path), "passed": reader_ok}, + "panels_receipt_sha256": sha256(panels_path), "video_sha256": video_hashes, + "parquet_sha256": sha256(parquet), "staged_source_alignment": alignment, + "status_semantics": "VALIDATION_READY is review complete but not publication or ACCEPTED", + "completed_unix": time.time()} + qa_path = W / "qa" / f"layout_{lid}.json" + write_json(qa_path, report) + if not report["passed"]: + raise SystemExit(1) + write_json(case / "STATUS.json", {"status": "VALIDATION_READY", "phase": "review_complete", + "accepted": False, "dataset": str(dataset), "qa": str(qa_path), + "qa_sha256": sha256(qa_path), "updated": time.time()}) + print(qa_path) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_00.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_00.json new file mode 100644 index 0000000000000000000000000000000000000000..b0ac01e2171c9a0edfc978645a3ddbee880f8211 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_00.json @@ -0,0 +1,88 @@ +[ + { + "kind": "drive", + "goal": [ + -1.226444595398072, + 1.5848132895203353, + -1.6289040933764731 + ], + "direction": -1, + "return_stage": "table_retreat" + }, + { + "kind": "turn", + "goal": [ + -1.226444595398072, + 1.5848132895203353, + -2.2567748333071034 + ], + "return_stage": "room_navigation" + }, + { + "kind": "drive", + "goal": [ + -1.8866031528100242, + 0.7783626325352906, + -2.2567748333071034 + ], + "direction": 1, + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.8866031528100242, + 0.7783626325352906, + -4.779138008609497 + ], + "return_stage": "room_navigation" + }, + { + "kind": "drive", + "goal": [ + -1.8565883898261104, + 1.227360532311024, + 1.5040472985700895 + ], + "direction": 1, + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.8565883898261104, + 1.227360532311024, + 1.5040472985700897 + ], + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.8565883898261104, + 1.227360532311024, + 0.313757348887558 + ], + "bridge_before": true, + "return_stage": "shelf_approach" + }, + { + "kind": "drive", + "goal": [ + -1.6004751648431665, + 1.3104629750479015, + 0.3137573488875581 + ], + "direction": 1, + "return_stage": "shelf_approach" + }, + { + "kind": "turn", + "goal": [ + -1.6004751648431665, + 1.3104629750479015, + 1.5040472985700897 + ], + "return_stage": "shelf_approach" + } +] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_01.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_01.json new file mode 100644 index 0000000000000000000000000000000000000000..24bc082c306417563fcd16799f2e04c726feda0d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/return_primitives.item_01.json @@ -0,0 +1,69 @@ +[ + { + "kind": "drive", + "goal": [ + -1.163106468584647, + 1.5888508160823767, + -1.6065754287276945 + ], + "direction": -1, + "return_stage": "table_retreat" + }, + { + "kind": "turn", + "goal": [ + -1.163106468584647, + 1.5888508160823767, + 3.8912117150544434 + ], + "return_stage": "room_navigation" + }, + { + "kind": "drive", + "goal": [ + -1.535753795948065, + 1.2419589461734646, + 3.8912117150544434 + ], + "direction": 1, + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.535753795948065, + 1.2419589461734646, + -4.279550820147613 + ], + "return_stage": "room_navigation" + }, + { + "kind": "drive", + "goal": [ + -1.5897350388489868, + 1.3587861317212708, + 2.003634487031973 + ], + "direction": 1, + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.5897350388489868, + 1.3587861317212708, + 1.605979104047414 + ], + "return_stage": "room_navigation" + }, + { + "kind": "turn", + "goal": [ + -1.5897350388489868, + 1.3587861317212708, + 1.605979104047414 + ], + "bridge_before": true, + "return_stage": "shelf_approach" + } +] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/robot_and_camera_parameters.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/robot_and_camera_parameters.json new file mode 100644 index 0000000000000000000000000000000000000000..89ee561215eb034afc9f0c57a1bcaaca34b34607 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/robot_and_camera_parameters.json @@ -0,0 +1,1272 @@ +{ + "FixedJoint": { + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "False", + "physics:localPos0": "(2.6647785, 4.3904977, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(0.7372611, 0, 0, -0.6756079)", + "physics:localRot1": "(1, 0, 0, 0)" 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"False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.0015318942023441195", + "physics:localPos0": "(0, 0.0195, 0.0565)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(0.70709145, -0.004648257, -0.70709145, -0.004648257)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "0.0", + "physics:upperLimit": "45.0", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx82_gripper_r_outer_joint3": { + "drive:angular:physics:damping": "0.0", + "drive:angular:physics:maxForce": "inf", + "drive:angular:physics:stiffness": "1.0", + "drive:angular:physics:targetPosition": "0.0", + "drive:angular:physics:targetVelocity": "None", + "drive:angular:physics:type": "force", + "physics:axis": "Z", + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.00039846132858656347", + "physics:localPos0": "(0.030852, 0.018551, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(-4.371139e-8, 1, 0, 0)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "-10.0", + "physics:upperLimit": "10.0", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx83_gripper_r_outer_joint4": { + "drive:angular:physics:damping": "1.0", + "drive:angular:physics:maxForce": "inf", + "drive:angular:physics:stiffness": "10.0", + "drive:angular:physics:targetPosition": "0.0", + "drive:angular:physics:targetVelocity": "None", + "drive:angular:physics:type": "force", + "physics:axis": "Z", + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.00018224485393147916", + "physics:localPos0": "(0.018118, 0.01574, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(1, 0, 0, 0)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "-2.0", + "physics:upperLimit": "2.0", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx89_gripper_r_outer_joint0": { + "drive:angular:physics:damping": "1.0", + "drive:angular:physics:maxForce": "inf", + "drive:angular:physics:stiffness": "10.0", + "drive:angular:physics:targetPosition": "0.0", + "drive:angular:physics:type": "force", + "physics:axis": "Z", + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "2.2422236725105904e-05", + "physics:localPos0": "(0, 0.01, 0)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(1, 0, 0, 0)", + "physics:localRot1": "(1, 0, 0, 0)", + "physics:lowerLimit": "-114.591552734375", + "physics:upperLimit": "114.591552734375", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:angular:bounceThreshold": "0.0", + "physxLimit:angular:damping": "0.0", + "physxLimit:angular:restitution": "0.10000000149011612", + "physxLimit:angular:stiffness": "0.0" + }, + "idx92_gripper_r_center_joint": { + "physics:breakForce": "3.4028234663852886e+38", + "physics:breakTorque": "3.4028234663852886e+38", + "physics:collisionEnabled": "False", + "physics:excludeFromArticulation": "False", + "physics:jointEnabled": "True", + "physics:JointEquivalentInertia": "0.0", + "physics:localPos0": "(0, 0, 0.14308)", + "physics:localPos1": "(0, 0, 0)", + "physics:localRot0": "(0.70710677, 0, 0, -0.70710677)", + "physics:localRot1": "(1, 0, 0, 0)" + }, + "idx53_gripper_l_outer_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, 0.022, 0.073)", + "physics:localPos1": "(-0.032, -0.01, 0)", + "physics:localRot0": "(0.0046482855, 0.7070915, -0.004648252, 0.7070915)", + "physics:localRot1": "(1, 2.5377787e-8, -8.560052e-8, -1.27006e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx54_gripper_l_inner_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, -0.022, 0.073)", + "physics:localPos1": "(-0.032, 0.01, 0)", + "physics:localRot0": "(0.0046482855, 0.7070915, -0.004648252, 0.7070915)", + "physics:localRot1": "(1, 2.5377787e-8, -8.560052e-8, -1.27006e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx93_gripper_r_outer_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, 0.022, 0.073)", + "physics:localPos1": "(-0.032, -0.01, 0)", + "physics:localRot0": "(0.004648279, 0.7070915, -0.0046482584, 0.7070915)", + "physics:localRot1": "(1, 3.778885e-8, 1.02719035e-7, 4.0319208e-7)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "idx94_gripper_r_inner_joint2": { + "physics:axis": "Z", + "physics:breakForce": "inf", + "physics:breakTorque": "inf", + "physics:collisionEnabled": "False", + "physics:coneAngle0Limit": "-1.0", + "physics:coneAngle1Limit": "-1.0", + "physics:excludeFromArticulation": "True", + "physics:jointEnabled": "True", + "physics:localPos0": "(0, -0.022, 0.073)", + "physics:localPos1": "(-0.032, 0.01, 0)", + "physics:localRot0": "(0.0046464815, 0.7070915, -0.0046464815, 0.7070915)", + "physics:localRot1": "(1, 0, 0, 0)", + "physxJoint:armature": "0.0010000000474974513", + "physxJoint:jointFriction": "0.0", + "physxJoint:maxJointVelocity": "10000.0", + "physxLimit:cone:bounceThreshold": "0.0", + "physxLimit:cone:damping": "0.0", + "physxLimit:cone:restitution": "1.0", + "physxLimit:cone:stiffness": "0.0" + }, + "dof_names": [ + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx21_arm_l_joint1", + "idx61_arm_r_joint1", + "idx22_arm_l_joint2", + "idx62_arm_r_joint2", + "idx23_arm_l_joint3", + "idx63_arm_r_joint3", + "idx24_arm_l_joint4", + "idx64_arm_r_joint4", + "idx25_arm_l_joint5", + "idx65_arm_r_joint5", + "idx26_arm_l_joint6", + "idx66_arm_r_joint6", + "idx27_arm_l_joint7", + "idx67_arm_r_joint7", + "idx31_gripper_l_inner_joint1", + "idx41_gripper_l_outer_joint1", + "idx71_gripper_r_inner_joint1", + "idx81_gripper_r_outer_joint1", + "idx32_gripper_l_inner_joint3", + "idx42_gripper_l_outer_joint3", + "idx72_gripper_r_inner_joint3", + "idx82_gripper_r_outer_joint3", + "idx33_gripper_l_inner_joint4", + "idx43_gripper_l_outer_joint4", + "idx73_gripper_r_inner_joint4", + "idx83_gripper_r_outer_joint4", + "idx39_gripper_l_inner_joint0", + "idx49_gripper_l_outer_joint0", + "idx79_gripper_r_inner_joint0", + "idx89_gripper_r_outer_joint0" + ], + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "runtime_gripper_max_effort_Nm": { + "idx41_gripper_l_outer_joint1": 80.0, + "idx81_gripper_r_outer_joint1": 80.0 + }, + "cameras": { + "top_head": { + "path": "/World/Robot/head_link2/Head_Camera", + "attributes": { + "cameraSensorType": "camera", + "clippingPlanes": "[]", + "clippingRange": "(0.01, 10000000)", + "exposure": "0.0", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.8959999084472656", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "640.0", + "omni:lensdistortion:opencvPinhole:cy": "400.0", + "omni:lensdistortion:opencvPinhole:fx": "646.0", + "omni:lensdistortion:opencvPinhole:fy": "646.0", + "omni:lensdistortion:opencvPinhole:imageSize": "(1280, 800)", + "omni:lensdistortion:opencvPinhole:k1": "-0.051394399255514145", + "omni:lensdistortion:opencvPinhole:k2": "0.05811314284801483", + "omni:lensdistortion:opencvPinhole:k3": "-0.017540574073791504", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "-0.0012259036302566528", + "omni:lensdistortion:opencvPinhole:p2": "0.0006320750107988715", + 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"1.5", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.7601311206817627", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:kit:centerOfInterest": "(0, 0, -400)", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "424.9142150878906", + "omni:lensdistortion:opencvPinhole:cy": "241.9685516357422", + "omni:lensdistortion:opencvPinhole:fx": "433.967041015625", + "omni:lensdistortion:opencvPinhole:fy": "433.44677734375", + "omni:lensdistortion:opencvPinhole:imageSize": "(848, 480)", + "omni:lensdistortion:opencvPinhole:k1": "-0.05225364863872528", + "omni:lensdistortion:opencvPinhole:k2": "0.0581255704164505", + "omni:lensdistortion:opencvPinhole:k3": "-0.018849309533834457", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "0.0002689115353859961", + "omni:lensdistortion:opencvPinhole:p2": "0.0001695405662758276", + "omni:lensdistortion:opencvPinhole:s1": "0.0", + "omni:lensdistortion:opencvPinhole:s2": "0.0", + "omni:lensdistortion:opencvPinhole:s3": "0.0", + "omni:lensdistortion:opencvPinhole:s4": "0.0", + "p0": "-0.0003672190650831908", + "projection": "perspective", + "purpose": "default", + "shutter:close": "0.0", + "shutter:open": "0.0", + "stereoRole": "mono", + "verticalAperture": "2.130786657333374", + "verticalApertureOffset": "0.0", + "visibility": "invisible", + "xformOp:orient": "(0.14219926877000713, 0.6868383037850345, -0.6989317357080853, -0.13973883223733854)", + "xformOp:scale": "(1, 1, 1)", + "xformOp:translate": "(-0.07593376119019446, 0.009, 0.0205)", + "xformOpOrder": "[xformOp:translate, xformOp:orient, xformOp:scale]" + }, + "world_extrinsics": "camera_extrinsics_20hz.npz" + }, + "hand_right": { + "path": "/World/Robot/gripper_r_base_link/Right_Camera", + "attributes": { + "cameraProjectionType": "pinhole", + "clippingPlanes": "[]", + "clippingRange": "(0.01, 10000000)", + "exposure": "0.0", + "fisheyeResolutionBudget": "1.5", + "focalLength": "1.9299999475479126", + "focusDistance": "400.0", + "fStop": "0.0", + "horizontalAperture": "3.7601311206817627", + "horizontalApertureOffset": "0.0", + "omni:kit:cameraLock": "True", + "omni:kit:centerOfInterest": "(0, 0, -0.252056884844326)", + "omni:lensdistortion:model": "opencvPinhole", + "omni:lensdistortion:opencvPinhole:cx": "425.91937255859375", + "omni:lensdistortion:opencvPinhole:cy": "236.65130615234375", + "omni:lensdistortion:opencvPinhole:fx": "433.7381286621094", + "omni:lensdistortion:opencvPinhole:fy": "433.2144470214844", + "omni:lensdistortion:opencvPinhole:imageSize": "(848, 480)", + "omni:lensdistortion:opencvPinhole:k1": "-0.053031452000141144", + "omni:lensdistortion:opencvPinhole:k2": "0.05992193520069122", + "omni:lensdistortion:opencvPinhole:k3": "-0.019611800089478493", + "omni:lensdistortion:opencvPinhole:k4": "0.0", + "omni:lensdistortion:opencvPinhole:k5": "0.0", + "omni:lensdistortion:opencvPinhole:k6": "0.0", + "omni:lensdistortion:opencvPinhole:p1": "-0.00033373627229593694", + "omni:lensdistortion:opencvPinhole:p2": "6.050317460903898e-05", + "omni:lensdistortion:opencvPinhole:s1": "0.0", + "omni:lensdistortion:opencvPinhole:s2": "0.0", + "omni:lensdistortion:opencvPinhole:s3": "0.0", + "omni:lensdistortion:opencvPinhole:s4": "0.0", + "p0": "-0.0003672190650831908", + "projection": "perspective", + "purpose": "default", + "shutter:close": "0.0", + "shutter:open": "0.0", + "stereoRole": "mono", + "verticalAperture": "2.130786657333374", + "verticalApertureOffset": "0.0", + "visibility": "invisible", + "xformOp:orient": "(-0.1397388322373386, -0.6989317357080854, -0.6868383037850344, -0.14219926877000738)", + "xformOp:scale": "(1, 1, 1)", + "xformOp:translate": "(0.07593, -0.009, 0.021)", + "xformOpOrder": "[xformOp:translate, xformOp:orient, xformOp:scale]" + }, + "world_extrinsics": "camera_extrinsics_20hz.npz" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene.usda new file mode 100644 index 0000000000000000000000000000000000000000..91d63952a8dcd03baf4b64b1ca2e0e6d15d52620 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene.usda @@ -0,0 +1,1240 @@ +#usda 1.0 +( + endTimeCode = 0 + metersPerUnit = 1 + startTimeCode = -1 + subLayers = [ + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/collision_profile.usda@, + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/scene.usda@ + ] + timeCodesPerSecond = 60 + upAxis = "Z" +) + +over "World" +{ + over "Robot" ( + delete apiSchemas = ["PhysicsArticulationRootAPI"] + ) + { + over "gripper_l_inner_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.19577096, 3.243861, -5.0232286) + vector3f physics:velocity = (0.47626412, 2.023027, 2.6391103) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (-0.0029768813, 0.9101259, 0.4142226, 0.009036674) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1902922, 0.9822645, 0.9647886) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9376798, -3.0736039, 7.052485) + vector3f physics:velocity = (0.468837, 2.1855843, 2.7638059) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (-0.43227595, 0.010121019, 0.0025615634, 0.90168095) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08190277, -0.9862224, 0.9649197) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9136846, -3.1066911, 5.0953894) + vector3f physics:velocity = (-0.20532943, 2.0989308, 2.552998) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (0.90646315, -0.009445641, 0.0044472115, -0.42215586) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.19057465, -0.98436904, 0.96366453) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2201351, 3.2741237, -7.0647206) + vector3f physics:velocity = (-0.18719472, 2.1986575, 2.707346) + float physxContactReport:threshold = 0 + float physxRigidBody:sleepThreshold = 0 + quatf xformOp:orient = (0.0035469404, 0.4290178, 0.9032459, 0.008828266) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08195478, 0.9841443, 0.9638105) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "base_link" ( + prepend apiSchemas = ["PhysicsArticulationRootAPI", "PhysxArticulationAPI", "PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.10078937, -0.013539686, -0.0018289083) + vector3f physics:velocity = (0.001908283, 0.011097478, 0.06512169) + bool physxArticulation:enabledSelfCollisions = 1 + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, -7.9679694e-7, -0.000053580014, 5.8216045e-7) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.0000102985905, 0.000001629412, 0.0000016242266) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + + over "FixedJoint" + { + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + } + } + + over "body_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.10078937, -0.013539686, -0.0018289083) + vector3f physics:velocity = (-0.0072427024, -0.05767733, 0.17153949) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, -7.962001e-7, -0.000053606313, 5.821619e-7) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-0.00005930915, 0.0000026597704, 0.64927447) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "body_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.28785434, 0.0127281705, -0.0018294448) + vector3f physics:velocity = (-0.0066979276, -0.057571433, 0.12558424) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70710605, 0.7071071, -0.0005278521, -0.00052871) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1309408, 0.0000027128974, 0.6492884) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "head_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.28785545, 0.01271181, -0.012801933) + vector3f physics:velocity = (-0.00019588991, 0.0845249, 0.16830435) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999997, -7.970665e-7, 0.0007470764, -0.000017110924) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13159968, 0.000003397423, 1.0902883) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "head_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.5767391, 0.032247286, -0.01280238) + vector3f physics:velocity = (-0.00073012954, 0.068252176, 0.1519162) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70710516, 0.7071061, -0.0012758086, -0.001251605) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.18192734, 0.0000017069775, 1.1502782) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.28785434, 0.0127281705, -0.0018294448) + vector3f physics:velocity = (-0.0031709508, 0.030194137, 0.18125123) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999997, -7.401507e-7, 0.0007470056, 5.956639e-7) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139646, 0.0000031631746, 0.9542883) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.28785434, 0.0127281705, -0.0018294448) + vector3f physics:velocity = (-0.0031715743, 0.03013333, 0.18092634) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.70710605, 0.70710707, -0.00052774895, -0.0005286129) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139655, 0.025003191, 0.9542882) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.5182588, -0.17693321, -0.0018252725) + vector3f physics:velocity = (-0.0032245829, 0.029477205, 0.17675276) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.7070962, 0.70709723, 0.0037707728, 0.0037701104) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139634, 0.21090314, 0.95428795) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.7294676, 2.9479887, 0.031578362) + vector3f physics:velocity = (-0.00049949705, 0.025637556, 0.41248924) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.004523906, 0.7108525, 0.0045729866, -0.7033117) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139634, 0.21090314, 0.95428795) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.6660265, 3.1770182, 0.075360715) + vector3f physics:velocity = (0.0031397282, 0.013359769, 0.9612875) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7116372, -0.70254135, 0.0019581504, 0.0020817285) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135426, 0.5158779, 0.9582115) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.69406426, 3.166766, -2.0454724) + vector3f physics:velocity = (0.026989773, 0.14001147, 1.3272377) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.000250147, 0.002858845, -0.00643085, 0.9999752) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135423, 0.5158784, 0.95821154) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link5" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (3.2215402, 2.9, -2.0959482) + vector3f physics:velocity = (0.039592873, 0.36480662, 1.6723453) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.71161085, -0.702521, -0.0058570253, -0.0063147624) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13146016, 0.7133618, 0.96075135) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_link6" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (3.1765609, 2.969617, -5.9531665) + vector3f physics:velocity = (0.08303051, 0.93391, 2.027546) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0064992304, 0.9999046, -0.008683743, 0.008552708) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13146015, 0.71336204, 0.9607514) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_l_end_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2063364, 3.2569847, -5.908531) + vector3f physics:velocity = (0.1325492, 1.3797824, 2.2761858) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7117109, -0.7024194, 0.0058462867, -0.0063584833) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13457467, 0.89381963, 0.9631242) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2063364, 3.2569847, -5.908531) + vector3f physics:velocity = (0.1596425, 1.6737729, 2.4372973) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.50775176, -0.49255154, 0.5008195, 0.49875945) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13457479, 0.89381963, 0.96312416) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.18984972, 3.2365067, -4.52711) + vector3f physics:velocity = (0.29993036, 1.8526645, 2.5393565) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9259917, 0.0033365516, -0.0089101745, 0.37742394) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.15504666, 0.9499695, 0.9638788) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.19675523, 3.2450836, -5.1056967) + vector3f physics:velocity = (0.4566467, 1.8851632, 2.5515082) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0030128763, 0.9117686, 0.410594, 0.009024733) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.190072, 0.95826715, 0.9644758) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_inner_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.18663533, 3.232514, -4.25778) + vector3f physics:velocity = (0.4779766, 2.022497, 2.6426136) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.004814277, 0.9769758, 0.21313801, 0.008206427) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1827518, 0.9888325, 0.9647679) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.22284889, 3.2774942, -7.292098) + vector3f physics:velocity = (0.051720854, 1.9058546, 2.5602977) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.3933963, -0.003889245, -0.00868297, 0.91931975) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1160525, 0.950643, 0.96385497) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.21559273, 3.2684817, -6.6841207) + vector3f physics:velocity = (-0.18755297, 1.9820429, 2.6048207) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0035706637, 0.42659095, 0.9043946, 0.008818713) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08133383, 0.9601542, 0.9634923) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_outer_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.22595938, 3.2813575, -7.5527234) + vector3f physics:velocity = (-0.2092008, 2.1961203, 2.704172) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0053190184, 0.23011513, 0.9731169, 0.007888484) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08970425, 0.9904617, 0.9640005) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_l_center_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.2063364, 3.2569847, -5.908531) + vector3f physics:velocity = (0.21894014, 2.320772, 2.7918746) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.7117109, -0.7024194, 0.0058463085, -0.0063585215) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13704354, 1.0368657, 0.9650051) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.28785434, 0.0127281705, -0.0018294448) + vector3f physics:velocity = (-0.0031713308, 0.030251352, 0.1817091) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0005278763, 0.0005286627, -0.7071061, 0.707107) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13139649, -0.024996739, 0.95428836) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.7185743, -0.19047074, -0.0018246764) + vector3f physics:velocity = (-0.0032246308, 0.029448586, 0.17909929) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.004072726, -0.0040718107, -0.70709455, 0.70709556) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1313967, -0.21299691, 0.9542886) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.5091228, -3.289413, -0.03760034) + vector3f physics:velocity = (-0.005438691, 0.025173744, 0.43829793) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.7030083, 0.0045795375, 0.7111525, -0.0045279856) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1313967, -0.2129968, 0.9542887) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.3660618, -3.026919, -0.087628424) + vector3f physics:velocity = (-0.014347001, 0.012380151, 0.95841116) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.0018309937, 0.0017043704, -0.70253646, 0.71164334) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1313518, -0.5179717, 0.9582169) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link4" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.3944309, -3.0136366, 2.1795282) + vector3f physics:velocity = (-0.017976621, 0.14896333, 1.325375) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.99997604, -0.0064383447, 0.002503143, 0.00040608927) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13135192, -0.5179717, 0.95821685) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link5" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.0277734, -3.2420387, 2.2236886) + vector3f physics:velocity = (0.0008052413, 0.3881169, 1.6691731) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0050857207, -0.0044274223, -0.70252633, 0.71162575) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13151866, -0.71545535, 0.9607596) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_link6" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (1.9763619, -3.289038, 5.9647894) + vector3f physics:velocity = (0.034594342, 0.9609613, 2.0203445) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.006673786, -0.008656725, 0.9999192, -0.0064891283) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13151866, -0.7154554, 0.9607595) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "arm_r_end_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9237174, -3.0928571, 5.9136586) + vector3f physics:velocity = (0.044925123, 1.4134417, 2.2656174) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.0050208247, 0.0071461718, -0.70242625, 0.71170294) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1346277, -0.8959131, 0.9631228) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_base_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9237174, -3.0928571, 5.9136586) + vector3f physics:velocity = (0.05944782, 1.7088145, 2.4248223) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.49969977, 0.50174344, -0.4916372, 0.5068003) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.1346277, -0.8959131, 0.9631227) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9068284, -3.1161456, 4.536151) + vector3f physics:velocity = (-0.055582177, 1.9057515, 2.4985564) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.009217795, 0.9259519, -0.37749717, -0.004902094) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.15509799, -0.95206475, 0.9638017) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9136858, -3.1066895, 5.0954833) + vector3f physics:velocity = (-0.2046849, 1.9579928, 2.4673102) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9136927, -0.009366562, 0.0046113697, -0.40627164) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.19012263, -0.9603752, 0.963351) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_inner_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9034665, -3.1207817, 4.2619386) + vector3f physics:velocity = (-0.2085173, 2.0973926, 2.5527375) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9775162, -0.00822406, 0.006431321, -0.2106018) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.18292205, -0.99080294, 0.9638733) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9406514, -3.0695062, 7.2948713) + vector3f physics:velocity = (0.19699165, 1.9253321, 2.5732172) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.01000208, -0.39326215, 0.91936713, -0.0029926337) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.11610387, -0.95273507, 0.96392334) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link3" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9331764, -3.0798137, 6.6851716) + vector3f physics:velocity = (0.44127727, 1.9701161, 2.6601963) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.42454612, 0.010098766, 0.002648053, 0.9053461) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08138549, -0.9622298, 0.9646071) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_outer_link2" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9438356, -3.0651155, 7.554586) + vector3f physics:velocity = (0.48944694, 2.1800914, 2.763028) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.2288221, 0.009336114, 0.004672678, 0.9734123) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.08969876, -0.992485, 0.9648783) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gripper_r_center_link" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-1.9237174, -3.0928571, 5.9136586) + vector3f physics:velocity = (0.087127976, 2.3606315, 2.7747283) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.71170306, 0.70242614, 0.0071462095, 0.0050208312) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (0.13709235, -1.0389596, 0.96499604) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "joints" + { + over "idx41_gripper_l_outer_joint1" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:maxForce = 80 + float state:angular:physics:position = 44.65972 + float state:angular:physics:velocity = -79.28686 + } + + over "idx81_gripper_r_outer_joint1" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:maxForce = 80 + float state:angular:physics:position = 44.663033 + float state:angular:physics:velocity = -79.154915 + } + + over "idx01_body_joint1" + { + float state:linear:physics:position = 0.0007723953 + float state:linear:physics:velocity = 0.10160032 + } + + over "idx02_body_joint2" + { + float state:angular:physics:position = 0.09175628 + float state:angular:physics:velocity = 22.318443 + } + + over "idx11_head_joint1" + { + float state:angular:physics:position = -0.0020183783 + float state:angular:physics:velocity = -0.62867796 + } + + over "idx12_head_joint2" + { + float state:angular:physics:position = 0.11917975 + float state:angular:physics:velocity = 16.589615 + } + + over "idx21_arm_l_joint1" + { + float state:angular:physics:position = -0.696631 + float state:angular:physics:velocity = -161.14525 + } + + over "idx61_arm_r_joint1" + { + float state:angular:physics:position = 0.74554384 + float state:angular:physics:velocity = 172.64867 + } + + over "idx22_arm_l_joint2" + { + float state:angular:physics:position = -0.73718905 + float state:angular:physics:velocity = -179.46353 + } + + over "idx62_arm_r_joint2" + { + float state:angular:physics:position = 0.7379124 + float state:angular:physics:velocity = 177.97322 + } + + over "idx23_arm_l_joint3" + { + float state:angular:physics:position = 0.9383792 + float state:angular:physics:velocity = 195.00566 + } + + over "idx63_arm_r_joint3" + { + float state:angular:physics:position = -0.94640094 + float state:angular:physics:velocity = -222.55899 + } + + over "idx24_arm_l_joint4" + { + float state:angular:physics:position = -0.03866377 + float state:angular:physics:velocity = -121.52681 + } + + over "idx64_arm_r_joint4" + { + float state:angular:physics:position = 0.0567978 + float state:angular:physics:velocity = 129.91089 + } + + over "idx25_arm_l_joint5" + { + float state:angular:physics:position = -1.3138971 + float state:angular:physics:velocity = -224.88623 + } + + over "idx65_arm_r_joint5" + { + float state:angular:physics:position = 1.0577039 + float state:angular:physics:velocity = 196.53036 + } + + over "idx26_arm_l_joint6" + { + float state:angular:physics:position = 0.9580435 + float state:angular:physics:velocity = 221.05334 + } + + over "idx66_arm_r_joint6" + { + float state:angular:physics:position = -0.9386987 + float state:angular:physics:velocity = -214.38643 + } + + over "idx27_arm_l_joint7" + { + float state:angular:physics:position = 0.9386274 + float state:angular:physics:velocity = 194.54059 + } + + over "idx67_arm_r_joint7" + { + float state:angular:physics:position = -0.9370274 + float state:angular:physics:velocity = -223.75977 + } + + over "idx31_gripper_l_inner_joint1" + { + float state:angular:physics:position = -44.655563 + float state:angular:physics:velocity = 79.16392 + } + + over "idx71_gripper_r_inner_joint1" + { + float state:angular:physics:position = -44.659 + float state:angular:physics:velocity = 78.9426 + } + + over "idx32_gripper_l_inner_joint3" + { + float state:angular:physics:position = -4.1361327 + float state:angular:physics:velocity = 33.156578 + } + + over "idx42_gripper_l_outer_joint3" + { + float state:angular:physics:position = 4.1710653 + float state:angular:physics:velocity = -34.84085 + } + + over "idx72_gripper_r_inner_joint3" + { + float state:angular:physics:position = -3.5844445 + float state:angular:physics:velocity = 32.054382 + } + + over "idx82_gripper_r_outer_joint3" + { + float state:angular:physics:position = 3.9288678 + float state:angular:physics:velocity = -34.940845 + } + + over "idx33_gripper_l_inner_joint4" + { + float state:angular:physics:position = -0.4564569 + float state:angular:physics:velocity = -4.7259345 + } + + over "idx43_gripper_l_outer_joint4" + { + float state:angular:physics:position = 0.30768842 + float state:angular:physics:velocity = 21.810728 + } + + over "idx73_gripper_r_inner_joint4" + { + float state:angular:physics:position = -1.9999992 + float state:angular:physics:velocity = -0.0053762887 + } + + over "idx83_gripper_r_outer_joint4" + { + float state:angular:physics:position = 0.98035777 + float state:angular:physics:velocity = 21.050102 + } + + over "idx39_gripper_l_inner_joint0" + { + float state:angular:physics:position = 24.329346 + float state:angular:physics:velocity = -43.86491 + } + + over "idx49_gripper_l_outer_joint0" + { + float state:angular:physics:position = -24.20402 + float state:angular:physics:velocity = 27.965567 + } + + over "idx79_gripper_r_inner_joint0" + { + float state:angular:physics:position = 25.627863 + float state:angular:physics:velocity = -47.76362 + } + + over "idx89_gripper_r_outer_joint0" + { + float state:angular:physics:position = -24.770216 + float state:angular:physics:velocity = 28.774542 + } + } + } + + over "PhysicsScene" ( + prepend apiSchemas = ["PhysxSceneAPI"] + ) + { + vector3f physics:gravityDirection = (0, 0, -1) + float physics:gravityMagnitude = 9.81 + uniform token physxScene:broadphaseType = "MBP" + bool physxScene:enableCCD = 1 + bool physxScene:enableGPUDynamics = 0 + bool physxScene:enableStabilization = 0 + uniform token physxScene:solverType = "TGS" + uint physxScene:timeStepsPerSecond = 240 + } + + over "Assets" + { + over "LivingRoomRug" + { + over "Surface" + { + bool physics:collisionEnabled = 0 + } + + over "OuterBorder" + { + over "West" + { + bool physics:collisionEnabled = 0 + } + + over "East" + { + bool physics:collisionEnabled = 0 + } + + over "South" + { + bool physics:collisionEnabled = 0 + } + + over "North" + { + bool physics:collisionEnabled = 0 + } + } + + over "InnerBorder" + { + over "West" + { + bool physics:collisionEnabled = 0 + } + + over "East" + { + bool physics:collisionEnabled = 0 + } + + over "South" + { + bool physics:collisionEnabled = 0 + } + + over "North" + { + bool physics:collisionEnabled = 0 + } + } + } + + over "ShelfItems" + { + over "cayman_jacked" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.0000052003384, -0.00000349291, -6.755202e-10) + vector3f physics:velocity = (1.16486305e-7, 1.7342794e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.99118716, 1.1727347e-8, -5.7896994e-9, -0.13246875) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.6646488, 1.51194, 1.1830254) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gatorade_m" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0, 0, 0) + vector3f physics:velocity = (0, 0, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.02470328, 9.0949465e-13, 3.4106049e-13, 0.9996948) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.8248767, 1.5659721, 1.182829) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "gatorade_t" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.2315566e-8, 1.2435407e-9, -3.378904e-8) + vector3f physics:velocity = (-4.134788e-11, 7.4167167e-10, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.76918566, 3.774403e-11, -2.8194336e-11, 0.63902533) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.4137124, 1.6525209, 1.1828291) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "starbucks" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0, 0, 0) + vector3f physics:velocity = (0, 0, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9946376, 7.275957e-12, -1.09139356e-11, -0.10342177) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-2.0479352, 1.4228084, 1.182829) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "good2grow" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.00014572211, -0.0011574546, 0.000020528547) + vector3f physics:velocity = (0.00003419478, -0.0000043071445, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.75207967, 2.2707354e-8, 6.070516e-8, -0.6590722) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.512102, 1.4741168, 1.182829) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "grapefruit_can" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.000023589548, 0.00011312748, 0.00004930749) + vector3f physics:velocity = (-0.0000027907859, 5.8192086e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (-0.21244383, -5.764742e-7, 2.9930476e-7, 0.97717327) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.2317066, 1.4470603, 1.1829599) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "sprite_c1" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0, 0, 0) + vector3f physics:velocity = (0, 0, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.89388204, 0, 0, -0.4483023) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.9897239, 1.8016589, 1.1829599) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "good2grow_copy_01" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.000031946645, -0.00069270184, -0.00028477138) + vector3f physics:velocity = (0.000020466245, -9.439971e-7, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9994097, 2.0944997e-8, -5.764973e-7, -0.034355517) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.2478863, 1.6789885, 1.182829) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + + over "Tier2Items" + { + over "chomps_r" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.0001196792, -0.0005928937, -0.00065619114) + vector3f physics:velocity = (0.000023383756, 0.0000030527485, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.85485816, -1.3096724e-8, 1.4532297e-8, -0.5188617) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.4839939, 1.5671718, 0.7513308) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "pring_bbq" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (2.6915314e-9, 8.59411e-9, -6.7940414e-7) + vector3f physics:velocity = (-3.6237102e-10, 1.1670243e-10, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.9999301, -4.4628905e-9, 3.7022528e-7, -0.011825219) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.8196323, 1.6120286, 0.75133073) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "honeymaid" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (6.514489e-8, -3.3521474e-8, -1.3626133e-7) + vector3f physics:velocity = (1.8890205e-9, 3.2059688e-9, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.92266625, -2.2459594e-7, -4.7065564e-7, 0.38559955) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.1940291, 1.550271, 0.7513308) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + + over "skit_littles" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (-0.000019520527, -0.00026890315, 0.00013474934) + vector3f physics:velocity = (0.000014438246, -0.0000015765213, -0.040875003) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (0.09396095, 4.1245943e-7, 4.8384514e-8, 0.9955759) + float3 xformOp:scale = (1, 1, 1) + float3 xformOp:translate = (-1.7485627, 1.7480879, 0.75133073) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + + over "DiningTableMicrowave" + { + over "Asset" + { + over "body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (1.3889053e-15, -3.536939e-17, -1.2938907e-8) + vector3f physics:velocity = (2.331888e-10, -2.5891493e-9, -5.3990647e-15) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, -3.2128558e-9, -5.390645e-15, -2.5608815e-8) + float3 xformOp:translate = (1.6689302e-8, 1.1239623e-8, 1.66893e-8) + } + } + } + + over "AirFryer" + { + over "Centered" + { + over "Body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "Tray" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + vector3f physics:angularVelocity = (0.00081485644, 0.000008714815, 0.000002779852) + vector3f physics:velocity = (-1.5506156e-9, -0.000013794832, -0.000079827296) + float physxContactReport:threshold = 0 + quatf xformOp:orient = (1, 9.237145e-11, -1.4197492e-7, -6.700795e-8) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (-3.0313110244151176e-8, -1.0967254127081105e-7, 3.067962950353831e-7) + token[] xformOpOrder = ["xformOp:translate", "xformOp:orient", "xformOp:scale"] + } + } + } + + over "Tray" ( + prepend apiSchemas = ["MaterialBindingAPI"] + ) + { + rel material:binding = ( + bindMaterialAs = "strongerThanDescendants" + ) + } + } + + def PhysicsRevoluteJoint "ChainNavigation_turn" ( + prepend apiSchemas = ["PhysicsDriveAPI:angular"] + ) + { + float drive:angular:physics:damping = 100000 + float drive:angular:physics:maxForce = 100000 + float drive:angular:physics:stiffness = 0 + float drive:angular:physics:targetVelocity = 0 + uniform token drive:angular:physics:type = "force" + uniform token physics:axis = "Z" + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + float physics:lowerLimit = -170 + float physics:upperLimit = 170 + } + + def PhysicsPrismaticJoint "ChainNavigation_drive" ( + prepend apiSchemas = ["PhysicsDriveAPI:linear"] + ) + { + float drive:linear:physics:damping = 100000 + float drive:linear:physics:maxForce = 100000 + float drive:linear:physics:stiffness = 0 + float drive:linear:physics:targetVelocity = 0 + uniform token drive:linear:physics:type = "force" + uniform token physics:axis = "X" + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + float physics:lowerLimit = -15 + float physics:upperLimit = 15 + } + + def PhysicsFixedJoint "ChainNavigation_hold" + { + rel physics:body1 = + uniform bool physics:excludeFromArticulation = 1 + bool physics:jointEnabled = 0 + point3f physics:localPos0 = (0, 0, 0) + point3f physics:localPos1 = (0, 0, 0) + quatf physics:localRot0 = (1, 0, 0, 0) + quatf physics:localRot1 = (1, 0, 0, 0) + } + + over "Background" + { + over "Shell" + { + over "KitchenRefrigerator" + { + over "body" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "left_door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + + over "right_door" ( + prepend apiSchemas = ["PhysxContactReportAPI"] + ) + { + float physxContactReport:threshold = 0 + } + } + } + } + + over "Looks" + { + def Material "ChainOriginalTray" + { + token outputs:surface.connect = + + def Shader "Surface" + { + uniform token info:id = "UsdPreviewSurface" + color3f inputs:diffuseColor = (0.14, 0.14, 0.14) + float inputs:roughness = 0.5 + token outputs:surface + } + } + } +} + +over "Render" ( + no_delete = true +) +{ + over "OmniverseKit" + { + over "HydraTextures" ( + hide_in_stage_window = true + no_delete = true + ) + { + over "omni_kit_widget_viewport_ViewportTexture_0" ( + hide_in_stage_window = true + no_delete = true + ) + { + rel camera = + bool omni:rtx:rt:ecoMode:enabled = 0 + rel orderedVars = + custom bool overrideClipRange = 0 + uniform int2 resolution = (1280, 720) + } + } + } +} + +over "OmniverseKit_Persp" +{ + float3 xformOp:rotateXYZ = (150.20723, -1.8883514, 90) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (1.8494795488078601, 3.324996692835411, 0.023089290376739388) + uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"] +} + +over "OmniverseKit_Front" +{ + float3 xformOp:rotateXYZ = (0, -90, -90) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (0, 5, 0) + uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"] +} + +over "OmniverseKit_Right" +{ + float3 xformOp:rotateXYZ = (180, -7.7606565e-15, 90) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (0, 0, -5) + uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"] +} + +over "OmniverseKit_Top" +{ + float3 xformOp:rotateXYZ = (0, 90, 1.2722219e-14) + float3 xformOp:scale = (1, 1, 1) + double3 xformOp:translate = (5, 0, 0) + uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"] +} + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene_physical.usda b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene_physical.usda new file mode 100644 index 0000000000000000000000000000000000000000..fed3b1f07d2ea34772a983d5a357c411b141bc8e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/scene_physical.usda @@ -0,0 +1,8 @@ +#usda 1.0 +( + subLayers = [ + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/collision_profile.usda@, + @/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/scene.usda@ + ] +) + diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/sim_info.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/sim_info.json new file mode 100644 index 0000000000000000000000000000000000000000..49e54d1e0eb4f7a6433dba336997333f37fd363c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/sim_info.json @@ -0,0 +1,202 @@ +{ + "action_schema": "g1_omnipicker_jointpos20_basevw_v1", + "physics_hz": 240, + "control_hz": 60, + "training_hz": 20, + "action_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_forward_m_s", + "base_yaw_rate_rad_s" + ], + "state_names": [ + "idx21_arm_l_joint1", + "idx22_arm_l_joint2", + "idx23_arm_l_joint3", + "idx24_arm_l_joint4", + "idx25_arm_l_joint5", + "idx26_arm_l_joint6", + "idx27_arm_l_joint7", + "idx61_arm_r_joint1", + "idx62_arm_r_joint2", + "idx63_arm_r_joint3", + "idx64_arm_r_joint4", + "idx65_arm_r_joint5", + "idx66_arm_r_joint6", + "idx67_arm_r_joint7", + "idx01_body_joint1", + "idx02_body_joint2", + "idx11_head_joint1", + "idx12_head_joint2", + "idx41_gripper_l_outer_joint1", + "idx81_gripper_r_outer_joint1", + "base_x_world_m", + "base_y_world_m", + "base_yaw_world_unwrapped_rad" + ], + "phase_names": [ + "initial_upper_shelf_posture", + "item_00_waist_to_selected_shelf", + "item_00_shelf_pick", + "item_00_shelf_exit_reverse", + "item_00_carry_raise", + "item_00_carry_compact", + "item_00_forward_navigation", + "item_00_bridge_to_place_initial", + "item_00_place_descent", + "item_00_place_release", + "item_00_release_retreat", + "item_00_settle", + "item_00_empty_return", + "item_00_complete", + "item_01_waist_to_selected_shelf", + "item_01_shelf_pick", + "item_01_shelf_exit_reverse", + "item_01_carry_raise", + "item_01_carry_compact", + "item_01_forward_navigation", + "item_01_bridge_to_place_initial", + "item_01_place_descent", + "item_01_place_release", + "item_01_release_retreat", + "item_01_settle", + "item_01_empty_return", + "item_01_complete", + "item_02_waist_to_selected_shelf", + "item_02_shelf_pick", + "item_02_shelf_exit_reverse", + "item_02_carry_raise", + "item_02_carry_compact", + "item_02_forward_navigation", + "item_02_bridge_to_place_initial", + "item_02_place_descent", + "item_02_place_release", + "item_02_release_retreat", + "item_02_settle", + "item_02_complete" + ], + "item_index_semantics": "0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase", + "requested_items": [ + { + "item_index": 0, + "target": "/World/Assets/ShelfItems/cayman_jacked", + "product": "cayman_jacked", + "hand": "l", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000225", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_01.npz", + "destination_xyz": [ + -1.2355652560064982, + 0.44574632054857005, + 0.45490211957462423 + ] + }, + { + "item_index": 1, + "target": "/World/Assets/Tier2Items/chomps_r", + "product": "chomps_r", + "hand": "r", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000375", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_02.npz", + "destination_xyz": [ + -1.3093583385662146, + 0.47860329798616896, + 0.45490211957462423 + ] + }, + { + "item_index": 2, + "target": "/World/Assets/Tier2Items/pring_bbq", + "product": "pring_bbq", + "hand": "l", + "pick_source": "/mnt/agent-bench-data/lingjun/ziche/data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000605", + "controls_file": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_08/controls_item_03.npz", + "destination_xyz": [ + -1.1244946994405842, + 0.47150154262367727, + 0.45490211957462423 + ] + } + ], + "initial_tray_items": 0, + "continuous_native_episode": true, + "action_units": [ + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "rad", + "rad", + "rad", + "rad", + "rad", + "m/s", + "rad/s" + ], + "state_units": [ + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "rad", + "rad", + "rad", + "rad", + "rad", + "m", + "m", + "rad" + ], + "normalization": null, + "time_semantics": "same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll", + "unrecorded_preroll_duration_s": 0.6333333333333333, + "image_generation_mode": "native_live_three_view", + "manual_visual_review_required": true, + "user_visual_acceptance_record": null, + "candidate_export_policy": "CANDIDATE_EXPORT_POLICY.json", + "gripper_max_effort_Nm": { + "left": 80, + "right": 80 + }, + "base_policy": "stationary_turn_or_straight_body_forward_v1", + "source_layout": "/mnt/agent-bench-data/lingjun/releases/living50-candidate-20260919/layouts/ID_30_LIVING_STRAIGHT.usdc" +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_geometry.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..3084090fcc78ffc9efe2c5090ff11c5b0b8bd72a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_geometry.py @@ -0,0 +1,118 @@ +"""Use existing exact/native sampled collision gates for this continuous task.""" +import sys,json,os,subprocess +from pathlib import Path +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]).resolve();out=case/'rollout';kind=sys.argv[2] +if kind=='world' and 'CHAIN_AUDIT_SHARD' not in os.environ: + sys.exit(subprocess.call([sys.executable,str(W/'parallel_world_audit.py'),str(case)])) +shard=int(os.environ.get('CHAIN_AUDIT_SHARD','-1')) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop'),str(R/'work/place_training_tray_20260918')] +job=json.loads((case/'job.json').read_text()) +from multi_validation import items +requested=items(job);requested_targets=[row['target'] for row in requested] +# Optional contemporary cooking representations refine conservative envelopes. +# All actual geometry, filters and tolerances remain unchanged. +import collision_scene as cs +cs.STOCK=list(dict.fromkeys(cs.STOCK+job.get('clutter',[])+[path for row in requested for path in row.get('clutter',[])])) +adapter=None +if (out/'collision_readback/RESULT.json').exists(): + import importlib.util,types,numpy as np + from pxr import Usd + path=R/'work/omni_pick_stage1_20260914/native_cooking_readback/nonrobot_coverage/analysis_extension/adapter_extra.py' + spec=importlib.util.spec_from_file_location('chain_captured_geometry',path);module=importlib.util.module_from_spec(spec) + # This task stores scene/readbacks under case/rollout, with runtime root in case. + # Validate exact same-case paths instead of the legacy directory nesting. + source=path.read_text() + legacy="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve().parent!=snapshot.resolve().parent or snapshot.resolve().parent!=self.readback.parent.parent:raise GeometryReview('Readbacks and snapshot belong to different capture directories')" + current="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve()!=_chain_runtime_root or snapshot.resolve()!=_chain_snapshot or self.readback.parent!=_chain_output or self.native_readback.parent!=_chain_output:raise GeometryReview('Readbacks and snapshot do not match this chain capture')" + assert source.count(legacy)==1 + module.__dict__.update(_chain_runtime_root=Path(job['scene']).resolve(),_chain_snapshot=(out/'scene.usda').resolve(),_chain_output=out.resolve()) + exec(compile(source.replace(legacy,current),str(path),'exec'),module.__dict__) + from collision_inventory import enabled_nonrobot_convex_colliders + module.export.enabled_nonrobot_convex_colliders=enabled_nonrobot_convex_colliders + original_normalize=module.normalize_rigid_mount + def normalize_roundoff(prim,current,captured,cache): + try:return original_normalize(prim,current,captured,cache) + except module.GeometryReview: + before=np.asarray(current['collider_to_rigid_owner_column_matrix'],float);after=np.asarray(captured['collider_to_rigid_owner_column_matrix'],float);points=np.asarray(prim.GetAttribute('points').Get(),float) + if points.ndim!=2 or points.shape[1]!=3:raise + coefficient=float(np.max(abs(before-after)));displacement=float(np.linalg.norm((np.c_[points,np.ones(len(points))]@(before-after).T)[:,:3],axis=1).max()) + if coefficient>1e-10 or displacement>1e-9:raise + return dict(method='bounded_float64_matrix_chain_roundoff',normalized_mount=before.tolist(),raw_end_mount=after.tolist(),raw_end_mount_max_coefficient_difference=coefficient,maximum_geometry_displacement_m=displacement,coefficient_bound=1e-10,geometry_displacement_bound_m=1e-9,normalized_model_is_live_PxMeshScale_identity=False,source_geometry_modified=False) + module.normalize_rigid_mount=normalize_roundoff + stage=Usd.Stage.Open(str(out/'scene.usda'));metadata=np.load(out/'full_state.npz',allow_pickle=False) + wrapped,AnalysisBody,adapter=module.build_extended_adapter(stage,out/'collision_readback',out/'collision_readback_nonrobot',metadata) + (out/('COOKED_ADAPTER_COVERAGE.json' if shard<0 else f'COOKED_ADAPTER_COVERAGE.part_{shard}.json')).write_text(json.dumps(adapter.receipt,indent=2)+'\n') + cs.collider_mesh=wrapped;cs.static_collision_mesh=wrapped;cs.Body=AnalysisBody + +if kind=='self': + import omni_native_audit as native + if adapter is not None:native.trimesh=types.SimpleNamespace(util=types.SimpleNamespace(concatenate=adapter.combine)) + native.audit(out/'physics_ticks/full_state.npz',out/'scene.usda',out/'SELF_COLLISION_AUDIT.json') +elif kind=='world': + item_index=int(os.environ['CHAIN_AUDIT_ITEM']);item=requested[item_index] + audit_target=item['target'];audit_hand=item['hand'] + import collision_initial_layout + from place_tray_layout import scene_geometry + from shapely.geometry import Polygon + original_surfaces=collision_initial_layout._surfaces + def place_surfaces(scene): + surfaces=original_surfaces(scene);geometry=scene_geometry(scene.stage,audit_target);top=float(geometry['tray_xyz'][2]+geometry['tray_floor_z']) + assert abs(top-job['tray_floor_z'])<1e-5 + surfaces.append(dict(top=top,polygon=Polygon(geometry['tray_floor']+geometry['tray_xyz'][:2]),paths=['/World/Assets/Tray']));return surfaces + collision_initial_layout._surfaces=place_surfaces + original_support=cs.is_shelf_support_body + cs.is_shelf_support_body=lambda body:original_support(body) or body.name.startswith('/World/Assets/Tray/') + import collision_sweep as sweep + from contact_policy import install + spec=dict(target=dict(prim_path=audit_target),hand=audit_hand) + import collision_scene as cs + original_static=cs.include_static_collision_geometry + def static_inventory(scene): + result=original_static(scene);templates=[];unresolved=[] + for path in result['undefined_active_enabled_static_colliders']: + prim=scene.stage.GetPrimAtPath(path) + if path.startswith('/Flattened_Prototype_') and not prim.IsDefined():templates.append(path) + else:unresolved.append(path) + result['nonphysical_undefined_template_opinions']=templates;result['undefined_active_enabled_static_colliders']=unresolved;result['complete']=not unresolved + return result + cs.include_static_collision_geometry=static_inventory + (out/f'place_metrics.part_{shard}.json').write_text(json.dumps(dict(item_index=item_index,target=audit_target,hand=audit_hand,destination_body_path='/World/Assets/Tray'))) + import inspect,time + install(sweep,spec) + import collision_acceptance + from task_collision_scope import install as install_task_scope + from task_collision_scope import nonphysical_rug_paths + omitted_rug_paths=set(nonphysical_rug_paths(stage)) + install_task_scope(collision_acceptance,requested_targets,omitted_rug_paths) + source=inspect.getsource(sweep.episode) + target_line=" target_name=str(d['target_prim']); source=_SCENE.objects.get(target_name)" + assert source.count(target_line)==1 + source=source.replace(target_line," target_name=_chain_target; source=_SCENE.objects.get(target_name)") + hand_line=" hand='l'" + assert source.count(hand_line)==1 + source=source.replace(hand_line," hand=_chain_hand",1) + support_line=" support_name=place_metadata.get('destination_body_path') or (str(d['current_furniture_body_path']) if 'current_furniture_body_path' in d else '')" + assert source.count(support_line)==1 + source=source.replace(support_line," hand=_chain_hand\n"+support_line) + begin=source.index(' def register(');end=source.index(' frame_ids=',begin) + register=source[begin:end].replace('_OPTIONS.contact_tolerance_mm','contact_limit') + register=register.replace(' nonlocal exact,cache_hits'," nonlocal exact,cache_hits\n if a.name.split('#')[0] in _chain_nonphysical_rug or b.name.split('#')[0] in _chain_nonphysical_rug:return") + sweep.__dict__['_chain_nonphysical_rug']=omitted_rug_paths + marker_limit=" if contact_limit is not None and row['category'].endswith('_requires_depth_review'):" + register=register.replace(marker_limit," contact_limit=_place_contact_limit(row,_OPTIONS.contact_tolerance_mm)\n"+marker_limit) + source=source[:begin]+register+source[end:] + marker=' for fi,f in enumerate(frame_ids):' + source=source.replace(marker,marker+"\n if not _chain_start<=fi<_chain_end:continue\n _chain_checked.append(fi)\n if fi%30==0:_chain_audit_progress(fi,len(frame_ids))") + def progress(frame,total): + path=out/f'WORLD_AUDIT_PROGRESS.part_{shard}.json';tmp=path.with_suffix('.tmp');tmp.write_text(json.dumps(dict(frame=frame,total=total,updated=time.time())));tmp.replace(path) + sweep.__dict__.update(_chain_audit_progress=progress,_chain_start=int(os.environ['CHAIN_AUDIT_START']),_chain_end=int(os.environ['CHAIN_AUDIT_END']),_chain_checked=[],_chain_target=audit_target,_chain_hand=audit_hand) + exec(compile(source,sweep.__file__,'exec'),sweep.__dict__) + from types import SimpleNamespace + from collision_scene import ROBOT + options=SimpleNamespace(scene=str(out/'scene.usda'),robot=str(ROBOT),output=str(out/'world_geometry'/f'part_{shard}'),scene_per_episode=False,authored_active=True,native_robot_world=True,swept=True,all_scene=False,solid_containment=True,stop_on_forbidden=True,depth=True,depth_points=512,contact_tolerance_mm=.5,stride=1,joint_step=.03,target_step=.005) + sweep.init(options);report=sweep.episode(out/'physics_ticks') + from collision_acceptance import summarize + full_report=json.loads(Path(report['output']).read_text());result=full_report['native_world_geometry_summary'] + result.update(partial_report=True,shard=shard,item_index=item_index,active_target=audit_target,active_hand=audit_hand,checked_frame_indices=sweep.__dict__['_chain_checked'],start=int(os.environ['CHAIN_AUDIT_START']),end=int(os.environ['CHAIN_AUDIT_END']),elapsed_seconds=full_report['elapsed_seconds'],source_geometry_report=report['output']) + (out/f'WORLD_COLLISION_AUDIT.part_{shard}.json').write_text(json.dumps(result,indent=2)+'\n') +else:raise ValueError(kind) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_views.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_views.py new file mode 100644 index 0000000000000000000000000000000000000000..805f409f02d042aa070dd0c8760091a2dfb62568 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/audit_views.py @@ -0,0 +1,54 @@ +"""Actual-pose wrist and initial selected-shelf visibility checks.""" +import sys,json,copy +from pathlib import Path +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation as Rot +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]);out=case/'rollout';job=json.loads((case/'job.json').read_text()) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/shelf_training_visible360_20260916'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +from wrist_upright import camera_mount +from wrist_orientation_policy import MIN_CAMERA_HEIGHT_M,MIN_CAMERA_UP_Z +from validate_export import write +from multi_validation import items,recording_contract +requested=items(job);z=np.load(out/'recording_60hz.npz');phases,item_index,_,_=recording_contract(z,requested);phases=np.asarray(phases) +stage=Usd.Stage.Open(str(out/'scene.usda'));paths=z['robot_link_paths'].tolist();wrist={};extrinsics={};samples=np.arange(2,len(z['q']),3) +for key,owner,cam in [('top_head','head_link2','Head_Camera'),('hand_left','gripper_l_base_link','Left_Camera'),('hand_right','gripper_r_base_link','Right_Camera')]: + prim=stage.GetPrimAtPath('/World/Robot/'+owner);camera=stage.GetPrimAtPath(str(prim.GetPath())+'/'+cam);cache=UsdGeom.XformCache();mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(prim)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + p=z['robot_link_pose'][:,paths.index(str(prim.GetPath()))];rotation=Rot.from_quat(p[:,3:]).as_matrix();world=np.tile(np.eye(4),(len(p),1,1));world[:,:3,:3]=rotation;world[:,:3,3]=p[:,:3];camera_world=world@mount;extrinsics[key]=camera_world[samples] + if key!='top_head': + height=(rotation@mount[:3,3])[:,2];up=(rotation@mount[:3,1])[:,2];wrist[key]=dict(passed=bool(height.min()>=MIN_CAMERA_HEIGHT_M and up.min()>=MIN_CAMERA_UP_Z),minimum_height_m=float(height.min()),minimum_up_z=float(up.min()),minimum_height_tick=int(height.argmin()+1),minimum_up_tick=int(up.argmin()+1)) +write(out/'WRIST_AUDIT.json',dict(passed=all(d['passed'] for d in wrist.values()),policy='wrist_camera_upright_v2',hands=wrist,actual_samples=len(z['q']))) +np.savez_compressed(out/'camera_extrinsics_reconstructed_by_view_audit.npz',**extrinsics,source_control_tick=samples+1) +# At the upper default pose a lower label can be out of view by design. Verify +# every requested item after its measured waist adjustment and before grasping. +d=np.load(out/'full_state.npz') +import collision_scene as cs,trimesh +from omni_randomization import full_environment_bodies +from visible_target_check import TargetCheck +robot=cs.Robot(cs.ROBOT);cam=UsdGeom.Camera(stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera'));head_index=paths.index('/World/Robot/head_link2');props=d['rigid_body_paths'].tolist();reports=[] +# The multi-item scene retains a deliberate subset of the distractors from +# several single-item source episodes. Reconstruct visibility from the actual +# captured rigid-body inventory, using source rows only as asset metadata. +object_catalog={} +for request in requested: + for source in json.loads((Path(request['pick_source'])/'episode.json').read_text())['objects']: + object_catalog.setdefault(source['prim_path'],source) +for item_number,item in enumerate(requested): + phase=f'item_{item_number:02d}_waist_to_selected_shelf';found=np.flatnonzero((item_index==item_number)&(phases==phase)) + if not len(found): + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Missing measured waist-to-selected-shelf phase'));continue + frame=int(found[-1]);spec=json.loads((Path(item['pick_source'])/'episode.json').read_text());base=cs.pose(z['base_pose'][frame,:3],z['base_pose'][frame,[6,3,4,5]]) + check=TargetCheck.__new__(TargetCheck);check.stage=stage;check.robot=robot;check.r=dict(spec['robot'],dof_names=z['dof_names'].tolist());check.base=base;check.fixtures=full_environment_bodies(stage);check.camera=cam;check.focal=float(cam.GetFocalLengthAttr().Get());check.ha=float(cam.GetHorizontalApertureAttr().Get());check.va=float(cam.GetVerticalApertureAttr().Get()) + parent=z['robot_link_pose'][frame,head_index];camera=cs.pose(parent[:3],parent[[6,3,4,5]])@cs.matrix(UsdGeom.Xformable(cam).GetLocalTransformation());placed=[];target=None;cache=UsdGeom.XformCache() + current_catalog={source['prim_path']:source for source in spec['objects']} + for prop_index,path in enumerate(props): + source=current_catalog.get(path,object_catalog.get(path)) + if source is None:continue # articulated/static appliances are fixtures + row=copy.deepcopy(source);row['xyz_m']=d['prop_xyz_m'][frame,prop_index].tolist();row['wxyz']=d['prop_wxyz'][frame,prop_index].tolist();prim=stage.GetPrimAtPath(path);ref=cs.rigid_part(cs.matrix(cache.GetLocalToWorldTransform(prim)));parts=cs.mesh_parts(prim,cache,reference=ref);body=cs.Body(path,trimesh.util.concatenate([m for _,m in parts]),cs.pose(row['xyz_m'],row['wxyz']));placed.append((row,[body],None)) + if path==item['target']:target=row + if target is None: + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Target absent from captured rigid-body inventory'));continue + posture=dict(q=z['q'][frame],camera=camera,actual_links={p.replace('/World/Robot',robot.root,1):cs.pose(z['robot_link_pose'][frame,i,:3],z['robot_link_pose'][frame,i,[6,3,4,5]]) for i,p in enumerate(paths)}) + report=check.visibility(target,placed,posture,actual_camera=camera);report.update(item_index=item_number,target=item['target'],frame=frame,source_control_tick=frame+1,phase=phase,source='Actual measured poses immediately before this item pickup in the same uninterrupted episode') + reports.append(report) +write(out/'VISIBILITY_AUDIT.json',dict(passed=all(row.get('passed') for row in reports),items=reports,actual_item_count=len(reports))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/candidate_qualification.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/candidate_qualification.py new file mode 100644 index 0000000000000000000000000000000000000000..bde57040c6ebf56e3c9506c6abdfc5194ec45a37 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/candidate_qualification.py @@ -0,0 +1,26 @@ +"""Candidate eligibility preserves strict failures and case-specific reviewed deviations.""" +from pathlib import Path +import json,hashlib + +def qualification(case): + case=Path(case);path=case/'rollout/TASK_SCHEMA_AUDIT.json' + audit=json.loads(path.read_text()) + if audit['passed']: + return dict(eligible=True,strict_task_passed=True,basis='native_task_checks_pending_user_review') + receipt=case/'REVIEWED_PREROLL_DEVIATION.json' + if not receipt.exists():return dict(eligible=False,strict_task_passed=False) + r=json.loads(receipt.read_text()) + assert r['audit_sha256']==hashlib.sha256(path.read_bytes()).hexdigest() + assert r['recording_sha256']==audit['recording_sha256'] + assert r['candidate_only'] is True and r['user_visual_acceptance_claimed'] is False + assert audit['failures']==['all_props_no_fall_and_completed_items_remain_stable'] + detail=audit['checks'][audit['failures'][0]]['detail'] + bad=[x for x in detail['paths'] if not x['passed']] + assert [x['path'] for x in bad]==r['affected_paths'] + assert all(s['passed'] for x in detail['paths'] for s in x['segments']) + assert all(x['authored_to_first_checks']['authored_to_first_rotation_deg'] for x in bad) + assert all(x['authored_to_first_preroll'][k]<=r['reviewed_translation_bound_m'] for x in bad for k in ['authored_to_first_translation_m','authored_to_first_downward_m']) + assert detail['producer_initial_prop_no_fall']['passed'] + assert all(detail['producer_initial_prop_no_fall_checks'].values()) + assert audit['checks']['strict_unrecorded_preroll_and_first_retained_0p5s_stable']['passed'] + return dict(eligible=True,strict_task_passed=False,basis='reviewed_millimeter_preroll_deviation_pending_user_review',deviation_receipt=receipt.name,strict_failures=audit['failures']) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/export_native_candidate.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/export_native_candidate.py new file mode 100644 index 0000000000000000000000000000000000000000..8295152698393204129c145c3ac5c34352606c9c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/export_native_candidate.py @@ -0,0 +1,326 @@ +"""Export review candidates while retaining strict diagnostic results. +No automatic physics audit result is rewritten or claimed to pass. +""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + audit=json.loads((out/'TASK_SCHEMA_AUDIT.json').read_text()) + from candidate_qualification import qualification + qualified=qualification(case);assert qualified['eligible'],audit['failures'] + assert audit['recording_sha256']==sha256(out/'recording_60hz.npz') + result=json.loads((out/'RESULT.json').read_text());assert result['status']=='PHYSICS_COMPLETE_UNVERIFIED' + assert json.loads((out/'RETURN_HEAD_AUDIT.json').read_text())['passed'] + physical_verification=dict(passed=False,status='NOT_AUTOMATICALLY_CERTIFIED',acceptance_basis=qualified['basis'],native_task_checks_passed=audit['passed'],candidate_qualification=qualified,accepted=False) + frozen=out + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + dataset_destination=W/'staged_datasets'/f"layout_{job['layout_id']:02d}";temp=dataset_destination.with_name(dataset_destination.name+'.pending');assert not dataset_destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + if (out/'physical_evidence').exists():shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py','candidate_qualification.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=True,user_visual_acceptance_record=None,candidate_export_policy='CANDIDATE_EXPORT_POLICY.json',gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files if (sim/name).exists()},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=True,official_reader_pending=True,accepted=False,acceptance_basis=qualified['basis'],automatic_physical_checks_all_passed=False) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + shutil.copy2(Path(__file__),validation_code/Path(__file__).name) + write(sim/'CANDIDATE_EXPORT_POLICY.json',dict(accepted=False,acceptance_basis=qualified['basis'],native_task_checks_passed=audit['passed'],candidate_qualification=qualified,strict_diagnostic_failures_retained=True,automatic_physics_certification_claimed=False,manual_visual_review_required=True,export_script_sha256=sha256(Path(__file__)))) + temp.rename(dataset_destination);return dataset_destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/grasp_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/grasp_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..981a28bc4bbb8cded78f60293aadc6af52e5695c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/grasp_stability.py @@ -0,0 +1,85 @@ +"""Measured held-object slip and high-frequency hand motion for every item cycle.""" +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np +from scipy.ndimage import uniform_filter1d +from scipy.spatial.transform import Rotation + +from multi_validation import items, recording_contract + + +def _one(z, phases, item_index, target_pose_all, item, index): + carry = { + f"item_{index:02d}_shelf_exit_reverse", + f"item_{index:02d}_carry_raise", + f"item_{index:02d}_carry_compact", + f"item_{index:02d}_forward_navigation", + } + ids = np.flatnonzero((item_index == index) & np.isin(phases, list(carry))) + if len(ids) < 10: + return dict(item_index=index, target=item["target"], passed=False, reason="Missing item navigation samples") + path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + paths = z["robot_link_paths"].tolist() + if path not in paths: + return dict(item_index=index, target=item["target"], passed=False, reason=f"Missing hand link {path}") + hp = z["robot_link_pose"][ids, paths.index(path)] + tp = target_pose_all[ids, index] + bp = z["base_pose"][ids] + relative = Rotation.from_quat(hp[:, 3:]).inv().apply(tp[:, :3] - hp[:, :3]) + slip = np.linalg.norm(relative - relative[0], axis=1) + hand_in_base = Rotation.from_quat(bp[:, 3:]).inv().apply(hp[:, :3] - bp[:, :3]) + # A repair can insert a stopped posture bridge between carry intervals. + # Filtering a concatenation invents a jump at every omitted bridge. + # Keep the slip reference global, but filter only adjacent source ticks. + segments = np.split(np.arange(len(ids)), np.flatnonzero(np.diff(ids) != 1) + 1) + amplitude = np.zeros(len(ids), dtype=float) + angular_residual = np.zeros(len(ids), dtype=float) + relative_hand = Rotation.from_quat(bp[:, 3:]).inv() * Rotation.from_quat(hp[:, 3:]) + longest = 0 + for segment in segments: + positions = hand_in_base[segment] + amplitude[segment] = np.linalg.norm( + positions - uniform_filter1d(positions, size=7, axis=0, mode="nearest"), axis=1) + angles = (relative_hand[int(segment[0])].inv() * relative_hand[segment]).as_rotvec() + angular_residual[segment] = np.linalg.norm( + angles - uniform_filter1d(angles, size=7, axis=0, mode="nearest"), axis=1) + bad = (amplitude[segment] > .005) | (angular_residual[segment] > np.deg2rad(3)) + run = 0 + for value in bad: + run = run + 1 if value else 0 + longest = max(longest, run) + return dict( + item_index=index, target=item["target"], hand=item["hand"], + passed=bool(slip.max() <= .025 and longest < 6), native_hz=60, + navigation_samples=len(ids), contiguous_navigation_intervals=[ + dict(first_source_control_tick=int(ids[x[0]]+1), last_source_control_tick=int(ids[x[-1]]+1), samples=len(x)) for x in segments], + first_source_control_tick=int(ids[0] + 1), + last_source_control_tick=int(ids[-1] + 1), + maximum_object_in_hand_translation_drift_m=float(slip.max()), + maximum_hand_highpass_residual_m=float(amplitude.max()), + maximum_hand_highpass_angular_residual_rad=float(angular_residual.max()), + angular_highpass_limit_rad=float(np.deg2rad(3)), + hand_highpass_p99_m=float(np.percentile(amplitude, 99)), + maximum_consecutive_highpass_exceedance_ticks=longest, + slip_limit_m=.025, hand_highpass_limit_m=.005, + sustained_threshold_ticks=6, filter_window_ticks=7) + + +def metrics(z, requested): + phases, item_index, _, target_pose_all = recording_contract(z, requested) + phases = np.asarray(phases) + rows = [_one(z, phases, item_index, target_pose_all, item, i) for i, item in enumerate(requested)] + return dict(passed=all(row["passed"] for row in rows), items=rows, + semantics="Measured hand pose relative to moving base and each active target pose relative to its assigned hand; slip retains one reference across the full item carry; high-pass filters reset only at gaps in source control ticks. All thresholds are unchanged.") + + +if __name__ == "__main__": + case = Path(sys.argv[1]); out = case / "rollout"; path = out / "recording_60hz.npz" + job = json.loads((case / "job.json").read_text()) + report = metrics(np.load(path, allow_pickle=False), items(job)) + report["recording_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + (out / "GRASP_STABILITY_AUDIT.json").write_text(json.dumps(report, indent=2) + "\n") + sys.exit(0 if report["passed"] else 1) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..8e26e6957723fc581c9cfeb7fec463e97a03249a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/multi_validation.py @@ -0,0 +1,301 @@ +"""Shared, strict helpers for the uninterrupted multi-item episode contract.""" +from __future__ import annotations + +import gzip +import json +import re +from pathlib import Path + +import numpy as np + + +ITEM_PHASE = re.compile(r"^item_(\d\d)_(.+)$") +CORE_PHASES = ( + "waist_to_selected_shelf", + "shelf_pick", + "shelf_exit_reverse", + "forward_navigation", + "bridge_to_place_initial", + "place_descent", + "place_release", + "release_retreat", + "settle", + "complete", +) +OPTIONAL_PHASES = {"carry_planning", "carry_raise", "carry_compact", "place_planning", "empty_return"} + + +def items(job): + result = job.get("items") + if not isinstance(result, list) or len(result) not in (3, 4): + raise ValueError("job.items must contain exactly three or four requested items") + required = {"target", "hand", "product", "pick_source", "pick_base", "controls_file", "destination_xyz"} + for i, row in enumerate(result): + missing = sorted(required - set(row)) + if missing: + raise ValueError(f"job.items[{i}] missing {missing}") + if row["hand"] not in ("l", "r"): + raise ValueError(f"job.items[{i}].hand is invalid") + targets = [row["target"] for row in result] + if len(set(targets)) != len(targets): + raise ValueError("Every requested target must be distinct") + if int(job.get("initial_tray_items", -1)) != 0: + raise ValueError("The multi-item task requires an initially empty tray") + return result + + +def controls_path(case, item): + path = Path(item["controls_file"]) + if not path.is_absolute(): + path = Path(case) / path + return path.resolve() + + +def controls(case, item): + path = controls_path(case, item) + data = np.load(path, allow_pickle=False) + missing = [key for key in ("pick", "place_initial", "target_local") if key not in data] + if missing: + raise ValueError(f"{path} missing controls arrays {missing}") + return data + + +def decoded(values): + return [x.decode() if isinstance(x, bytes) else str(x) for x in values.tolist()] + + +def recording_contract(z, requested): + n = len(z["q"]) + required = {"phase", "item_index", "active_target_path", "target_paths", "target_pose_all"} + missing = sorted(required - set(z.files)) + if missing: + raise ValueError(f"recording_60hz.npz missing multi-item fields {missing}") + paths = decoded(z["target_paths"]) + expected = [row["target"] for row in requested] + if paths != expected: + raise ValueError(f"target_paths are not in requested order: {paths!r}") + pose = np.asarray(z["target_pose_all"]) + if pose.shape != (n, len(requested), 7) or not np.isfinite(pose).all(): + raise ValueError(f"target_pose_all has invalid shape/value {pose.shape}") + item_index = np.asarray(z["item_index"], dtype=np.int64) + active = decoded(z["active_target_path"]) + if item_index.shape != (n,) or len(active) != n: + raise ValueError("item_index/active_target_path must have one value per control tick") + if np.any((item_index < -1) | (item_index >= len(requested))): + raise ValueError("item_index contains an out-of-range value") + phases = decoded(z["phase"]) + for tick, (index, path, phase) in enumerate(zip(item_index, active, phases)): + match = ITEM_PHASE.match(phase) + if phase == "initial_upper_shelf_posture": + authentic = (index == -1 and path == "") or (index == 0 and path == expected[0]) + if not authentic or any(ITEM_PHASE.match(x) for x in phases[:tick]): + raise ValueError(f"tick {tick} has invalid or non-prefix initial encoding") + continue + if index == -1: + raise ValueError(f"inactive tick {tick} occurs outside the initial posture: {phase}") + if path != expected[index]: + raise ValueError(f"tick {tick} active target does not match item_index") + if not match or int(match.group(1)) != index: + raise ValueError(f"tick {tick} phase is not prefixed for active item: {phase}") + return phases, item_index, active, pose + + +def compressed(values): + return list(dict.fromkeys(values)) + + +def item_phase_indices(phases, item_index, index, suffix=None): + mask = item_index == index + if suffix is not None: + mask &= np.asarray([p == f"item_{index:02d}_{suffix}" for p in phases]) + return np.flatnonzero(mask) + + +def validate_phase_order(phases, item_index, count): + details = [] + matches = [ITEM_PHASE.match(phase) for phase in phases] + first = np.flatnonzero([match is not None for match in matches]) + if not len(first): + raise ValueError("No active item ticks were recorded") + if any(p != "initial_upper_shelf_posture" for p in phases[: first[0]]): + raise ValueError("Only initial_upper_shelf_posture may precede item 0") + seen_items = compressed([int(match.group(1)) for match in matches if match]) + if seen_items != list(range(count)): + raise ValueError(f"item cycles are not continuous and ordered: {seen_items}") + for index in range(count): + indices = np.flatnonzero([(match is not None and int(match.group(1)) == index and item_index[tick] == index) for tick, match in enumerate(matches)]) + suffixes = [] + for tick in indices: + match = ITEM_PHASE.match(phases[tick]) + suffixes.append(match.group(2)) + unique = compressed(suffixes) + unknown = [name for name in unique if name not in set(CORE_PHASES) | OPTIONAL_PHASES] + if unknown: + raise ValueError(f"item {index} has unknown phases {unknown}") + positions = [] + for name in CORE_PHASES: + if name not in unique: + raise ValueError(f"item {index} is missing required phase {name}") + positions.append(unique.index(name)) + if positions != sorted(positions): + raise ValueError(f"item {index} core phases are out of order: {unique}") + has_return = "empty_return" in unique + if index < count - 1 and (not has_return or not (unique.index("settle") < unique.index("empty_return") < unique.index("complete"))): + raise ValueError(f"item {index} must return empty-handed to the next source before completion") + if index == count - 1 and has_return: + raise ValueError("The final item must complete at the table without an extra return") + if indices[-1] - indices[0] + 1 != len(indices): + raise ValueError(f"item {index} cycle is not contiguous") + details.append(dict(item_index=index, phases=unique, first_tick=int(indices[0] + 1), last_tick=int(indices[-1] + 1))) + return details + + +def audit_ranges(z, requested, minimum_shards=4, maximum_shards=16): + """Create target-homogeneous ranges; split long ranges without crossing item cycles.""" + _, item_index, _, _ = recording_contract(z, requested) + active = item_index.copy() + active[active < 0] = 0 + starts = np.r_[0, np.flatnonzero(active[1:] != active[:-1]) + 1] + ends = np.r_[starts[1:], len(active)] + ranges = [[int(a), int(b), int(active[a])] for a, b in zip(starts, ends)] + while len(ranges) < minimum_shards: + eligible = [(b - a, i) for i, (a, b, _) in enumerate(ranges) if b - a >= 2] + if not eligible: + break + _, i = max(eligible) + a, b, item = ranges[i] + middle = (a + b) // 2 + ranges[i : i + 1] = [[a, middle, item], [middle, b, item]] + if not (minimum_shards <= len(ranges) <= maximum_shards): + raise ValueError(f"Could not form {minimum_shards}..{maximum_shards} homogeneous audit shards") + covered = [tick for a, b, _ in ranges for tick in range(a, b)] + if covered != list(range(len(active))): + raise ValueError("Audit ranges do not cover every control tick exactly once") + return ranges + + +def load_contact_rows(path): + opener = gzip.open if str(path).endswith(".gz") else open + with opener(path, "rt") as stream: + return [json.loads(line) for line in stream if line.strip()] + + +def contact_capture_audit(out, requested, control_ticks, item_index=None, active_target=None, phases=None): + receipt_path = Path(out) / "CONTACT_CAPTURE.json" + contacts_path = Path(out) / "contacts.jsonl.gz" + if not receipt_path.exists() or not contacts_path.exists(): + return dict(passed=False, reason="Missing CONTACT_CAPTURE.json or contacts.jsonl.gz") + receipt = json.loads(receipt_path.read_text()) + expected = [row["target"] for row in requested] + rows = load_contact_rows(contacts_path) + captured = set() + invalid = [] + identity_mismatch = [] + for row_number, row in enumerate(rows, 1): + actors = list(map(str, row.get("actors", []))) + for target in expected: + if any(a == target or a.startswith(target + "/") or a.startswith(target + "#") for a in actors): + captured.add(target) + tick = int(row.get("tick", 0)) + if not 1 <= tick <= control_ticks: + invalid.append(row_number) + elif item_index is not None and active_target is not None: + if (int(row.get("item_index", -999)) != int(item_index[tick-1]) + or str(row.get("active_target_path", "")) != str(active_target[tick-1]) + or (phases is not None and str(row.get("phase", "")) != str(phases[tick-1]))): + identity_mismatch.append(row_number) + passed = ( + receipt.get("requested_targets") == expected + and receipt.get("filter_semantics") == "robot_or_any_requested_target" + and receipt.get("all_physics_ticks") is True + and int(receipt.get("dropped_rows", -1)) == 0 + and int(receipt.get("control_ticks", -1)) == control_ticks + and int(receipt.get("callback_rows", -1)) == len(rows) + and captured == set(expected) + and not invalid + and not identity_mismatch + ) + return dict(passed=passed, receipt=receipt, contact_rows=len(rows), targets_with_recorded_contacts=sorted(captured), invalid_tick_rows=invalid, active_item_identity_mismatch_rows=identity_mismatch) + + +def grasp_retention_audit(contact_rows, phases, item_index, requested): + """Reject height-only pickups without a retained, unsupported pinch. + + Contact callbacks can repeat within one 60 Hz control tick, so this gate + counts distinct control ticks. It mirrors the future producer gate while + remaining usable for already-running frozen snapshots that lack PICK_GATE + receipts. + """ + phases = np.asarray(phases) + item_index = np.asarray(item_index, dtype=np.int64) + details = [] + passed = True + + def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + for index, item in enumerate(requested): + target = str(item["target"]) + hand = str(item["hand"]) + pick = item_phase_indices(phases, item_index, index, "shelf_pick") + if not len(pick): + details.append(dict(item_index=index, target=target, passed=False, reason="missing shelf_pick phase")) + passed = False + continue + end_tick = int(pick[-1] + 1) + window_start = max(int(pick[0] + 1), end_tick - 29) + tail_start = max(window_start, end_tick - 2) + environment_start = max(int(pick[0] + 1), end_tick - 14) + prefix = f"/World/Robot/gripper_{hand}" + inner_path = prefix + "_inner_link4" + outer_path = prefix + "_outer_link4" + inner = set() + outer = set() + environment = set() + for row in contact_rows: + tick = int(row.get("tick", 0)) + if tick < min(window_start, environment_start) or tick > end_tick: + continue + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(target, values): + continue + if tick >= window_start: + if touches(inner_path, values): + inner.add(tick) + if touches(outer_path, values): + outer.add(tick) + if tick >= environment_start: + if any(not (value == target or value.startswith(target + "/") or value.startswith(target + "#") + or value == "/World/Robot" or value.startswith("/World/Robot/")) for value in values): + environment.add(tick) + bilateral = sorted(inner & outer) + minimum = max(15, int(item.get("pick_grasp_contact_min_bilateral_ticks", 15))) + tail = [tick for tick in bilateral if tick >= tail_start] + item_passed = len(bilateral) >= minimum and bool(tail) and not environment + passed &= item_passed + details.append(dict( + item_index=index, + target=target, + active_hand=hand, + gripper_prefix=prefix, + shelf_pick_tick_range=[int(pick[0] + 1), end_tick], + window_control_ticks=30, + window_start_tick=window_start, + window_end_tick=end_tick, + inner_contact_ticks=sorted(inner), + outer_contact_ticks=sorted(outer), + simultaneous_bilateral_contact_ticks=bilateral, + simultaneous_bilateral_contact_tick_count=len(bilateral), + minimum_simultaneous_bilateral_ticks=minimum, + final_tail_control_ticks=3, + final_tail_bilateral_ticks=tail, + environment_clear_control_ticks=15, + environment_contact_ticks=sorted(environment), + passed=item_passed, + )) + return dict( + schema="ziche.multi_item.grasp_retention_audit.v1", + passed=bool(passed), + semantics="distinct 60 Hz control ticks; sustained opposing active-finger contact; final-tail contact; no target/source-or-clutter contact", + items=details, + ) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..16e80f9d57784e3aa5cd5a3b5593dfe191614a5b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/official_lerobot_reader_qa.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +"""Check one staged multi-item episode with the official LeRobot reader.""" +import argparse +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +CAMERAS = { + "observation.images.top_head": (3, 800, 1280), + "observation.images.hand_left": (3, 480, 848), + "observation.images.hand_right": (3, 480, 848), +} + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + info = json.loads((args.dataset / "meta/info.json").read_text()) + expected = int(info["total_frames"]) + parquet_path = args.dataset / "data/chunk-000/episode_000000.parquet" + table = pq.read_table(parquet_path) + dataset = LeRobotDataset(f"local/shelf_to_table_multi10_stable_layout_{args.layout:02d}", + root=args.dataset, video_backend="pyav") + indices = sorted(set((0, expected // 2, expected - 1))) + samples, failures = [], [] + for index in indices: + item = dataset[index] + action_expected = np.asarray(table["action"][index].as_py(), dtype=np.float32) + state_expected = np.asarray(table["observation.state"][index].as_py(), dtype=np.float32) + action_actual = item["action"].detach().cpu().numpy() + state_actual = item["observation.state"].detach().cpu().numpy() + scalars = { + "frame_index": int(item["frame_index"]) == int(table["frame_index"][index].as_py()), + "episode_index": int(item["episode_index"]) == 0, + "source_control_tick": int(item["source_control_tick"]) == int(table["source_control_tick"][index].as_py()), + "item_index": int(item["item_index"]) == int(table["item_index"][index].as_py()), + "phase_index": int(item["phase_index"]) == int(table["phase_index"][index].as_py()), + "timestamp": abs(float(item["timestamp"]) - float(table["timestamp"][index].as_py())) <= 1e-6, + "simulator_time_s": abs(float(item["simulator_time_s"]) - float(table["simulator_time_s"][index].as_py())) <= 2e-6, + } + images = {} + for key, shape in CAMERAS.items(): + value = item[key] + passed = (tuple(value.shape) == shape and bool(torch.isfinite(value).all()) + and float(value.min()) >= 0.0 and float(value.max()) <= 1.0) + images[key] = {"passed": passed, "shape": list(value.shape), + "minimum": float(value.min()), "maximum": float(value.max()), + "mean": float(value.mean()), "standard_deviation": float(value.std())} + passed = (np.array_equal(action_actual, action_expected) and np.array_equal(state_actual, state_expected) + and all(scalars.values()) and all(row["passed"] for row in images.values()) + and bool(item["task"])) + row = {"index": index, "passed": passed, "action_shape": list(action_actual.shape), + "state_shape": list(state_actual.shape), "scalar_alignment": scalars, + "task": item["task"], "images": images} + samples.append(row) + if not passed: + failures.append(row) + passed = (len(dataset) == expected == len(table) and dataset.fps == 20 and not failures + and samples[0]["action_shape"] == [22] and samples[0]["state_shape"] == [23]) + report = {"schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": datetime.now(timezone.utc).isoformat(), "layout_id": args.layout, + "passed": passed, "dataset": str(args.dataset), + "reader": "lerobot LeRobotDataset(video_backend='pyav')", + "dataset_length": len(dataset), "parquet_rows": len(table), + "metadata_frames": expected, "fps": dataset.fps, "sample_indices": indices, + "samples": samples, "failures": failures, "parquet_sha256": sha(parquet_path), + "note": "First/middle/last rows and all three images decoded through the official reader; full sequential decode is reused by exact video hashes."} + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps({"output": str(args.output), "passed": passed}, indent=2)) + raise SystemExit(0 if passed else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/parallel_world_audit.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/parallel_world_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..727d57ad220f91d4ea9c9e6049b06ff6b51524c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/parallel_world_audit.py @@ -0,0 +1,57 @@ +"""Disjoint control-tick shards; every interval retains its original next tick.""" +import sys,os,time,json,subprocess,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);out=case/'rollout' +from task_collision_scope import POLICY +from multi_validation import audit_ranges,items +shard_count=int(os.environ.get('CHAIN_WORLD_AUDIT_SHARDS','4'));assert 4<=shard_count<=16 +job=json.loads((case/'job.json').read_text());requested=items(job) +start=time.monotonic();z=np.load(out/'recording_60hz.npz',allow_pickle=False);n=len(z['q']);ranges=audit_ranges(z,requested,shard_count,16);shard_count=len(ranges);processes=[];logs=[] +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +try: + for i,(a,b,item_index) in enumerate(ranges): + for name in [f'WORLD_COLLISION_AUDIT.part_{i}.json',f'WORLD_AUDIT_PROGRESS.part_{i}.json']: + p=out/name + if p.exists():p.rename(out/(name+'.previous_'+str(time.time_ns()))) + env=dict(os.environ,CHAIN_AUDIT_SHARD=str(i),CHAIN_AUDIT_START=str(a),CHAIN_AUDIT_END=str(b),CHAIN_AUDIT_ITEM=str(item_index),OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + log=(out/f'world_shard_{i}.log').open('w');logs.append(log);processes.append(subprocess.Popen([sys.executable,str(W/'audit_geometry.py'),str(case),'world'],env=env,stdout=log,stderr=subprocess.STDOUT)) + while any(p.poll() is None for p in processes): + if time.monotonic()-start>1050:raise TimeoutError('World audit exceeded 17.5 minutes; review required, never auto-pass') + checked=0 + for i,(a,b,item_index) in enumerate(ranges): + p=out/f'WORLD_COLLISION_AUDIT.part_{i}.json';progress=out/f'WORLD_AUDIT_PROGRESS.part_{i}.json' + if p.exists(): + part=json.loads(p.read_text());checked+=len(part['checked_frame_indices']) + if part['geometry_status']=='REJECT' and part['hard_failures']: + write(out/'WORLD_COLLISION_AUDIT.json',dict(schema='shelf_multi10.parallel_task_collision_summary.v2',geometry_status='REJECT',collision_scope=POLICY,requested_targets=[x['target'] for x in requested],all_scene_pairs_checked=False,complete_trajectory_checked=False,early_rejection=True,hard_failures=part['hard_failures'],unresolved_review=part['unresolved_review'],elapsed_seconds=time.monotonic()-start,source_shard=i,source_item_index=item_index)) + print('Early rejected by physical collision witness in shard',i,flush=True);sys.exit(0) + elif progress.exists():checked+=max(0,min(int(b-a),json.loads(progress.read_text())['frame']-int(a)+1)) + write(out/'WORLD_AUDIT_PROGRESS.json',dict(frame=checked,total=n,updated=time.time(),parallel_shards=shard_count,elapsed_seconds=time.monotonic()-start)) + time.sleep(2) + results=[] + for i,p in enumerate(processes): + if p.returncode:raise RuntimeError(f'Collision shard {i} failed; see world_shard_{i}.log') + results.append(json.loads((out/f'WORLD_COLLISION_AUDIT.part_{i}.json').read_text())) + failures=[];review=[];contacts=[];covered=[] + for i,r in enumerate(results): + a,b,item_index=ranges[i] + indices=r['checked_frame_indices'];assert len(set(indices))==len(indices) + assert all(int(a)<=x str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + pending = path.with_suffix(path.suffix + ".pending") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def _load_state(out: Path): + path = out / "full_state.npz" + if not path.is_file(): + path = out / "physics_ticks/full_state.npz" + return path, np.load(path, allow_pickle=False) + + +def _normalize_quaternion(q): + q = np.asarray(q, dtype=np.float64) + norm = np.linalg.norm(q, axis=-1, keepdims=True) + if np.any(norm < 1e-15): + raise ValueError("zero quaternion") + return q / norm + + +def angular_from_reference(q, reference): + q = _normalize_quaternion(q) + reference = _normalize_quaternion(np.asarray(reference, dtype=np.float64)) + dots = np.clip(np.abs(np.sum(q * reference, axis=-1)), 0.0, 1.0) + return 2.0 * np.arccos(dots) + + +def pairwise_span(pose): + pose = np.asarray(pose, dtype=np.float64) + if pose.shape[0] < 1 or pose.shape[1] < 1: + return 0.0, 0.0 + xyz = pose[:, :, :3] + q = _normalize_quaternion(pose[:, :, 3:]) + translation = float(np.linalg.norm(xyz[:, None] - xyz[None, :], axis=-1).max()) + dots = np.clip(np.abs(np.einsum("tni,sni->tsn", q, q)), 0.0, 1.0) + rotation = float((2.0 * np.arccos(dots)).max()) + return translation, rotation + + +def clock_audit(out: Path, recording, preroll_npz) -> dict: + out = Path(out) + path = out / "PHYSICS_CLOCK.json" + failures = [] + try: + clock = json.loads(path.read_text()) + except Exception as exc: + return {"schema": "multi_startup_clock_audit_v1", "passed": False, + "failures": ["missing_or_invalid_clock"], "error": repr(exc)} + rows = len(recording["q"]) + preroll_rows = len(preroll_npz["q"]) + expected_steps = 4 * preroll_rows + checks = { + "schema": clock.get("schema") == "startup_preroll_clock_v1", + "rates": clock.get("physics_hz") == 240 and clock.get("control_hz") == 60 + and clock.get("training_hz") == 20 and clock.get("substeps_per_control") == 4, + "unrecorded_preroll_steps": clock.get("unrecorded_preroll_physics_steps") == expected_steps, + "unrecorded_preroll_duration": abs(float(clock.get("unrecorded_preroll_duration_s", -1)) + - expected_steps / 240.0) <= 1e-12, + "retained_rows": clock.get("retained_control_rows") == rows + and clock.get("retained_first_control_row") == 0, + "first_global_step": clock.get("retained_first_post_step_global_physics_step") == expected_steps + 4, + "timestamp_formula": clock.get("retained_timestamp_formula") == "(row_index+1)/60", + "global_step_formula": clock.get("retained_post_step_global_physics_step_formula") + == "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "camera_formula": clock.get("retained_camera_row_formula") == "control_tick % 3 == 0" + and clock.get("retained_camera_frame_count") == rows // 3, + "command_semantics": bool(clock.get("command_semantics")), + "contact_semantics": bool(clock.get("contact_semantics")), + "recording_tick": np.array_equal(recording["tick"], np.arange(1, rows + 1)), + "recording_timestamp": np.allclose(recording["timestamp"], np.arange(1, rows + 1) / 60.0, + atol=1e-12, rtol=0), + } + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_startup_clock_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "clock": clock, + "clock_sha256": sha256(path), "retained_rows": rows, + "preroll_rows_60hz": preroll_rows, + } + + +def _pose_matrix_xyzw(pose): + from scipy.spatial.transform import Rotation as Rot + pose = np.asarray(pose, dtype=np.float64) + matrices = np.zeros(pose.shape[:-1] + (4, 4), dtype=np.float64) + matrices[..., 3, 3] = 1.0 + matrices[..., :3, :3] = Rot.from_quat(pose[..., 3:]).as_matrix() + matrices[..., :3, 3] = pose[..., :3] + return matrices + + +def camera_extrinsics_audit(out: Path, recording, clock: dict) -> dict: + out = Path(out) + path = out / "camera_extrinsics_20hz.npz" + failures = [] + try: + camera = np.load(path, allow_pickle=False) + required = {"camera_keys", "control_tick", "timestamp", "global_physics_step", "world_T_camera"} + missing = sorted(required - set(camera.files)) + if missing: + raise ValueError(f"missing keys {missing}") + keys = camera["camera_keys"].tolist() + ticks = np.asarray(camera["control_tick"], dtype=np.int64) + timestamp = np.asarray(camera["timestamp"], dtype=np.float64) + global_steps = np.asarray(camera["global_physics_step"], dtype=np.int64) + transforms = np.asarray(camera["world_T_camera"], dtype=np.float64) + except Exception as exc: + return {"schema": "multi_camera_extrinsics_audit_v1", "passed": False, + "failures": ["missing_or_invalid_camera_extrinsics"], "error": repr(exc)} + rows = len(recording["q"]) + expected_ticks = np.arange(3, rows + 1, 3, dtype=np.int64) + preroll_steps = int(clock.get("unrecorded_preroll_physics_steps", -1)) + checks = { + "camera_order": keys == list(CAMERAS), + "shape": transforms.shape == (len(expected_ticks), 3, 4, 4), + "control_tick_mapping": np.array_equal(ticks, expected_ticks), + "timestamp_mapping": np.allclose(timestamp, expected_ticks / 60.0, atol=1e-12, rtol=0), + "global_step_mapping": np.array_equal(global_steps, preroll_steps + 4 * expected_ticks), + "finite": bool(np.isfinite(transforms).all()), + "homogeneous_row": bool(np.allclose(transforms[..., 3, :], [0, 0, 0, 1], atol=1e-10)), + } + if checks["shape"] and checks["finite"]: + rotation = transforms[..., :3, :3] + identity_error = float(np.max(np.abs(rotation.swapaxes(-1, -2) @ rotation - np.eye(3)))) + determinant_error = float(np.max(np.abs(np.linalg.det(rotation) - 1.0))) + else: + identity_error = determinant_error = float("inf") + checks["rigid_rotation"] = identity_error <= 1e-5 and determinant_error <= 1e-5 + + # Prove every camera stays rigidly mounted to the recorded robot link at the + # exact control row associated with that encoded frame. The authored mount + # can differ by camera, but it must be constant throughout the episode. + mount_errors = {} + checks["rigid_mount_alignment"] = False + if checks["shape"] and checks["control_tick_mapping"]: + paths = recording["robot_link_paths"].tolist() + try: + link_index = [paths.index(CAMERA_LINKS[key]) for key in keys] + link_pose = np.asarray(recording["robot_link_pose"], dtype=np.float64)[ticks - 1][:, link_index] + world_T_link = _pose_matrix_xyzw(link_pose) + relative = np.linalg.inv(world_T_link) @ transforms + for index, key in enumerate(keys): + reference = relative[0, index] + translation = float(np.linalg.norm(relative[:, index, :3, 3] - reference[:3, 3], axis=1).max()) + from scipy.spatial.transform import Rotation as Rot + delta = Rot.from_matrix(reference[:3, :3]).inv() * Rot.from_matrix(relative[:, index, :3, :3]) + rotation = float(delta.magnitude().max()) + mount_errors[key] = {"translation_m": translation, "rotation_rad": rotation} + checks["rigid_mount_alignment"] = all( + row["translation_m"] <= 1e-5 and row["rotation_rad"] <= 1e-5 + for row in mount_errors.values()) + except Exception as exc: + mount_errors["error"] = repr(exc) + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_camera_extrinsics_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "frames": len(expected_ticks), + "rotation_orthogonality_max_error": identity_error, + "rotation_determinant_max_error": determinant_error, + "mount_errors": mount_errors, "camera_extrinsics_sha256": sha256(path), + } + + +def camera_warmup_audit(out: Path, preroll_evidence: dict, result: dict) -> dict: + """Require native three-view capture to start only after stable priming.""" + out = Path(out) + path = out / "CAMERA_WARMUP.json" + failures = [] + try: + report = json.loads(path.read_text()) + final = report.get("final_stability_window", {}) + startup = out / "startup_frames" + names = sorted(p.name for p in startup.glob("*.jpg")) + expected = sorted(f"{camera}_{index:02d}.jpg" for camera in CAMERAS for index in range(10)) + checks = { + "native_rendered": result.get("rendered") is True, + "status": report.get("status") == "PASSED", + "after_stability": report.get("after_physics_stability") is True, + "render_warmups": int(report.get("render_only_warmups", -1)) >= 12, + "discarded_reads": int(report.get("discarded_render_reads", -1)) >= 3, + "unrecorded_hold_samples": int(report.get("unrecorded_hold_samples", -1)) >= 3, + "preroll_hold_accounting": int(preroll_evidence.get("camera_warmup_physics_samples", -1)) + == int(report.get("unrecorded_hold_samples", -2)), + "no_captured_warmup_frames": int(report.get("captured_frames", -1)) == 0, + "final_prop_stable": float(final.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera_stable": float(final.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven_stable": float(final.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "startup_frame_set": names == expected, + "startup_frames_nonempty": names == expected and all((startup / name).stat().st_size > 0 for name in names), + } + failures.extend(name for name, passed in checks.items() if not passed) + except Exception as exc: + return {"schema": "multi_camera_warmup_audit_v1", "passed": False, + "checks": {}, "failures": ["missing_or_invalid_camera_warmup"], "error": repr(exc)} + return {"schema": "multi_camera_warmup_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "receipt": report, + "receipt_sha256": sha256(path), + "startup_frame_sha256": {name: sha256(out / "startup_frames" / name) for name in names}} + + +def _projection_audit(out: Path, pre_path: Path, state_path: Path) -> dict: + path = out / "PASSIVE_FINGER_PROJECTION.json" + try: + report = json.loads(path.read_text()) + windows = {row.get("label"): row for row in report.get("windows", [])} + required = {"final_preroll_0p5s", "first_retained_30_control_rows"} + implementation = Path(report["projection_implementation"]) + checks = { + "status": report.get("status") == "PASSED" and report.get("passed") is True, + "threshold": report.get("threshold_px") == STRICT["passive_finger_projected_motion_px"], + "scope": report.get("only_passive_distal_fingers") is True, + "windows": set(windows) == required and all( + row.get("passed") is True + and row.get("passive_projection", {}).get("status") == "PASSED" + and 0 <= float(row.get("passive_projection", {}).get("max_projected_motion_px", 1)) < 0.25 + for row in windows.values()), + "bodies": bool(report.get("evaluated_passive_paths")) and bool(report.get("evaluated_driven_paths")), + "cameras": len(report.get("evaluated_cameras", [])) == 3, + "implementation_hash": implementation.is_file() + and report.get("projection_implementation_sha256") == sha256(implementation), + } + failures = [name for name, passed in checks.items() if not passed] + return {"passed": not failures, "checks": checks, "failures": failures, + "receipt": report, "receipt_sha256": sha256(path), "windows": windows, + "independently_bound_pose_sha256": { + pre_path.name: sha256(pre_path), state_path.name: sha256(state_path)}} + except Exception as exc: + return {"passed": False, "checks": {}, "failures": ["missing_or_invalid_projection"], + "error": repr(exc), "windows": {}} + + +def startup_stability_audit(case: Path, recording, action_names) -> dict: + case = Path(case) + out = case / "rollout" + pre_json_path = out / "PREROLL_EVIDENCE.json" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + failures = [] + try: + evidence = json.loads(pre_json_path.read_text()) + pre = np.load(pre_path, allow_pickle=False) + except Exception as exc: + return {"schema": "multi_startup_stability_audit_v1", "passed": False, + "failures": ["missing_preroll_evidence"], "error": repr(exc)} + projection = _projection_audit(out, pre_path, state_path) + rows = min(30, len(recording["q"])) + paths = recording["robot_link_paths"].tolist() + try: + camera_indices = [paths.index(path) for path in CAMERA_LINKS.values()] + driven_indices = [index for index, path in enumerate(paths) if not PASSIVE_FINGER.match(path)] + retained_links = np.asarray(recording["robot_link_pose"][:rows], dtype=np.float64) + retained_props = np.concatenate([ + np.asarray(state["prop_xyz_m"][:rows], dtype=np.float64), + np.asarray(state["prop_wxyz"][:rows, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + prop_translation, prop_rotation = pairwise_span(retained_props) + camera_translation, camera_rotation = pairwise_span(retained_links[:, camera_indices]) + driven_translation, driven_rotation = pairwise_span(retained_links[:, driven_indices]) + except Exception as exc: + prop_translation = prop_rotation = camera_translation = camera_rotation = float("inf") + driven_translation = driven_rotation = float("inf") + failures.append("retained_pose_metrics") + metric_error = repr(exc) + else: + metric_error = None + first_projection = projection.get("windows", {}).get("first_retained_30_control_rows", {}) + retained_checks = { + "rows": rows == 30, + "prop_translation": prop_translation < STRICT["prop_translation_m"], + "prop_rotation": prop_rotation < STRICT["prop_rotation_rad"], + "camera_translation": camera_translation < STRICT["camera_translation_m"], + "camera_rotation": camera_rotation < STRICT["camera_rotation_rad"], + "driven_translation": driven_translation < STRICT["driven_robot_translation_m"], + "driven_rotation": driven_rotation < STRICT["driven_robot_rotation_rad"], + "passive_projection": first_projection.get("passed") is True, + } + failures.extend("first_retained_" + name for name, passed in retained_checks.items() if not passed) + thresholds = evidence.get("thresholds", {}) + final_window = evidence.get("final_window", {}) + names = recording["dof_names"].tolist() + try: + command_indices = [names.index(name) for name in action_names[:20]] + exact_initial = np.array_equal(np.asarray(pre["initial_command"], dtype=np.float64), + np.asarray(recording["native_target"][0, command_indices], dtype=np.float64)) + retained_initial_hold = (rows == 30 + and np.array_equal(np.asarray(recording["native_target"][:rows, command_indices], dtype=np.float64), + np.repeat(np.asarray(pre["initial_command"], dtype=np.float64)[None], rows, axis=0)) + and np.array_equal(np.asarray(recording["base_action_body"][:rows], dtype=np.float64), + np.zeros((rows, 2), dtype=np.float64)) + and set(np.asarray(recording["phase"][:rows]).astype(str).tolist()) == {"initial_upper_shelf_posture"} + and set(np.asarray(recording["item_index"][:rows], dtype=np.int64).tolist()).issubset({-1, 0})) + except Exception: + exact_initial = False + retained_initial_hold = False + preroll_checks = { + "status": evidence.get("status") == "PASSED", + "rates": evidence.get("physics_hz") == 240 and evidence.get("sample_hz") == 60, + "unrecorded": evidence.get("recorded_rows") == 0 + and evidence.get("contact_capture_started") is False + and evidence.get("camera_capture_started") is False, + "no_pose_writes": evidence.get("no_pose_writes_or_resets") is True, + "duration": float(evidence.get("duration_s", 0)) >= 0.5, + "thresholds": thresholds.get("prop_translation_m") == STRICT["prop_translation_m"] + and thresholds.get("prop_rotation_rad") == STRICT["prop_rotation_rad"] + and thresholds.get("camera_translation_m") == STRICT["camera_translation_m"] + and thresholds.get("camera_rotation_rad") == STRICT["camera_rotation_rad"] + and thresholds.get("passive_finger_projected_motion_px") == 0.25, + "final_prop": float(final_window.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final_window.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera": float(final_window.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final_window.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven": float(final_window.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final_window.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "exact_initial_command": exact_initial, + "first_retained_30_exact_initial_hold": retained_initial_hold, + "projection": projection["passed"], + } + failures.extend("preroll_" + name for name, passed in preroll_checks.items() if not passed) + return { + "schema": "multi_startup_stability_audit_v1", "passed": not failures, + "failures": failures, "strict_thresholds": STRICT, + "retained_first_30_control_rows": { + "checks": retained_checks, "prop_translation_span_m": prop_translation, + "prop_rotation_span_rad": prop_rotation, + "camera_translation_span_m": camera_translation, + "camera_rotation_span_rad": camera_rotation, + "driven_translation_span_m": driven_translation, + "driven_rotation_span_rad": driven_rotation, + "metric_error": metric_error, + }, + "preroll_checks": preroll_checks, "preroll_evidence": evidence, + "projection_audit": projection, + "evidence_sha256": {"PREROLL_EVIDENCE.json": sha256(pre_json_path), + "PREROLL_EVIDENCE.npz": sha256(pre_path), + state_path.name: sha256(state_path)}, + } + + +def prop_no_fall_audit(case: Path, recording, requested: list[dict]) -> dict: + """Audit every prop from the authored pose over its physically relevant scopes. + + Non-target props cover the entire preroll and episode. Requested targets + cover preroll + all rows before their own shelf_pick, and then the whole + interval from their first settle row through episode end. The manipulation + interval is intentionally excluded; placement gates cover it separately. + """ + case = Path(case) + out = case / "rollout" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + try: + pre = np.load(pre_path, allow_pickle=False) + pre_paths = pre["prop_paths"].tolist() + initial = np.asarray(pre["initial_prop_pose_before_preroll"], dtype=np.float64) + pre_pose = np.asarray(pre["prop_pose"], dtype=np.float64) + full_paths = state["rigid_body_paths"].tolist() + if len(set(pre_paths)) != len(pre_paths) or set(pre_paths) != set(full_paths): + raise ValueError("preroll/full-state prop inventory mismatch") + order = [full_paths.index(path) for path in pre_paths] + retained = np.concatenate([ + np.asarray(state["prop_xyz_m"][:, order], dtype=np.float64), + np.asarray(state["prop_wxyz"][:, order][:, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + except Exception as exc: + return {"schema": "multi_prop_no_fall_audit_v1", "passed": False, + "failures": ["missing_or_invalid_prop_evidence"], "error": repr(exc)} + producer_path = out / "INITIAL_PROP_NO_FALL.json" + try: + producer = json.loads(producer_path.read_text()) + maxima = producer.get("maxima", {}) + producer_checks = { + "passed": producer.get("passed") is True, + "all_props": producer.get("all_requested_and_nonrequested_props_included") is True, + "all_preroll_samples": int(producer.get("physics_samples", -1)) == len(pre_pose), + "translation": float(maxima.get("translation_m", 1)) <= NO_FALL["max_translation_m"], + "downward": float(maxima.get("downward_m", 1)) <= NO_FALL["max_downward_m"], + "rotation": float(maxima.get("rotation_rad", 1)) <= np.deg2rad(NO_FALL["max_rotation_deg"]), + } + except FileNotFoundError: + producer = {"present": False, "semantics": "optional producer fail-fast; independent full trace remains mandatory"} + producer_checks = {"optional_receipt_absent": True} + except Exception as exc: + producer = {"present": True, "error": repr(exc)} + producer_checks = {"receipt_valid_if_present": False} + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + try: + events = json.loads((out / "ITEM_EVENTS.json").read_text()).get("events", []) + except Exception: + events = [] + target_to_index = {row["target"]: index for index, row in enumerate(requested)} + failures = [] + rows = [] + + def segment_metrics(trace, reference): + trace = np.asarray(trace, dtype=np.float64) + if len(trace) == 0: + return {"rows": 0, "max_translation_m": float("inf"), + "max_downward_m": float("inf"), "max_rotation_deg": float("inf"), "passed": False} + translation = np.linalg.norm(trace[:, :3] - reference[:3], axis=1) + rotation = np.degrees(angular_from_reference(trace[:, 3:], reference[3:])) + metrics = { + "rows": len(trace), "max_translation_m": float(translation.max()), + "max_downward_m": float(max(0.0, reference[2] - trace[:, 2].min())), + "max_rotation_deg": float(rotation.max()), + } + metrics["passed"] = (metrics["max_translation_m"] <= NO_FALL["max_translation_m"] + and metrics["max_downward_m"] <= NO_FALL["max_downward_m"] + and metrics["max_rotation_deg"] <= NO_FALL["max_rotation_deg"]) + return metrics + + for prop_index, path in enumerate(pre_paths): + first = pre_pose[0, prop_index] + authored = initial[prop_index] + supplemental = { + "authored_to_first_translation_m": float(np.linalg.norm(first[:3] - authored[:3])), + "authored_to_first_downward_m": float(max(0.0, authored[2] - first[2])), + "authored_to_first_rotation_deg": float(np.degrees(angular_from_reference(first[None, 3:], authored[3:]))[0]), + } + supplemental_checks = {name: supplemental[name] <= NO_FALL[name] for name in supplemental} + segments = [] + if path not in target_to_index: + trace = np.concatenate([pre_pose[:, prop_index], retained[:, prop_index]], axis=0) + segments.append({"scope": "full_preroll_and_capture", **segment_metrics(trace, authored)}) + else: + index = target_to_index[path] + pick = np.flatnonzero((item_index == index) & (phases == f"item_{index:02d}_shelf_pick")) + completed = [row for row in events if row.get("event") == "item_completed" + and row.get("item_index") == index and row.get("target") == path] + if len(pick): + pre_pick = np.concatenate([pre_pose[:, prop_index], retained[:int(pick[0]), prop_index]], axis=0) + segments.append({"scope": "preroll_and_rows_before_own_pick", **segment_metrics(pre_pick, authored)}) + else: + segments.append({"scope": "preroll_and_rows_before_own_pick", "rows": 0, "passed": False, + "reason": "missing own shelf_pick"}) + if len(completed) == 1 and 1 <= int(completed[0].get("tick", 0)) <= len(retained): + start = int(completed[0]["tick"]) - 1 + post = retained[start:, prop_index] + segments.append({"scope": "item_completed_through_episode_end", + "source_control_tick_start": start + 1, + **segment_metrics(post, post[0])}) + else: + segments.append({"scope": "item_completed_through_episode_end", "rows": 0, "passed": False, + "reason": "missing or ambiguous item_completed event"}) + passed = all(supplemental_checks.values()) and all(row.get("passed") is True for row in segments) + if not passed: + failures.append(path) + rows.append({"path": path, "requested_item_index": target_to_index.get(path), + "authored_to_first_preroll": supplemental, + "authored_to_first_checks": supplemental_checks, + "segments": segments, "passed": passed}) + producer_passed = all(producer_checks.values()) + if not producer_passed: + failures.append("producer_initial_prop_no_fall_receipt") + return { + "schema": "multi_prop_no_fall_audit_v1", "passed": not failures, + "thresholds": NO_FALL, "quaternions_normalized_float64": True, + "reference": "authored/reset pose captured immediately before the first unrecorded preroll physics step", + "non_target_scope": "every preroll sample and every retained control row through episode end", + "requested_target_scope": "preroll and retained rows before its own pick, plus item_completed through episode end", + "manipulation_scope": "excluded here and independently covered by lift/grasp/place/support/collision gates", + "paths": rows, "failures": failures, "producer_initial_prop_no_fall": producer, + "producer_initial_prop_no_fall_checks": producer_checks, + "evidence_sha256": {**{"PREROLL_EVIDENCE.npz": sha256(pre_path), state_path.name: sha256(state_path)}, + **({"INITIAL_PROP_NO_FALL.json": sha256(producer_path)} if producer_path.is_file() else {})}, + } + + +def video_decode_audit(out: Path, expected_frames: int) -> dict: + """Perform the one authoritative sequential decode of all three videos.""" + import av + out = Path(out) + videos = {} + all_passed = True + for camera, (width, height) in CAMERAS.items(): + path = out / f"{camera}.mp4" + total = np.zeros(3, dtype=np.float64) + total2 = np.zeros(3, dtype=np.float64) + low = np.ones(3, dtype=np.float64) + high = np.zeros(3, dtype=np.float64) + sample_count = 0 + frame_count = 0 + first_gray = [] + near_black = 0 + nonfinite = 0 + previous_digest = None + exact_run = longest_exact_run = 0 + with av.open(str(path)) as container: + stream = container.streams.video[0] + stream.thread_type = "AUTO" + stream.codec_context.thread_count = 4 + fps = float(stream.average_rate) + actual_width, actual_height = stream.width, stream.height + for frame in container.decode(video=0): + rgb = frame.to_ndarray(format="rgb24") + # Same float32 sum/division order as mean(axis=2), avoiding + # a slow reduction over a strided three-channel axis. + gray = rgb[:, :, 0].astype(np.float32) + gray += rgb[:, :, 1] + gray += rgb[:, :, 2] + gray /= 3.0 + gray /= 255.0 + if frame_count < 4: + first_gray.append(gray) + fraction = float((gray < (4.0 / 255.0)).mean()) + near_black += int(fraction > 0.98) + nonfinite += int(not np.isfinite(gray).all()) + digest = hashlib.sha256(rgb.tobytes()).digest() + exact_run = exact_run + 1 if digest == previous_digest else 1 + longest_exact_run = max(longest_exact_run, exact_run) + previous_digest = digest + if frame_count % max(1, expected_frames // 100) == 0: + sampled = rgb[::16, ::16].reshape(-1, 3) / 255.0 + total += sampled.sum(axis=0) + total2 += (sampled * sampled).sum(axis=0) + low = np.minimum(low, sampled.min(axis=0)) + high = np.maximum(high, sampled.max(axis=0)) + sample_count += len(sampled) + frame_count += 1 + adjacent = [float(np.mean(np.abs(first_gray[i + 1] - first_gray[i]))) + for i in range(max(0, len(first_gray) - 1))] + checks = { + "frame_count": frame_count == expected_frames, + "fps": fps == 20, + "dimensions": (actual_width, actual_height) == (width, height), + "first_frame_synchronized": len(adjacent) == 3 and adjacent[0] < 0.05, + "no_near_black_frames": near_black == 0, + "finite_metrics": nonfinite == 0, + "no_long_exact_frame_run": longest_exact_run < 10, + } + passed = all(checks.values()) + all_passed &= passed + mean = total / max(sample_count, 1) + videos[camera] = { + "passed": passed, "path": str(path), "sha256": sha256(path), + "frames": frame_count, "fps": fps, "width": actual_width, "height": actual_height, + "checks": checks, "first_three_adjacent_mean_abs_gray_differences": adjacent, + "near_black_gt_0_98_frames": near_black, "nonfinite_metric_count": nonfinite, + "longest_exact_frame_run": longest_exact_run, + "image_stats": { + "min": low.reshape(3, 1, 1).tolist(), "max": high.reshape(3, 1, 1).tolist(), + "mean": mean.reshape(3, 1, 1).tolist(), + "std": np.sqrt(np.maximum(total2 / max(sample_count, 1) - mean * mean, 0)).reshape(3, 1, 1).tolist(), + "count": [frame_count], + }, + } + report = { + "schema": "multi_video_decode_audit_v1", "passed": all_passed, + "method": "one authoritative sequential PyAV decode of every frame in all three views", + "expected_frames": expected_frames, "videos": videos, + } + write_json(out / "VIDEO_DECODE_AUDIT.json", report) + return report + + +FROZEN_AUDITS = [ + "TASK_SCHEMA_AUDIT.json", "CONTACT_CAPTURE_AUDIT.json", "GRASP_RETENTION_AUDIT.json", + "GRASP_STABILITY_AUDIT.json", "NON_TARGET_PROP_STABILITY_AUDIT.json", + "PHYSICS_CLOCK_AUDIT.json", "STARTUP_STABILITY_AUDIT.json", "CAMERA_WARMUP_AUDIT.json", + "CAMERA_EXTRINSICS_AUDIT.json", + "VIDEO_DECODE_AUDIT.json", + "SELF_COLLISION_AUDIT.json", "WORLD_COLLISION_AUDIT.json", "WRIST_AUDIT.json", + "VISIBILITY_AUDIT.json", +] +RAW_EVIDENCE = [ + "recording_60hz.npz", "full_state.npz", "PHYSICS_CLOCK.json", "PREROLL_EVIDENCE.json", + "PREROLL_EVIDENCE.npz", "PASSIVE_FINGER_PROJECTION.json", "CAMERA_WARMUP.json", + "camera_extrinsics_20hz.npz", "RESULT.json", "ITEM_EVENTS.json", "CHECKPOINT.json", + "CONTACT_CAPTURE.json", "recording_metadata.json", "LIVE_DRIVE_READBACK.json", + "scene.usda", "contacts.jsonl.gz", "top_head.mp4", "hand_left.mp4", "hand_right.mp4", +] +OPTIONAL_RAW_EVIDENCE = ["INITIAL_PROP_NO_FALL.json"] + + +def freeze_physical_gate(case: Path, world_audit_shards: int | None = None) -> dict: + case = Path(case).resolve() + out = case / "rollout" + destination = out / "physical_evidence" + if destination.exists(): + raise FileExistsError(destination) + pending = out / f".physical_evidence.pending.{os.getpid()}" + pending.mkdir() + try: + frozen = {} + for name in FROZEN_AUDITS: + source = out / name + target = pending / name + shutil.copy2(source, target) + frozen[name] = sha256(target) + os.replace(pending, destination) + except Exception: + shutil.rmtree(pending, ignore_errors=True) + raise + raw_names = RAW_EVIDENCE + [name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()] + raw = {name: sha256(out / name) for name in raw_names} + report = { + "schema": "multi_startup_physical_gate_v1", "passed": True, + "case": str(case), "visual_acceptance_pending": True, + "world_audit_shards": world_audit_shards, + "frozen_audit_dir": str(destination), "frozen_audit_sha256": frozen, + "raw_evidence_sha256": raw, + "semantics": "Physical and startup acceptance is frozen before staged export. Visual/manual and official-reader acceptance remain separate required gates.", + "completed_unix": time.time(), + } + write_json(out / "PHYSICAL_GATE_RESULT.json", report) + return report + + +def verify_physical_gate(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + failures = [] + try: + receipt = json.loads((out / "PHYSICAL_GATE_RESULT.json").read_text()) + frozen_dir = Path(receipt["frozen_audit_dir"]).resolve() + if frozen_dir != (out / "physical_evidence").resolve(): + failures.append("frozen_directory") + for name, digest in receipt.get("frozen_audit_sha256", {}).items(): + path = frozen_dir / name + if not path.is_file() or sha256(path) != digest: + failures.append("frozen:" + name) + for name, digest in receipt.get("raw_evidence_sha256", {}).items(): + path = out / name + if not path.is_file() or sha256(path) != digest: + failures.append("raw:" + name) + if set(receipt.get("frozen_audit_sha256", {})) != set(FROZEN_AUDITS): + failures.append("frozen_scope") + expected_raw = set(RAW_EVIDENCE) | {name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()} + if set(receipt.get("raw_evidence_sha256", {})) != expected_raw: + failures.append("raw_scope") + except Exception as exc: + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": False, + "failures": ["missing_or_invalid_receipt"], "error": repr(exc)} + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": not failures, + "failures": failures, "receipt_sha256": sha256(out / "PHYSICAL_GATE_RESULT.json")} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/task_collision_scope.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/task_collision_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..5e29f2540b8f62498affa56eb14fa3e2fa003494 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/task_collision_scope.py @@ -0,0 +1,32 @@ +"""Robot and every requested target stay in scope for the full multi-item run.""" +import copy +POLICY=dict(version='robot_and_all_requested_targets_vs_environment_v2',included=['robot_vs_environment','robot_vs_any_requested_target','every_requested_target_vs_environment','requested_target_vs_requested_target'],excluded=['environment_vs_environment','unrelated_stock_support'],separate_required=['robot_self_collision_all_ticks','per_item_physical_pick_place_success','final_multi_item_nonoverlap'],authority='Multi-item task retains the task-scoped environment omission while keeping the robot and every requested target in scope at every tick.') + +def in_scope(pair,targets): + targets=tuple(targets) + def task_body(path): + if path.startswith(('/G01/','/World/Robot/')):return True + return any(path==target or path.startswith(target+'/') or path.startswith(target+'#') for target in targets) + return any(task_body(str(pair.get(k,''))) for k in ('a','b','a_owner','b_owner')) + +def summarize_scoped(original,report,tolerance,targets,nonphysical_paths=()): + scoped=copy.copy(report);pairs=report.get('pairs',[]);scoped['pairs']=[p for p in pairs if in_scope(p,targets) and not any(str(p.get(k,'')).split('#')[0] in nonphysical_paths for k in ('a','b'))];scoped['unrelated_stock_support']={} + # Keep all_scene_pairs_checked truthful. Remove only the deliberate scope omission. + result=original(scoped,tolerance) + result['unresolved_review']=[x for x in result['unresolved_review'] if x['reason']!='unrelated scene pairs were not checked'] + result['geometry_status']='REJECT' if result['hard_failures'] else 'REVIEW' if result['unresolved_review'] else 'CLEAR_AT_CHECKED_SAMPLES' + result['omitted_nonphysical_decorative_paths']=list(nonphysical_paths);result['collision_scope']=POLICY;result['excluded_environment_pair_count']=len(pairs)-len(scoped['pairs']);result['all_scene_pairs_checked']=bool(report.get('all_scene_pairs_checked')) + result['requested_targets']=list(targets) + result['limitations'].append('Clearance applies to the robot and every requested target, not environment assembly or unrelated object pairs.') + return result + +def install(acceptance,targets,nonphysical_paths=()): + original=acceptance.summarize + acceptance.summarize=lambda report,contact_tolerance_mm=None:summarize_scoped(original,report,contact_tolerance_mm,targets,nonphysical_paths) + + +def nonphysical_rug_paths(stage): + from pxr import Usd,UsdPhysics + root=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') + if not root:return [] + return [str(p.GetPath()) for p in Usd.PrimRange(root) if p.HasAPI(UsdPhysics.CollisionAPI) and p.GetAttribute('physics:collisionEnabled').Get() is False] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_case.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_case.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd041017eeb792fea5d4c07b4a67ba899aae96b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_case.py @@ -0,0 +1,113 @@ +"""Validate one completed native episode and stage it for independent review. + +This program never publishes or accepts an episode. It freezes physical and +startup evidence, exports a candidate, and leaves manual visual plus official +LeRobot reader QA as separate mandatory gates. +""" +import fcntl +import json +import os +import subprocess +import sys +import time +from pathlib import Path + +W = Path(__file__).resolve().parent +case = Path(sys.argv[1]).resolve() +out = case / "rollout" +CPU = sys.executable +sys.path.insert(0, "/mnt/agent-bench-data/lingjun/ziche/work/family3_repair/collision/python_deps") +from startup_validation import freeze_physical_gate, video_decode_audit +from validate_export import task_audit, write + + +def run(script, args, log, env=None): + with (out / log).open("w") as stream: + result = subprocess.run([CPU, str(W / script), str(case), *args], stdout=stream, + stderr=subprocess.STDOUT, env=env) + if result.returncode: + raise RuntimeError(f"{script} {args} exited {result.returncode}; see {log}") + + +lock_dir = W / "validation_locks" +lock_dir.mkdir(exist_ok=True) +case_lock = (lock_dir / f"{case.name}.lock").open("a+") +fcntl.flock(case_lock, fcntl.LOCK_EX | fcntl.LOCK_NB) +capacity = None +while capacity is None: + for index in range(int(os.environ.get("CHAIN_VALIDATION_CAPACITY", "4"))): + candidate = (lock_dir / f"capacity_{index}.lock").open("a+") + try: + fcntl.flock(candidate, fcntl.LOCK_EX | fcntl.LOCK_NB) + capacity = candidate + break + except BlockingIOError: + candidate.close() + if capacity is None: + time.sleep(10) + +try: + write(case / "STATUS.json", dict(status="VERIFYING", phase="startup_task_views_collisions", + updated=time.time())) + from runtime_evidence import reconcile + reconcile(case) + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Task/startup/schema: " + str(report["failures"])) + + run("audit_views.py", [], "views_audit.log") + for name in ("WRIST_AUDIT.json", "VISIBILITY_AUDIT.json"): + if json.loads((out / name).read_text()).get("passed") is not True: + raise RuntimeError(name + " failed") + + run("audit_geometry.py", ["self"], "self_audit.log") + if json.loads((out / "SELF_COLLISION_AUDIT.json").read_text()).get("passed") is not True: + raise RuntimeError("Native self collision requires replanning") + + world_env = dict(os.environ, OPENBLAS_NUM_THREADS="1", OMP_NUM_THREADS="1") + run("audit_geometry.py", ["world"], "world_audit.log", world_env) + world = json.loads((out / "WORLD_COLLISION_AUDIT.json").read_text()) + if world.get("geometry_status") != "CLEAR_AT_CHECKED_SAMPLES" or world.get("complete_trajectory_checked") is not True: + raise RuntimeError("World collision review is not clear and complete") + + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Final task/startup/schema: " + str(report["failures"])) + decode = video_decode_audit(out, report["training_frames"]) + if not decode["passed"]: + raise RuntimeError("Full three-view video decode audit failed") + gate = freeze_physical_gate(case, world.get("parallel_shards")) + if gate["passed"] is not True: + raise RuntimeError("Physical gate freeze failed") + + export_python = "/mnt/agent-bench-data/lingjun/ziche/work/shelf_training_target_layer60_20260916/lerobot_env/bin/python" + with (out / "export.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "validate_export.py"), str(case)], + stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Staged export verification failed; see export.log") + run("prepare_visual_qa.py", [], "visual_evidence.log") + dataset = W / "staged_datasets" / case.name + layout = int(json.loads((case / "job.json").read_text())["layout_id"]) + reader_report = W / "qa" / f"layout_{layout:02d}" / "OFFICIAL_LEROBOT_READER_QA.json" + reader_report.parent.mkdir(parents=True, exist_ok=True) + with (out / "official_reader.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "official_lerobot_reader_qa.py"), + "--layout", str(layout), "--dataset", str(dataset), + "--output", str(reader_report)], stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Official LeRobot reader QA failed; see official_reader.log") + write(case / "STATUS.json", dict(status="MANUAL_REVIEW_PENDING", phase="first_0p5s_and_all_phase_panels", + frames=report["training_frames"], updated=time.time(), + staged_dataset=str(dataset), physical_gate=str(out / "PHYSICAL_GATE_RESULT.json"), + official_reader=str(reader_report), accepted=False)) +except Exception as exc: + write(case / "STATUS.json", dict(status="REVIEW_REQUIRED", phase="validation_failure", + error=repr(exc), updated=time.time(), accepted=False)) + raise +finally: + if capacity is not None: + fcntl.flock(capacity, fcntl.LOCK_UN) + capacity.close() + fcntl.flock(case_lock, fcntl.LOCK_UN) + case_lock.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_export.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_export.py new file mode 100644 index 0000000000000000000000000000000000000000..c9b884c326dd7766b6307a032d5ab21c3c46d9d3 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_export.py @@ -0,0 +1,318 @@ +"""Verify schema/task evidence; export only after independent collision gates pass.""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + physical_verification=verify_physical_gate(case);assert physical_verification['passed'],physical_verification + frozen=out/'physical_evidence';audit=json.loads((frozen/'TASK_SCHEMA_AUDIT.json').read_text());assert audit['passed'],audit['failures'];assert audit['recording_sha256']==hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest() + for file,field,value in [('SELF_COLLISION_AUDIT.json','passed',True),('WORLD_COLLISION_AUDIT.json','geometry_status','CLEAR_AT_CHECKED_SAMPLES'),('WRIST_AUDIT.json','passed',True),('VISIBILITY_AUDIT.json','passed',True)]: + assert json.loads((frozen/file).read_text())[field]==value,file + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + dataset_destination=W/'staged_datasets'/f"layout_{job['layout_id']:02d}";temp=dataset_destination.with_name(dataset_destination.name+'.pending');assert not dataset_destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=True,gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=True,official_reader_pending=True) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + temp.rename(dataset_destination);return dataset_destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_staged.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_staged.py new file mode 100644 index 0000000000000000000000000000000000000000..88d5f8ee86d702f799a7b69c4d5b66d63e51f97c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/sim_info/episode_000000/validation_code/validate_staged.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +"""Admit a staged candidate to VALIDATION_READY after independent QA only.""" +import argparse +import hashlib +import json +import time +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from startup_validation import sha256, verify_physical_gate, write_json + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def load(path): + return json.loads(Path(path).read_text()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + args = parser.parse_args() + lid = f"{args.layout:02d}" + case = (W / "cases" / f"layout_{lid}").resolve() + out = case / "rollout" + dataset = W / "staged_datasets" / f"layout_{lid}" + sim = dataset / "sim_info/episode_000000" + evidence_dir = W / "qa" / f"layout_{lid}" / "visual_evidence" + panels_path = evidence_dir / "PANELS.json" + manual_path = W / "qa" / f"layout_{lid}" / "ROOT_MANUAL_REVIEW.json" + reader_path = W / "qa" / f"layout_{lid}" / "OFFICIAL_LEROBOT_READER_QA.json" + failures = [] + + physical = verify_physical_gate(case) + if not physical["passed"]: + failures.append("immutable_physical_gate") + provenance = load(sim / "STARTUP_PREROLL_PROVENANCE.json") + evidence_hashes = provenance.get("evidence_sha256", {}) + bad_evidence = [name for name, digest in evidence_hashes.items() + if not (sim / name).is_file() or sha256(sim / name) != digest] + if bad_evidence: + failures.append("exported_provenance:" + ",".join(bad_evidence)) + + panels = load(panels_path) + panel_hashes = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} + if any(not (evidence_dir / name).is_file() or sha256(evidence_dir / name) != digest + for name, digest in panel_hashes.items()): + failures.append("visual_panel_hashes") + manual = load(manual_path) + manual_ok = (manual.get("schema") == "ziche.multi10.root_manual_visual_review.v1" + and manual.get("layout_id") == args.layout and manual.get("passed") is True + and manual.get("panels") == panel_hashes + and manual.get("reviewed_all_three_views") is True + and manual.get("reviewed_first_retained_0p5s") is True + and manual.get("reviewed_every_phase_midpoint") is True) + if not manual_ok: + failures.append("manual_visual_review") + + reader = load(reader_path) + parquet = dataset / "data/chunk-000/episode_000000.parquet" + reader_ok = (reader.get("passed") is True and reader.get("layout_id") == args.layout + and reader.get("parquet_sha256") == sha256(parquet)) + if not reader_ok: + failures.append("official_lerobot_reader") + + decode = load(sim / "VIDEO_DECODE_AUDIT.json") + video_hashes = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + video_hashes[camera] = sha256(video) + if (video_hashes[camera] != provenance["video_sha256"].get(camera) + or video_hashes[camera] != decode["videos"][camera]["sha256"] + or decode["videos"][camera].get("passed") is not True): + failures.append("video:" + camera) + + table = pq.read_table(parquet) + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + samples = np.arange(2, len(recording["q"]), 3) + ticks = np.asarray(table["source_control_tick"].to_numpy(), dtype=np.int64) + items = np.asarray(table["item_index"].to_numpy(), dtype=np.int64) + clock = load(out / "PHYSICS_CLOCK.json") + sim_time = np.asarray(table["simulator_time_s"].to_numpy(), dtype=np.float64) + alignment = (len(table) == len(samples) and np.array_equal(ticks, samples + 1) + and np.array_equal(items, recording["item_index"][samples]) + and np.allclose(sim_time, float(clock["unrecorded_preroll_duration_s"]) + + (samples + 1) / 60.0, atol=2e-6, rtol=0)) + if not alignment: + failures.append("staged_source_alignment") + + report = {"schema": "ziche.multi10.staged_final_qa.v1", "layout_id": args.layout, + "passed": not failures, "failures": failures, "case": str(case), + "dataset": str(dataset), "physical_gate_verification": physical, + "provenance_sha256": sha256(sim / "STARTUP_PREROLL_PROVENANCE.json"), + "manual_review": {"path": str(manual_path), "sha256": sha256(manual_path), "passed": manual_ok}, + "official_reader": {"path": str(reader_path), "sha256": sha256(reader_path), "passed": reader_ok}, + "panels_receipt_sha256": sha256(panels_path), "video_sha256": video_hashes, + "parquet_sha256": sha256(parquet), "staged_source_alignment": alignment, + "status_semantics": "VALIDATION_READY is review complete but not publication or ACCEPTED", + "completed_unix": time.time()} + qa_path = W / "qa" / f"layout_{lid}.json" + write_json(qa_path, report) + if not report["passed"]: + raise SystemExit(1) + write_json(case / "STATUS.json", {"status": "VALIDATION_READY", "phase": "review_complete", + "accepted": False, "dataset": str(dataset), "qa": str(qa_path), + "qa_sha256": sha256(qa_path), "updated": time.time()}) + print(qa_path) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/source_meta/episodes.jsonl b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000007/source_meta/episodes.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2a4c2fe1ab499c6b4c33f28b89e364a00a765e72 --- /dev/null +++ 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+ ], + "tolerance_m": 0.001 + }, + "proposal_path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM02_FOCUSED_SIDE_APPROACH_PROPOSAL.json", + "proposal_sha256": "a81ec7e7ac7f34468ac7b14baeed7a86d53798f84a2d800f1c7d5d370b567b0d" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/635599c8143e80f5a4728b2951f6035b4d498d7b26a53569b989526071abf0ad.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/635599c8143e80f5a4728b2951f6035b4d498d7b26a53569b989526071abf0ad.py new file mode 100644 index 0000000000000000000000000000000000000000..19e6c7bd998231d15928e44491d66a016ccf9db8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/635599c8143e80f5a4728b2951f6035b4d498d7b26a53569b989526071abf0ad.py @@ -0,0 +1,335 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + if len(g["rows"]) != 28221 or g["item_index"] != 2: raise RuntimeError("Stale focused repair boundary") + g["solve"] = solve + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/ad191813c89892675c4aa6fe6e9d50b322a182dd984a0a56eecaea9830312b91.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/ad191813c89892675c4aa6fe6e9d50b322a182dd984a0a56eecaea9830312b91.json new file mode 100644 index 0000000000000000000000000000000000000000..ccd5618868060591e47d8ba80a88ad6077f5abf2 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/ad191813c89892675c4aa6fe6e9d50b322a182dd984a0a56eecaea9830312b91.json @@ -0,0 +1,148 @@ +{ + "schema": "ziche.multi_item_place_station_repair.v1", + "reason": "Current frozen boundary qualified for carry, compact bridge, full placement and release sweep; retry remains subject to live native checks.", + "expected_boundary_tick": 14564, + "expected_item_index": 1, + "expected_phase": "item_01_place_planning", + "repair_module": "repair_place_station_item01_tick14564_2089e9ea218a.py", + "repair_module_sha256": "bd6845ed2cdad04a25d456e65d1d8694826077e503f4bb21e522bbaf3f495219", + "proposal": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json", + "proposal_sha256": "2089e9ea218a154e3b2bc97e9019b65ae0f827519ff1bcaf29074254e8eaefd5", + "job_overrides": { + "placement_slot": { + "xy": [ + -1.2312090794126613, + 0.4599218654290152 + ], + "yaw_rad": -1.6091936281181196, + "footprint_xy": [ + [ + -1.253785518363411, + 0.39323937336010767 + ], + [ + -1.213815002689118, + 0.3917038587214576 + ], + [ + -1.208632640461912, + 0.5266043574979223 + ], + [ + -1.2486031561362048, + 0.5281398721365723 + ] + ], + "tolerance_m": 0.001 + }, + "destination_xyz": [ + -1.2312090794126613, + 0.4599218654290152, + 0.45490211957462423 + ], + "place_base": [ + [ + 0.3090169943749472, + 0.9510565162951536, + 0.0, + -1.6412090794126613 + ], + [ + -0.9510565162951536, + 0.3090169943749472, + -0.0, + 1.170062696532255 + ], + [ + -0.0, + 0.0, + 1.0, + 0.0013677068054676056 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "repair_station": { + "place_base": [ + [ + 0.3090169943749472, + 0.9510565162951536, + 0.0, + -1.6412090794126613 + ], + [ + -0.9510565162951536, + 0.3090169943749472, + -0.0, + 1.170062696532255 + ], + [ + -0.0, + 0.0, + 1.0, + 0.0013677068054676056 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "place_initial20": [ + -0.539791239092212, + 1.3858703117925886, + -0.15029005595389927, + -0.9991244085537674, + 0.7872083684050362, + 0.8063198037460786, + 0.7546000684635557, + 1.0847647650524297, + -0.6783847727599561, + -0.3371089766507753, + 1.2578689005534816, + -0.6790028033712651, + -1.459364109043261, + 0.24410749898484435, + 0.007616361603140831, + 0.5235987755982988, + 0.0, + 0.4363323129985824, + 0.2285582274198532, + 0.7843977808952332 + ], + "placement_slot": { + "xy": [ + -1.2312090794126613, + 0.4599218654290152 + ], + "yaw_rad": -1.6091936281181196, + "footprint_xy": [ + [ + -1.253785518363411, + 0.39323937336010767 + ], + [ + -1.213815002689118, + 0.3917038587214576 + ], + [ + -1.208632640461912, + 0.5266043574979223 + ], + [ + -1.2486031561362048, + 0.5281398721365723 + ] + ], + "tolerance_m": 0.001 + }, + "proposal_path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json", + "proposal_sha256": "2089e9ea218a154e3b2bc97e9019b65ae0f827519ff1bcaf29074254e8eaefd5" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/bd6845ed2cdad04a25d456e65d1d8694826077e503f4bb21e522bbaf3f495219.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/bd6845ed2cdad04a25d456e65d1d8694826077e503f4bb21e522bbaf3f495219.py new file mode 100644 index 0000000000000000000000000000000000000000..7ca36b883d8fd0e123c38d6919e8e87715f1e72b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/applied/bd6845ed2cdad04a25d456e65d1d8694826077e503f4bb21e522bbaf3f495219.py @@ -0,0 +1,334 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + g["solve"] = solve + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_01_place.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_01_place.json new file mode 100644 index 0000000000000000000000000000000000000000..ccd5618868060591e47d8ba80a88ad6077f5abf2 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_01_place.json @@ -0,0 +1,148 @@ +{ + "schema": "ziche.multi_item_place_station_repair.v1", + "reason": "Current frozen boundary qualified for carry, compact bridge, full placement and release sweep; retry remains subject to live native checks.", + "expected_boundary_tick": 14564, + "expected_item_index": 1, + "expected_phase": "item_01_place_planning", + "repair_module": "repair_place_station_item01_tick14564_2089e9ea218a.py", + "repair_module_sha256": "bd6845ed2cdad04a25d456e65d1d8694826077e503f4bb21e522bbaf3f495219", + "proposal": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json", + "proposal_sha256": "2089e9ea218a154e3b2bc97e9019b65ae0f827519ff1bcaf29074254e8eaefd5", + "job_overrides": { + "placement_slot": { + "xy": [ + -1.2312090794126613, + 0.4599218654290152 + ], + "yaw_rad": -1.6091936281181196, + "footprint_xy": [ + [ + -1.253785518363411, + 0.39323937336010767 + ], + [ + -1.213815002689118, + 0.3917038587214576 + ], + [ + -1.208632640461912, + 0.5266043574979223 + ], + [ + -1.2486031561362048, + 0.5281398721365723 + ] + ], + "tolerance_m": 0.001 + }, + "destination_xyz": [ + -1.2312090794126613, + 0.4599218654290152, + 0.45490211957462423 + ], + "place_base": [ + [ + 0.3090169943749472, + 0.9510565162951536, + 0.0, + -1.6412090794126613 + ], + [ + -0.9510565162951536, + 0.3090169943749472, + -0.0, + 1.170062696532255 + ], + [ + -0.0, + 0.0, + 1.0, + 0.0013677068054676056 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "repair_station": { + "place_base": [ + [ + 0.3090169943749472, + 0.9510565162951536, + 0.0, + -1.6412090794126613 + ], + [ + -0.9510565162951536, + 0.3090169943749472, + -0.0, + 1.170062696532255 + ], + [ + -0.0, + 0.0, + 1.0, + 0.0013677068054676056 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "place_initial20": [ + -0.539791239092212, + 1.3858703117925886, + -0.15029005595389927, + -0.9991244085537674, + 0.7872083684050362, + 0.8063198037460786, + 0.7546000684635557, + 1.0847647650524297, + -0.6783847727599561, + -0.3371089766507753, + 1.2578689005534816, + -0.6790028033712651, + -1.459364109043261, + 0.24410749898484435, + 0.007616361603140831, + 0.5235987755982988, + 0.0, + 0.4363323129985824, + 0.2285582274198532, + 0.7843977808952332 + ], + "placement_slot": { + "xy": [ + -1.2312090794126613, + 0.4599218654290152 + ], + "yaw_rad": -1.6091936281181196, + "footprint_xy": [ + [ + -1.253785518363411, + 0.39323937336010767 + ], + [ + -1.213815002689118, + 0.3917038587214576 + ], + [ + -1.208632640461912, + 0.5266043574979223 + ], + [ + -1.2486031561362048, + 0.5281398721365723 + ] + ], + "tolerance_m": 0.001 + }, + "proposal_path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM01_POLAR_RETREAT_VARIANT_PROPOSAL.json", + "proposal_sha256": "2089e9ea218a154e3b2bc97e9019b65ae0f827519ff1bcaf29074254e8eaefd5" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_02_place.json b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_02_place.json new file mode 100644 index 0000000000000000000000000000000000000000..4db90d7ee0c64f0afb0fa9e397145bdc6a51aec4 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/item_02_place.json @@ -0,0 +1,148 @@ +{ + "schema": "ziche.multi_item_place_station_repair.v1", + "reason": "Sequential fine repair of layout09 final Honey Maid: right-side approach and changed free tray slot; current live carry, bridge, complete placement and open-gripper sweep all clear.", + "expected_boundary_tick": 28221, + "expected_item_index": 2, + "expected_phase": "item_02_place_planning", + "repair_module": "repair_case09_final_side_tick28221_a81ec7e7ac7f.py", + "repair_module_sha256": "635599c8143e80f5a4728b2951f6035b4d498d7b26a53569b989526071abf0ad", + "proposal": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM02_FOCUSED_SIDE_APPROACH_PROPOSAL.json", + "proposal_sha256": "a81ec7e7ac7f34468ac7b14baeed7a86d53798f84a2d800f1c7d5d370b567b0d", + "job_overrides": { + "placement_slot": { + "xy": [ + -1.029549989795302, + 0.4993426681659436 + ], + "yaw_rad": 1.4912138670372856, + "footprint_xy": [ + [ + -1.0569474330311097, + 0.4388291973980737 + ], + [ + -1.012089856833125, + 0.43525176555741274 + ], + [ + -1.0021525465594945, + 0.5598561389338139 + ], + [ + -1.047010122757479, + 0.5634335707744749 + ] + ], + "tolerance_m": 0.001 + }, + "destination_xyz": [ + -1.029549989795302, + 0.4993426681659436, + 0.45490211957462423 + ], + "place_base": [ + [ + -0.999111320717128, + 0.04214936316097839, + 0.0, + -0.4026868135177334 + ], + [ + -0.04214936316097839, + -0.999111320717128, + 0.0, + 0.4226927374856806 + ], + [ + 0.0, + 0.0, + 1.0, + 0.0013683382421731949 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "repair_station": { + "place_base": [ + [ + -0.999111320717128, + 0.04214936316097839, + 0.0, + -0.4026868135177334 + ], + [ + -0.04214936316097839, + -0.999111320717128, + 0.0, + 0.4226927374856806 + ], + [ + 0.0, + 0.0, + 1.0, + 0.0013683382421731949 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "place_initial20": [ + -0.9242413640022278, + 0.5412313938140869, + 0.17275646328926086, + -1.3104835748672485, + 0.5802505612373352, + 1.4175702333450317, + -0.15734726190567017, + 1.0683540105819702, + -0.5972049236297607, + -0.2661028206348419, + 1.3510088920593262, + -0.5712903738021851, + -1.4610986709594727, + 0.17026785016059875, + 0.0075940657407045364, + 0.6108652381980153, + -3.6488795558398124e-06, + 0.43633878231048584, + 0.7844204306602478, + 0.26138609647750854 + ], + "placement_slot": { + "xy": [ + -1.029549989795302, + 0.4993426681659436 + ], + "yaw_rad": 1.4912138670372856, + "footprint_xy": [ + [ + -1.0569474330311097, + 0.4388291973980737 + ], + [ + -1.012089856833125, + 0.43525176555741274 + ], + [ + -1.0021525465594945, + 0.5598561389338139 + ], + [ + -1.047010122757479, + 0.5634335707744749 + ] + ], + "tolerance_m": 0.001 + }, + "proposal_path": "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006/cases/layout_09/CASE9_ITEM02_FOCUSED_SIDE_APPROACH_PROPOSAL.json", + "proposal_sha256": "a81ec7e7ac7f34468ac7b14baeed7a86d53798f84a2d800f1c7d5d370b567b0d" + } + } +} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_case09_final_side_tick28221_a81ec7e7ac7f.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_case09_final_side_tick28221_a81ec7e7ac7f.py new file mode 100644 index 0000000000000000000000000000000000000000..19e6c7bd998231d15928e44491d66a016ccf9db8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_case09_final_side_tick28221_a81ec7e7ac7f.py @@ -0,0 +1,335 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + if len(g["rows"]) != 28221 or g["item_index"] != 2: raise RuntimeError("Stale focused repair boundary") + g["solve"] = solve + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_place_station_item01_tick14564_2089e9ea218a.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_place_station_item01_tick14564_2089e9ea218a.py new file mode 100644 index 0000000000000000000000000000000000000000..7ca36b883d8fd0e123c38d6919e8e87715f1e72b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/repairs/repair_place_station_item01_tick14564_2089e9ea218a.py @@ -0,0 +1,334 @@ +"""Measured-grasp placement with the existing supported tray candidate policy.""" +import sys,os,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +sys.path.insert(0,str(R/'work/place_training_tray_20260918')) +from layout_policy import destination_candidates +from ik_solver import Arm + +def solve(robot,names,q0,base,object_pose,vertices,job,obstacles,pose_check=None,world_check=None,release_sweep=None): + q0=np.asarray(q0,dtype=np.float64).copy();hand=job['hand'];link=robot.root+'/gripper_'+hand+'_base_link' + active=[names.index(f'idx{2 if hand=="l" else 6}{k}_arm_{hand}_joint{k}') for k in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + held=np.linalg.inv(robot.fk(names,q0,base)[link])@object_pose;invheld=np.linalg.inv(held) + yaw=Rot.from_matrix(object_pose[:3,:3]).as_euler('xyz')[2];floor=Polygon(job['tray_polygon_xy']) + reserved=[Polygon(x) for x in job.get('reserved_tray_footprints',[])] + all_obstacles=list(obstacles)+reserved + slot=job.get('placement_slot') + if slot: + # Every other planned slot remains available for the subsequent trip. + rng=np.random.default_rng(int(job['seed'])+391) + candidates=[];center=np.asarray(slot['xy'],float) + desired=float(slot['yaw_rad']);tol=float(slot.get('tolerance_m',.02)) + yaw_options=[yaw,desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2] + candidate_budget=int(job.get("placement_candidate_budget",800)) + if not 5<=candidate_budget<=800:raise ValueError("placement_candidate_budget must be between 5 and 800") + for index in range(candidate_budget): + xy=center if index=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + hand_goal=goal@invheld + toward=base[:2,3]-hand_goal[:2,3];toward= toward/max(np.linalg.norm(toward),1e-9) + retreat_deltas=[np.array([0.,0.,.15])] + # Fold the open hand toward the base when the fixed upward retreat is unreachable. + # Every candidate retains full self, world and opening/retreat sweep checks. + for back,up in [(.15,.10),(.20,.05),(.24,.02),(.12,.15)]: + retreat_deltas.append(np.array([toward[0]*back,toward[1]*back,up])) + for delta in retreat_deltas: + retreat_goal=hand_goal.copy();retreat_goal[:3,3]+=delta;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,retreat_direction_m=delta.tolist(),candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) + +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + g["solve"] = solve + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/apply_exact_landing_overlay.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/apply_exact_landing_overlay.py new file mode 100644 index 0000000000000000000000000000000000000000..fef605610b90429a889948f6b651aa5e61f3a462 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/apply_exact_landing_overlay.py @@ -0,0 +1,15 @@ +"""Mechanically apply a qualified exact-chain first landing and its verified repack.""" +import sys,json +from pathlib import Path +W=Path(__file__).resolve().parent +for cid in map(int,sys.argv[1:]): + case=W/'cases'/f'layout_{cid:02d}'; jp=case/'job.json'; before=case/'PRE_ALIGNMENT.json'; job=json.loads(jp.read_text()); proposal=json.loads((case/'EXACT_CHAIN_LANDING_PROPOSAL.json').read_text()) + if not proposal.get('passed') or not proposal.get('floor_inset8_covers') or min(proposal['future_gaps_after_repack'])<=.012:raise ValueError(f'Unqualified proposal {cid}') + if not before.exists():before.write_text(json.dumps(job,indent=2)+'\n') + slots=proposal['proposed_slots']; + if len(slots)!=len(job['items']):raise ValueError('slot count') + base=proposal['base'];base[2][3]=job['items'][0]['place_base'][2][3] + job['items'][0]['place_base']=base;job['items'][0]['station_alignment_policy']='transferred_actual_chain_goal_exact_v1';job['items'][0]['placement_slot']=slots[0];job['items'][0]['destination_xyz']=[*slots[0]['xy'],job['tray_floor_z']] + for item,slot in zip(job['items'][1:],slots[1:]):item['placement_slot']=slot;item['destination_xyz']=[*slot['xy'],job['tray_floor_z']] + job['place_base']=base;job['destination_xyz']=job['items'][0]['destination_xyz'];job['tray_slots']=slots;job['exact_chain_landing_proposal']=str(case/'EXACT_CHAIN_LANDING_PROPOSAL.json');job['pre_alignment_job']=str(before) + jp.write_text(json.dumps(job,indent=2)+'\n');print(jp) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_geometry.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..3084090fcc78ffc9efe2c5090ff11c5b0b8bd72a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_geometry.py @@ -0,0 +1,118 @@ +"""Use existing exact/native sampled collision gates for this continuous task.""" +import sys,json,os,subprocess +from pathlib import Path +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]).resolve();out=case/'rollout';kind=sys.argv[2] +if kind=='world' and 'CHAIN_AUDIT_SHARD' not in os.environ: + sys.exit(subprocess.call([sys.executable,str(W/'parallel_world_audit.py'),str(case)])) +shard=int(os.environ.get('CHAIN_AUDIT_SHARD','-1')) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop'),str(R/'work/place_training_tray_20260918')] +job=json.loads((case/'job.json').read_text()) +from multi_validation import items +requested=items(job);requested_targets=[row['target'] for row in requested] +# Optional contemporary cooking representations refine conservative envelopes. +# All actual geometry, filters and tolerances remain unchanged. +import collision_scene as cs +cs.STOCK=list(dict.fromkeys(cs.STOCK+job.get('clutter',[])+[path for row in requested for path in row.get('clutter',[])])) +adapter=None +if (out/'collision_readback/RESULT.json').exists(): + import importlib.util,types,numpy as np + from pxr import Usd + path=R/'work/omni_pick_stage1_20260914/native_cooking_readback/nonrobot_coverage/analysis_extension/adapter_extra.py' + spec=importlib.util.spec_from_file_location('chain_captured_geometry',path);module=importlib.util.module_from_spec(spec) + # This task stores scene/readbacks under case/rollout, with runtime root in case. + # Validate exact same-case paths instead of the legacy directory nesting. + source=path.read_text() + legacy="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve().parent!=snapshot.resolve().parent or snapshot.resolve().parent!=self.readback.parent.parent:raise GeometryReview('Readbacks and snapshot belong to different capture directories')" + current="if not stage.GetRootLayer().realPath or Path(data['root_layer']).resolve()!=_chain_runtime_root or snapshot.resolve()!=_chain_snapshot or self.readback.parent!=_chain_output or self.native_readback.parent!=_chain_output:raise GeometryReview('Readbacks and snapshot do not match this chain capture')" + assert source.count(legacy)==1 + module.__dict__.update(_chain_runtime_root=Path(job['scene']).resolve(),_chain_snapshot=(out/'scene.usda').resolve(),_chain_output=out.resolve()) + exec(compile(source.replace(legacy,current),str(path),'exec'),module.__dict__) + from collision_inventory import enabled_nonrobot_convex_colliders + module.export.enabled_nonrobot_convex_colliders=enabled_nonrobot_convex_colliders + original_normalize=module.normalize_rigid_mount + def normalize_roundoff(prim,current,captured,cache): + try:return original_normalize(prim,current,captured,cache) + except module.GeometryReview: + before=np.asarray(current['collider_to_rigid_owner_column_matrix'],float);after=np.asarray(captured['collider_to_rigid_owner_column_matrix'],float);points=np.asarray(prim.GetAttribute('points').Get(),float) + if points.ndim!=2 or points.shape[1]!=3:raise + coefficient=float(np.max(abs(before-after)));displacement=float(np.linalg.norm((np.c_[points,np.ones(len(points))]@(before-after).T)[:,:3],axis=1).max()) + if coefficient>1e-10 or displacement>1e-9:raise + return dict(method='bounded_float64_matrix_chain_roundoff',normalized_mount=before.tolist(),raw_end_mount=after.tolist(),raw_end_mount_max_coefficient_difference=coefficient,maximum_geometry_displacement_m=displacement,coefficient_bound=1e-10,geometry_displacement_bound_m=1e-9,normalized_model_is_live_PxMeshScale_identity=False,source_geometry_modified=False) + module.normalize_rigid_mount=normalize_roundoff + stage=Usd.Stage.Open(str(out/'scene.usda'));metadata=np.load(out/'full_state.npz',allow_pickle=False) + wrapped,AnalysisBody,adapter=module.build_extended_adapter(stage,out/'collision_readback',out/'collision_readback_nonrobot',metadata) + (out/('COOKED_ADAPTER_COVERAGE.json' if shard<0 else f'COOKED_ADAPTER_COVERAGE.part_{shard}.json')).write_text(json.dumps(adapter.receipt,indent=2)+'\n') + cs.collider_mesh=wrapped;cs.static_collision_mesh=wrapped;cs.Body=AnalysisBody + +if kind=='self': + import omni_native_audit as native + if adapter is not None:native.trimesh=types.SimpleNamespace(util=types.SimpleNamespace(concatenate=adapter.combine)) + native.audit(out/'physics_ticks/full_state.npz',out/'scene.usda',out/'SELF_COLLISION_AUDIT.json') +elif kind=='world': + item_index=int(os.environ['CHAIN_AUDIT_ITEM']);item=requested[item_index] + audit_target=item['target'];audit_hand=item['hand'] + import collision_initial_layout + from place_tray_layout import scene_geometry + from shapely.geometry import Polygon + original_surfaces=collision_initial_layout._surfaces + def place_surfaces(scene): + surfaces=original_surfaces(scene);geometry=scene_geometry(scene.stage,audit_target);top=float(geometry['tray_xyz'][2]+geometry['tray_floor_z']) + assert abs(top-job['tray_floor_z'])<1e-5 + surfaces.append(dict(top=top,polygon=Polygon(geometry['tray_floor']+geometry['tray_xyz'][:2]),paths=['/World/Assets/Tray']));return surfaces + collision_initial_layout._surfaces=place_surfaces + original_support=cs.is_shelf_support_body + cs.is_shelf_support_body=lambda body:original_support(body) or body.name.startswith('/World/Assets/Tray/') + import collision_sweep as sweep + from contact_policy import install + spec=dict(target=dict(prim_path=audit_target),hand=audit_hand) + import collision_scene as cs + original_static=cs.include_static_collision_geometry + def static_inventory(scene): + result=original_static(scene);templates=[];unresolved=[] + for path in result['undefined_active_enabled_static_colliders']: + prim=scene.stage.GetPrimAtPath(path) + if path.startswith('/Flattened_Prototype_') and not prim.IsDefined():templates.append(path) + else:unresolved.append(path) + result['nonphysical_undefined_template_opinions']=templates;result['undefined_active_enabled_static_colliders']=unresolved;result['complete']=not unresolved + return result + cs.include_static_collision_geometry=static_inventory + (out/f'place_metrics.part_{shard}.json').write_text(json.dumps(dict(item_index=item_index,target=audit_target,hand=audit_hand,destination_body_path='/World/Assets/Tray'))) + import inspect,time + install(sweep,spec) + import collision_acceptance + from task_collision_scope import install as install_task_scope + from task_collision_scope import nonphysical_rug_paths + omitted_rug_paths=set(nonphysical_rug_paths(stage)) + install_task_scope(collision_acceptance,requested_targets,omitted_rug_paths) + source=inspect.getsource(sweep.episode) + target_line=" target_name=str(d['target_prim']); source=_SCENE.objects.get(target_name)" + assert source.count(target_line)==1 + source=source.replace(target_line," target_name=_chain_target; source=_SCENE.objects.get(target_name)") + hand_line=" hand='l'" + assert source.count(hand_line)==1 + source=source.replace(hand_line," hand=_chain_hand",1) + support_line=" support_name=place_metadata.get('destination_body_path') or (str(d['current_furniture_body_path']) if 'current_furniture_body_path' in d else '')" + assert source.count(support_line)==1 + source=source.replace(support_line," hand=_chain_hand\n"+support_line) + begin=source.index(' def register(');end=source.index(' frame_ids=',begin) + register=source[begin:end].replace('_OPTIONS.contact_tolerance_mm','contact_limit') + register=register.replace(' nonlocal exact,cache_hits'," nonlocal exact,cache_hits\n if a.name.split('#')[0] in _chain_nonphysical_rug or b.name.split('#')[0] in _chain_nonphysical_rug:return") + sweep.__dict__['_chain_nonphysical_rug']=omitted_rug_paths + marker_limit=" if contact_limit is not None and row['category'].endswith('_requires_depth_review'):" + register=register.replace(marker_limit," contact_limit=_place_contact_limit(row,_OPTIONS.contact_tolerance_mm)\n"+marker_limit) + source=source[:begin]+register+source[end:] + marker=' for fi,f in enumerate(frame_ids):' + source=source.replace(marker,marker+"\n if not _chain_start<=fi<_chain_end:continue\n _chain_checked.append(fi)\n if fi%30==0:_chain_audit_progress(fi,len(frame_ids))") + def progress(frame,total): + path=out/f'WORLD_AUDIT_PROGRESS.part_{shard}.json';tmp=path.with_suffix('.tmp');tmp.write_text(json.dumps(dict(frame=frame,total=total,updated=time.time())));tmp.replace(path) + sweep.__dict__.update(_chain_audit_progress=progress,_chain_start=int(os.environ['CHAIN_AUDIT_START']),_chain_end=int(os.environ['CHAIN_AUDIT_END']),_chain_checked=[],_chain_target=audit_target,_chain_hand=audit_hand) + exec(compile(source,sweep.__file__,'exec'),sweep.__dict__) + from types import SimpleNamespace + from collision_scene import ROBOT + options=SimpleNamespace(scene=str(out/'scene.usda'),robot=str(ROBOT),output=str(out/'world_geometry'/f'part_{shard}'),scene_per_episode=False,authored_active=True,native_robot_world=True,swept=True,all_scene=False,solid_containment=True,stop_on_forbidden=True,depth=True,depth_points=512,contact_tolerance_mm=.5,stride=1,joint_step=.03,target_step=.005) + sweep.init(options);report=sweep.episode(out/'physics_ticks') + from collision_acceptance import summarize + full_report=json.loads(Path(report['output']).read_text());result=full_report['native_world_geometry_summary'] + result.update(partial_report=True,shard=shard,item_index=item_index,active_target=audit_target,active_hand=audit_hand,checked_frame_indices=sweep.__dict__['_chain_checked'],start=int(os.environ['CHAIN_AUDIT_START']),end=int(os.environ['CHAIN_AUDIT_END']),elapsed_seconds=full_report['elapsed_seconds'],source_geometry_report=report['output']) + (out/f'WORLD_COLLISION_AUDIT.part_{shard}.json').write_text(json.dumps(result,indent=2)+'\n') +else:raise ValueError(kind) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_return_head.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_return_head.py new file mode 100644 index 0000000000000000000000000000000000000000..189b0ade576f8c942f52f71fea48c2d61ccef60e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_return_head.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +"""Audit recorded level gaze during every empty-hand return. + +This is deliberately separate from the frozen validation release. It reads a +completed native trace and the producer policy/transition receipts, writes one +case-local report, and never changes task acceptance or source recordings. +""" +from __future__ import annotations + +import hashlib +import json +import os +import sys +from pathlib import Path + +import numpy as np + +POLICY = "empty_return_forward_head_v3" +HEAD_JOINTS = ("idx11_head_joint1", "idx12_head_joint2") +COMPENSATED_BODY_JOINT = "idx02_body_joint2" +PITCH_CLIP_RAD = np.asarray((-0.34033920413889424, 0.5148721293383273), dtype=np.float64) +MOVING_EPS = 1e-8 +COMMAND_EPS_RAD = 1e-10 +ACTUAL_EPS_RAD = float(np.deg2rad(0.1)) + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + pending = path.with_suffix(path.suffix + f".pending.{os.getpid()}") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def expected_head(body_pitch): + """Return [yaw, pitch] for the most forward feasible gaze at each body pitch.""" + body_pitch = np.asarray(body_pitch, dtype=np.float64) + result = np.zeros(body_pitch.shape + (2,), dtype=np.float64) + result[..., 1] = np.clip(-body_pitch, PITCH_CLIP_RAD[0], PITCH_CLIP_RAD[1]) + return result + + +def audit(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + recording_path = out / "recording_60hz.npz" + policy_path = out / "RETURN_HEAD_POLICY.json" + failures = [] + try: + recording = np.load(recording_path, allow_pickle=False) + policy = json.loads(policy_path.read_text()) + names = recording["dof_names"].tolist() + head_indices = [names.index(name) for name in HEAD_JOINTS] + body_index = names.index(COMPENSATED_BODY_JOINT) + command = np.asarray(recording["native_target"][:, head_indices], dtype=np.float64) + actual = np.asarray(recording["q"][:, head_indices], dtype=np.float64) + body_command = np.asarray(recording["native_target"][:, body_index], dtype=np.float64) + body_actual = np.asarray(recording["q"][:, body_index], dtype=np.float64) + base_action = np.asarray(recording["base_action_body"], dtype=np.float64) + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + ticks = np.asarray(recording["tick"], dtype=np.int64) + except Exception as exc: + return {"schema": "ziche.multi10.return_forward_head_audit.v3", "passed": False, + "case": str(case), "failures": ["missing_or_invalid_recording_or_policy"], + "error": repr(exc)} + + rows = len(phases) + basic_checks = { + "aligned_arrays": command.shape == actual.shape == (rows, 2) + and base_action.shape == (rows, 2) + and len(item_index) == rows, + "finite": bool(np.isfinite(command).all() and np.isfinite(actual).all() + and np.isfinite(base_action).all()), + "tick_sequence": np.array_equal(ticks, np.arange(1, rows + 1)), + "policy_name": policy.get("policy") == POLICY, + "head_joint_scope": policy.get("head_joints") == list(HEAD_JOINTS), + "compensated_body_joint": policy.get("compensate_body_joint") == COMPENSATED_BODY_JOINT, + "pitch_clip": np.array_equal(np.asarray(policy.get("pitch_clip_rad"), dtype=np.float64), + PITCH_CLIP_RAD), + "restore_stopped": policy.get("restore_only_while_stopped_at_shelf") is True, + } + failures.extend(name for name, passed in basic_checks.items() if not passed) + installed_tick = int(policy.get("installed_at_control_tick", -1)) + implementation = Path(policy.get("implementation_path", "")) + implementation_check = (implementation.is_file() + and policy.get("implementation_sha256") == sha256(implementation)) + if not implementation_check: + failures.append("policy_implementation_hash") + + empty = np.asarray([phase.endswith("_empty_return") for phase in phases]) + moving = np.max(np.abs(base_action), axis=1) > MOVING_EPS + moving_return = empty & moving + expected_command = expected_head(body_command) + expected_actual = expected_head(body_actual) + command_error = np.max(np.abs(command - expected_command), axis=1) + actual_error = np.max(np.abs(actual - expected_actual), axis=1) + optical_pitch_command = -(body_command + command[:, 1]) + optical_pitch_actual = -(body_actual + actual[:, 1]) + moving_command_bad = np.flatnonzero(moving_return & (command_error > COMMAND_EPS_RAD)) + moving_actual_bad = np.flatnonzero(moving_return & (actual_error > ACTUAL_EPS_RAD)) + if len(moving_command_bad): + failures.append("moving_return_head_command_not_body_pitch_compensated") + if len(moving_actual_bad): + failures.append("moving_return_head_actual_error") + + # Every declared head raise/restore transition must be recorded while the + # base is stopped. Ranges use zero-based row boundaries: start inclusive, + # end exclusive, matching producer len(rows) values around blend(). + transition_rows = np.zeros(rows, dtype=bool) + transitions = [] + receipt_paths = sorted(out.glob("RETURN_HEAD_TRANSITION.item_*.json")) + for path in receipt_paths: + row_failures = [] + try: + receipt = json.loads(path.read_text()) + start = int(receipt["start_tick"]) + end = int(receipt["end_tick"]) + kind = receipt["kind"] + valid_range = 0 <= start < end <= rows + if valid_range: + selected = np.arange(start, end) + transition_rows[selected] = True + stopped = bool(np.all(np.max(np.abs(base_action[selected]), axis=1) <= MOVING_EPS)) + correct_phase = bool(np.all(empty[selected])) + correct_item = bool(np.all(item_index[selected] == int(receipt["item_index"]))) + item_moving = np.flatnonzero(moving_return & (item_index == int(receipt["item_index"]))) + if kind == "raise_before_walking": + temporal = len(item_moving) > 0 and end <= int(item_moving[0]) + target_value = np.asarray(receipt.get("target_rad", []), dtype=float) + start_target = expected_head(body_command[start]) if valid_range else np.asarray([np.nan, np.nan]) + kind_target = target_value.shape == (2,) and np.max(np.abs(target_value - start_target)) <= COMMAND_EPS_RAD + body_constant = valid_range and np.max(np.abs(body_command[selected] - body_command[start])) <= COMMAND_EPS_RAD + else: + temporal = len(item_moving) > 0 and start > int(item_moving[-1]) + kind_target = np.asarray(receipt.get("target_rad", [])).shape == (2,) + body_constant = True + else: + selected = np.asarray([], dtype=int) + stopped = correct_phase = correct_item = temporal = kind_target = body_constant = False + source = Path(receipt.get("source_path", "")) + receipt_checks = { + "schema": receipt.get("schema") == "multi_return_head_transition_v1", + "policy": receipt.get("policy") == POLICY, + "kind": kind in {"raise_before_walking", "restore_pick_gaze_after_arrival"}, + "range": valid_range, + "after_install": valid_range and start >= installed_tick, + "base_stopped": stopped and receipt.get("base_held") is True, + "empty_return_phase": correct_phase, + "item_alignment": correct_item, + "before_or_after_all_item_base_motion": temporal, + "raise_targets_level_or_restore_has_two_targets": kind_target, + "raise_body_pitch_constant": body_constant, + "head_joint_scope": receipt.get("head_joints") == list(HEAD_JOINTS), + "target_shape": np.asarray(receipt.get("target_rad", [])).shape == (2,), + "screened": receipt.get("all_live_props_screened") is True, + "no_pose_writes": receipt.get("no_pose_writes") is True, + "source_hash": source.is_file() and receipt.get("source_sha256") == sha256(source), + } + row_failures = [name for name, passed in receipt_checks.items() if not passed] + except Exception as exc: + start = end = None + kind = None + receipt_checks = {} + row_failures = ["invalid_receipt:" + repr(exc)] + if row_failures: + failures.append("transition:" + path.name) + transitions.append({"path": str(path), "sha256": sha256(path), "kind": kind, + "start_tick": start, "end_tick": end, + "checks": receipt_checks, "failures": row_failures}) + + # A different commanded gaze while stationary is permitted for the stopped + # waist/arm preparation preceding the walk. The explicit transition + # receipts prove the policy's raise and restore operations themselves never + # overlap base motion; all base-moving rows are governed by the stricter + # zero-command/0.1-degree checks above. + unexpected_transition_motion = np.flatnonzero(transition_rows & moving) + if len(unexpected_transition_motion): + failures.append("head_transition_overlaps_base_motion") + + return_items = sorted(set(item_index[empty].tolist())) + moving_return_items = sorted(set(item_index[moving_return].tolist())) + nonfinal_expected = list(range(max(0, int(item_index.max(initial=-1))))) + return_scope_complete = (return_items == nonfinal_expected + and moving_return_items == nonfinal_expected + and len(moving_return) > 0) + if not return_scope_complete: + failures.append("empty_return_item_scope") + + preinstall_return = np.flatnonzero(empty & (np.arange(rows) < installed_tick)) + postinstall_moving = moving_return & (np.arange(rows) >= max(0, installed_tick)) + postinstall_compliant = (not np.any(postinstall_moving & (command_error > COMMAND_EPS_RAD)) + and not np.any(postinstall_moving & (actual_error > ACTUAL_EPS_RAD))) + full_trace_compliant = (len(moving_command_bad) == 0 and len(moving_actual_bad) == 0 + and len(unexpected_transition_motion) == 0 and return_scope_complete) + installed_before_recording = installed_tick == 0 + if not installed_before_recording: + failures.append("fresh_episode_policy_not_installed_before_recording") + + def witnesses(indices): + return [{"row_index": int(index), "source_control_tick": int(index + 1), + "phase": phases[index], "item_index": int(item_index[index]), + "base_action_body": base_action[index].tolist(), + "head_command_rad": command[index].tolist(), + "head_actual_rad": actual[index].tolist(), + "body2_command_rad": float(body_command[index]), + "body2_actual_rad": float(body_actual[index]), + "expected_head_command_rad": expected_command[index].tolist(), + "expected_head_from_actual_body_rad": expected_actual[index].tolist(), + "command_max_error_rad": float(command_error[index]), + "actual_max_error_deg": float(np.degrees(actual_error[index])), + "predicted_optical_pitch_command_deg": float(np.degrees(optical_pitch_command[index])), + "predicted_optical_pitch_actual_deg": float(np.degrees(optical_pitch_actual[index]))} + for index in indices[:50]] + + report = { + "schema": "ziche.multi10.return_forward_head_audit.v3", "passed": not failures, + "case": str(case), "policy": POLICY, "policy_installed_at_control_tick": installed_tick, + "classification": ("FULL_FRESH_EPISODE_COMPLIANT" if installed_before_recording and full_trace_compliant + else "FULL_TRACE_COMPLIANT_LATE_INSTALL_NOT_FRESH_ELIGIBLE" if full_trace_compliant + else "POST_INSTALL_ONLY" if postinstall_compliant else "NONCOMPLIANT"), + "fresh_episode_required": True, "installed_before_recording": installed_before_recording, + "full_trace_compliant": full_trace_compliant, "postinstall_trace_compliant": postinstall_compliant, + "fixed_expected_metadata": { + "head_joints": list(HEAD_JOINTS), "compensate_body_joint": COMPENSATED_BODY_JOINT, + "pitch_clip_rad": PITCH_CLIP_RAD.tolist(), "pitch_clip_deg": np.degrees(PITCH_CLIP_RAD).tolist(), + "command_formula": "head_yaw=0; head_pitch=clip(-commanded_body2,-19.5deg,+29.5deg)", + "actual_formula": "expected_head_pitch=clip(-measured_body2,-19.5deg,+29.5deg); actual joint tracking error <=0.1deg", + "predicted_optical_pitch_formula": "-(body2+head_pitch)", + "correction": "v2 incorrectly treated camera pitch as a fixed head-only target. v3 accounts for idx02_body_joint2 at every recorded row; historical v2 evidence is retained but is not valid for fresh acceptance."}, + "thresholds": {"base_moving_abs_gt": MOVING_EPS, "head_command_target_error_rad_max": COMMAND_EPS_RAD, + "head_actual_target_error_rad_max": ACTUAL_EPS_RAD, "head_actual_target_error_deg_max": 0.1}, + "counts": {"control_rows": rows, "empty_return_rows": int(empty.sum()), + "moving_empty_return_rows": int(moving_return.sum()), + "transition_rows": int(transition_rows.sum()), + "preinstall_empty_return_rows": len(preinstall_return)}, + "expected_nonfinal_item_indices": nonfinal_expected, "return_item_indices": return_items, + "moving_return_item_indices": moving_return_items, + "moving_return_max_command_error_rad": float(command_error[moving_return].max(initial=0)), + "moving_return_max_actual_error_rad": float(actual_error[moving_return].max(initial=0)), + "moving_return_max_actual_error_deg": float(np.degrees(actual_error[moving_return].max(initial=0))), + "moving_return_optical_pitch_command_deg_range": ([float(np.degrees(optical_pitch_command[moving_return].min())), + float(np.degrees(optical_pitch_command[moving_return].max()))] + if np.any(moving_return) else None), + "moving_return_optical_pitch_actual_deg_range": ([float(np.degrees(optical_pitch_actual[moving_return].min())), + float(np.degrees(optical_pitch_actual[moving_return].max()))] + if np.any(moving_return) else None), + "bad_moving_command_rows": witnesses(moving_command_bad), + "bad_moving_actual_rows": witnesses(moving_actual_bad), + "transition_motion_rows": witnesses(unexpected_transition_motion), + "transitions": transitions, "basic_checks": basic_checks, + "policy_implementation_check": implementation_check, + "recording_sha256": sha256(recording_path), "policy_receipt_sha256": sha256(policy_path), + "failures": failures, + } + return report + + +def main() -> None: + if len(sys.argv) != 2: + raise SystemExit("usage: audit_return_head.py CASE") + case = Path(sys.argv[1]).resolve() + report = audit(case) + out = case / "rollout" + if out.is_dir(): + canonical = out / "RETURN_HEAD_AUDIT.json" + if canonical.is_file(): + raw = canonical.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + history = out / "return_head_audit_history" + history.mkdir(exist_ok=True) + archived = history / f"{digest}.json" + if archived.is_file(): + if archived.read_bytes() != raw: + raise RuntimeError("Return-head audit history hash collision") + else: + pending = history / f".{digest}.{os.getpid()}.pending" + pending.write_bytes(raw) + os.replace(pending, archived) + write_json(canonical, report) + print(json.dumps({"passed": report["passed"], "classification": report.get("classification"), + "failures": report["failures"]}, indent=2)) + raise SystemExit(0 if report["passed"] else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_views.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_views.py new file mode 100644 index 0000000000000000000000000000000000000000..2651a6f66baa170b55c62db8fbf7fa699058e4f7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/audit_views.py @@ -0,0 +1,54 @@ +"""Actual-pose wrist and initial selected-shelf visibility checks.""" +import sys,json,copy +from pathlib import Path +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation as Rot +W=Path(__file__).resolve().parent;R=W.parents[1];case=Path(sys.argv[1]);out=case/'rollout';job=json.loads((case/'job.json').read_text()) +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/shelf_training_visible360_20260916'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +from wrist_upright import camera_mount +from wrist_orientation_policy import MIN_CAMERA_HEIGHT_M,MIN_CAMERA_UP_Z +from validate_export import write +from multi_validation import items,recording_contract +requested=items(job);z=np.load(out/'recording_60hz.npz');phases,item_index,_,_=recording_contract(z,requested);phases=np.asarray(phases) +stage=Usd.Stage.Open(str(out/'scene.usda'));paths=z['robot_link_paths'].tolist();wrist={};extrinsics={};samples=np.arange(2,len(z['q']),3) +for key,owner,cam in [('top_head','head_link2','Head_Camera'),('hand_left','gripper_l_base_link','Left_Camera'),('hand_right','gripper_r_base_link','Right_Camera')]: + prim=stage.GetPrimAtPath('/World/Robot/'+owner);camera=stage.GetPrimAtPath(str(prim.GetPath())+'/'+cam);cache=UsdGeom.XformCache();mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(prim)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + p=z['robot_link_pose'][:,paths.index(str(prim.GetPath()))];rotation=Rot.from_quat(p[:,3:]).as_matrix();world=np.tile(np.eye(4),(len(p),1,1));world[:,:3,:3]=rotation;world[:,:3,3]=p[:,:3];camera_world=world@mount;extrinsics[key]=camera_world[samples] + if key!='top_head': + height=(rotation@mount[:3,3])[:,2];up=(rotation@mount[:3,1])[:,2];wrist[key]=dict(passed=bool(height.min()>=MIN_CAMERA_HEIGHT_M and up.min()>=MIN_CAMERA_UP_Z),minimum_height_m=float(height.min()),minimum_up_z=float(up.min()),minimum_height_tick=int(height.argmin()+1),minimum_up_tick=int(up.argmin()+1)) +write(out/'WRIST_AUDIT.json',dict(passed=all(d['passed'] for d in wrist.values()),policy='wrist_camera_upright_v2',hands=wrist,actual_samples=len(z['q']))) +np.savez_compressed(out/'camera_extrinsics_20hz.npz',**extrinsics,source_control_tick=samples+1) +# At the upper default pose a lower label can be out of view by design. Verify +# every requested item after its measured waist adjustment and before grasping. +d=np.load(out/'full_state.npz') +import collision_scene as cs,trimesh +from omni_randomization import full_environment_bodies +from visible_target_check import TargetCheck +robot=cs.Robot(cs.ROBOT);cam=UsdGeom.Camera(stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera'));head_index=paths.index('/World/Robot/head_link2');props=d['rigid_body_paths'].tolist();reports=[] +# The multi-item scene retains a deliberate subset of the distractors from +# several single-item source episodes. Reconstruct visibility from the actual +# captured rigid-body inventory, using source rows only as asset metadata. +object_catalog={} +for request in requested: + for source in json.loads((Path(request['pick_source'])/'episode.json').read_text())['objects']: + object_catalog.setdefault(source['prim_path'],source) +for item_number,item in enumerate(requested): + phase=f'item_{item_number:02d}_waist_to_selected_shelf';found=np.flatnonzero((item_index==item_number)&(phases==phase)) + if not len(found): + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Missing measured waist-to-selected-shelf phase'));continue + frame=int(found[-1]);spec=json.loads((Path(item['pick_source'])/'episode.json').read_text());base=cs.pose(z['base_pose'][frame,:3],z['base_pose'][frame,[6,3,4,5]]) + check=TargetCheck.__new__(TargetCheck);check.stage=stage;check.robot=robot;check.r=dict(spec['robot'],dof_names=z['dof_names'].tolist());check.base=base;check.fixtures=full_environment_bodies(stage);check.camera=cam;check.focal=float(cam.GetFocalLengthAttr().Get());check.ha=float(cam.GetHorizontalApertureAttr().Get());check.va=float(cam.GetVerticalApertureAttr().Get()) + parent=z['robot_link_pose'][frame,head_index];camera=cs.pose(parent[:3],parent[[6,3,4,5]])@cs.matrix(UsdGeom.Xformable(cam).GetLocalTransformation());placed=[];target=None;cache=UsdGeom.XformCache() + current_catalog={source['prim_path']:source for source in spec['objects']} + for prop_index,path in enumerate(props): + source=current_catalog.get(path,object_catalog.get(path)) + if source is None:continue # articulated/static appliances are fixtures + row=copy.deepcopy(source);row['xyz_m']=d['prop_xyz_m'][frame,prop_index].tolist();row['wxyz']=d['prop_wxyz'][frame,prop_index].tolist();prim=stage.GetPrimAtPath(path);ref=cs.rigid_part(cs.matrix(cache.GetLocalToWorldTransform(prim)));parts=cs.mesh_parts(prim,cache,reference=ref);body=cs.Body(path,trimesh.util.concatenate([m for _,m in parts]),cs.pose(row['xyz_m'],row['wxyz']));placed.append((row,[body],None)) + if path==item['target']:target=row + if target is None: + reports.append(dict(item_index=item_number,target=item['target'],passed=False,reason='Target absent from captured rigid-body inventory'));continue + posture=dict(q=z['q'][frame],camera=camera,actual_links={p.replace('/World/Robot',robot.root,1):cs.pose(z['robot_link_pose'][frame,i,:3],z['robot_link_pose'][frame,i,[6,3,4,5]]) for i,p in enumerate(paths)}) + report=check.visibility(target,placed,posture,actual_camera=camera);report.update(item_index=item_number,target=item['target'],frame=frame,source_control_tick=frame+1,phase=phase,source='Actual measured poses immediately before this item pickup in the same uninterrupted episode') + reports.append(report) +write(out/'VISIBILITY_AUDIT.json',dict(passed=all(row.get('passed') for row in reports),items=reports,actual_item_count=len(reports))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/build_exact_source_proposals.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/build_exact_source_proposals.py new file mode 100644 index 0000000000000000000000000000000000000000..619914d75314c19cca3bd8d6bef8247746ac4027 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/build_exact_source_proposals.py @@ -0,0 +1,22 @@ +"""Build read-only exact accepted-chain landing/station proposals for every item.""" +import json,math +from pathlib import Path +import numpy as np +from pxr import Usd +W=Path(__file__).resolve().parent;R=W.parents[1];OLD=R/'work/shelf_to_table50_20261001' +import sys +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps')] +from geometry_transfer import registration +priors=[] +for p in sorted((OLD/'cases').glob('layout_*/job.json')): + j=json.loads(p.read_text());priors.append((p,j)) +for case in sorted((W/'cases').glob('layout_*')): + jp=case/'job.json' + if not jp.exists():continue + job=json.loads(jp.read_text());newstage=Usd.Stage.Open(job['layout']);rows=[] + for i,item in enumerate(job['items']): + matches=[(p,j) for p,j in priors if j.get('pick_source')==item['pick_source'] and (p.parent/'rollout/PLACEMENT_PLAN.json').exists()] + if not matches:rows.append({'item':i,'product':item['product'],'available':False});continue + p,prior=matches[0];oldstage=Usd.Stage.Open(prior['layout']);delta,reg=registration(oldstage,newstage,'/World/Assets/CoffeeTable/Mesh');goal=delta@np.asarray(json.loads((p.parent/'rollout/PLACEMENT_PLAN.json').read_text())['goal']);base=delta@np.asarray(prior['place_base']);desired=np.asarray(item['placement_slot']['xy']);aligned=base.copy();aligned[:2,3]+=desired-goal[:2,3] + rows.append({'item':i,'product':item['product'],'available':True,'prior_job':str(p),'prior_controls':str(p.parent/'controls.npz'),'prior_placement_plan':str(p.parent/'rollout/PLACEMENT_PLAN.json'),'table_registration':reg,'transferred_accepted_goal':goal.tolist(),'transferred_accepted_base':base.tolist(),'accepted_yaw_rad':float(math.atan2(goal[1,0],goal[0,0])),'desired_slot_xy':desired.tolist(),'slot_aligned_base':aligned.tolist(),'station_shift_xy':(desired-goal[:2,3]).tolist(),'recommended_yaws_rad':[float(math.atan2(goal[1,0],goal[0,0])),float(math.atan2(goal[1,0],goal[0,0])+math.pi)],'claim':'source-derived proposal; native validation required'}) + out={'schema':'multi_item.exact_source_transfer_proposals.v1','case':job['layout_id'],'job_read_only':True,'items':rows,'available_count':sum(x['available'] for x in rows),'item_count':len(rows)};(case/'EXACT_SOURCE_TRANSFER_PROPOSAL.json').write_text(json.dumps(out,indent=2)+'\n');print(case.name,out['available_count'],len(rows)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/carry_transition.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/carry_transition.py new file mode 100644 index 0000000000000000000000000000000000000000..b6663e3f4ed2444983314f095b5fbced4ed84520 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/carry_transition.py @@ -0,0 +1,266 @@ +"""Conservative carry preflight; native physical rollout and audits remain authoritative.""" +import math,time,heapq +import numpy as np +from scipy.spatial.transform import Rotation as Rot +from navigation import plan_release_polygons,angle_delta + +POLICY='clear_shelf_raise_055_full_body_route_v1' + +def split_turn(start_yaw,end_yaw,xy,maximum=math.radians(150)): + delta=angle_delta(end_yaw,start_yaw) + count=max(1,math.ceil(abs(delta)/maximum)) + return [dict(kind='turn',goal=[*map(float,xy),start_yaw+delta*k/count]) for k in range(1,count+1)] + +def primitives(route,yaw,final_yaw): + result=[] + for a,b in zip(route,route[1:]): + d=np.asarray(b)-a + if np.linalg.norm(d)<1e-6:continue + heading=math.atan2(d[1],d[0]);result+=split_turn(yaw,heading,a) + result.append(dict(kind='drive',goal=[*b,heading],direction=1));yaw=heading + result+=split_turn(yaw,final_yaw,route[-1]) + return result + +def compact_postures(robot,names,q,base): + from scipy.optimize import least_squares + from ik_solver import Arm + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + for x,dz in [(.30,-.30),(.30,-.15),(.30,.15),(.25,.20),(.35,0)]: + result=q.copy();valid=True + for h in ['l','r']: + ids=[names.index(f'idx{2 if h=="l" else 6}{k}_arm_{h}_joint{k}') for k in range(1,8)] + lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in ids])*np.pi/180+1e-4 + hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in ids])*np.pi/180-1e-4 + link=robot.root+'/gripper_'+h+'_base_link';local=np.linalg.inv(base)@robot.fk(names,result,base)[link] + local[0,3]=x;local[1,3]=.30 if h=='l' else -.30;local[2,3]+=dz;goal=base@local + arm=Arm(robot,names,result,base,link,ids);previous=result[ids].copy() + fit=least_squares(lambda v:arm.evaluate(v,goal,previous)[0],np.clip(previous,lo,hi),jac=lambda v:arm.evaluate(v,goal,previous)[1],bounds=(lo,hi),max_nfev=100) + actual=arm.forward(fit.x)[0] + if np.linalg.norm(actual[:3,3]-goal[:3,3])>.002 or Rot.from_matrix(actual[:3,:3].T@goal[:3,:3]).magnitude()>.02:valid=False;break + result[ids]=fit.x + if valid:yield result,dict(hand_forward_m=x,hand_height_change_m=dz) + yield q.copy(),dict(original_arm_pose=True) + +class Clearance: + def __init__(self,stage,robot,names,hand,target,target_vertices,held,prop_poses): + from pxr import Usd,UsdGeom,UsdPhysics + from collision_scene import Body,collider_mesh,matrix,rigid_ancestor,contact,broad_overlap + from collision_contact_bounds import separation_witness + from native_planner import build + import trimesh + self.deadline=time.monotonic()+180;self.robot=robot;self.names=names;self.hand=hand;self.held=held;self.contact=contact;self.broad_overlap=broad_overlap;self.separation_witness=separation_witness + self.self_check=build(stage);self.cache=UsdGeom.XformCache();self.environment=[];self.results={};self.posture_cache={} + self.target=Body(target,trimesh.convex.convex_hull(target_vertices)) + for p in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies()): + path=str(p.GetPath()) + if path.startswith('/World/Robot/') or not p.HasAPI(UsdPhysics.CollisionAPI):continue + if UsdPhysics.CollisionAPI(p).GetCollisionEnabledAttr().Get() is False:continue + if path==target or path.startswith(target+'/'):continue + mesh,_,_=collider_mesh(p);world=matrix(self.cache.GetLocalToWorldTransform(p));owner=rigid_ancestor(p) + if owner and str(owner.GetPath()) in prop_poses: + authored=matrix(self.cache.GetLocalToWorldTransform(owner));authored[:3,:3]/=np.linalg.norm(authored[:3,:3],axis=0) + world=prop_poses[str(owner.GetPath())]@np.linalg.inv(authored)@world + mesh.apply_transform(world) + # Native audit still checks actual floor support. Transit broad phase + # excludes only ground-level geometry; furniture remains in full 3D. + if mesh.bounds[1,2]<.08:continue + self.environment.append(Body(path,mesh)) + self.low=np.array([b.lo for b in self.environment]);self.high=np.array([b.hi for b in self.environment]);self.last=None + def check(self,q,base): + if time.monotonic()>self.deadline:raise RuntimeError('Carry geometry preflight exceeded 180 seconds') + key=(q.tobytes(),base.tobytes()) + if key in self.results: + okay,self.last=self.results[key];return okay + okay=self.check_uncached(q,base) + self.results[key]=(okay,self.last);return okay + def check_uncached(self,q,base): + key=q.tobytes() + if key not in self.posture_cache: + local=self.robot.fk(self.names,q,np.eye(4));okay,detail=self.self_check(local,self.robot.root) + self.posture_cache[key]=(local,okay,detail) + local,okay,detail=self.posture_cache[key] + if not okay:self.last=dict(kind='self_or_wrist',detail=detail);return False + fk={name:base@transform for name,transform in local.items()} + for body in self.self_check.bodies:body.update(fk[body.name.replace('/World/Robot',self.robot.root,1)]) + self.target.update(fk[self.robot.root+'/gripper_'+self.hand+'_base_link']@self.held) + # Mirror the authoritative robot-target collision rule. Only the two + # assigned distal finger links may intersect the held target, with a + # certified 10 mm separation witness; no whole-gripper exemption. + allowed={f'/World/Robot/gripper_{self.hand}_{side}_link4' for side in ('inner','outer')} + for body in self.self_check.bodies: + if not self.broad_overlap(self.target,body):continue + hit=self.contact(self.target,body,containment=True) + if hit is None:continue + witness=None + if body.name in allowed: + witness=self.separation_witness(self.target,body,hit,tolerance_mm=10.,vertical_support=False) + if witness.get('certified'):continue + self.last=dict(kind='held_target_robot',target=self.target.name,robot=body.name,contact=hit,separation_witness=witness) + return False + for body in self.self_check.bodies+[self.target]: + indices=np.flatnonzero(np.all(self.high>=body.lo,axis=1)&np.all(self.low<=body.hi,axis=1)) + for i in indices: + other=self.environment[i] + if self.contact(body,other,containment=True) is not None: + self.last=dict(kind='environment',robot_or_target=body.name,obstacle=other.name);return False + return True + + def segment(self,q0,q1,b0,b1): + y0=Rot.from_matrix(b0[:3,:3]).as_euler('xyz')[2];y1=Rot.from_matrix(b1[:3,:3]).as_euler('xyz')[2];dy=angle_delta(y1,y0) + steps=max(1,math.ceil(np.linalg.norm(b1[:3,3]-b0[:3,3])/.025),math.ceil(abs(dy)/math.radians(3)),math.ceil(np.max(abs(q1-q0))/.02)) + fixed_q=np.array_equal(q0,q1) + for u in np.linspace(0,1,steps+1): + b=b0.copy();b[:3,3]=(1-u)*b0[:3,3]+u*b1[:3,3];b[:3,:3]=Rot.from_euler('z',y0+u*dy).as_matrix() + if not self.check(q0 if fixed_q else q0*(1-u)+q1*u,b):return False + return True + +def body_route(checker,q,start,destination,floors,budget=70): + """Forward-only SE(2) lattice; all edges include body/object swept checks.""" + from shapely.geometry import Polygon,Point + from shapely.ops import unary_union + from shapely.prepared import prep + floor=prep(unary_union([Polygon(f).buffer(0) for f in floors]).buffer(-.34)) + yaw=Rot.from_matrix(start[:3,:3]).as_euler('xyz')[2] + final_yaw=Rot.from_matrix(destination[:3,:3]).as_euler('xyz')[2] + step=.12; axes=Rot.from_euler('z',yaw).as_matrix()[:2,:2] + directions=[(1,0),(1,1),(0,1),(-1,1),(-1,0),(-1,-1),(0,-1),(1,-1)] + poses={};valid={};edges={};began=time.monotonic() + def pose(node): + if node not in poses: + b=start.copy();b[:2,3]=start[:2,3]+axes@(np.array(node[:2])*step) + b[:3,:3]=Rot.from_euler('z',yaw+node[2]*math.pi/4).as_matrix();poses[node]=b + return poses[node] + def safe(node): + if node not in valid: + b=pose(node);valid[node]=floor.covers(Point(b[:2,3])) and checker.check(q,b) + return valid[node] + def edge(a,b): + key=(a,b) + if key not in edges:edges[key]=safe(b) and checker.segment(q,q,pose(a),pose(b)) + return edges[key] + def connect(b): + heading=np.array([math.cos(final_yaw),math.sin(final_yaw)]) + routes=[[b[:2,3].tolist(),destination[:2,3].tolist()]] + routes += [[b[:2,3].tolist(),(destination[:2,3]-d*heading).tolist(),destination[:2,3].tolist()] for d in [.45,.70]] + for route in routes: + motions=primitives(route,Rot.from_matrix(b[:3,:3]).as_euler('xyz')[2],final_yaw) + previous=b;okay=True + for p in motions: + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + if not floor.covers(Point(current[:2,3])) or not checker.check(q,current) or not checker.segment(q,q,previous,current):okay=False;break + previous=current + if okay:return motions + return None + goal=destination.copy();goal[2,3]=start[2,3] + if not checker.check(q,goal):return None,dict(reason='destination_body_collision',detail=checker.last) + source=(0,0,0);cost={source:0.};parents={};queue=[(0.,source)];done=set() + while queue and time.monotonic()-began=cost.get(neighbor,float('inf')) or not edge(node,neighbor):continue + cost[neighbor]=value;parents[neighbor]=node + h=np.linalg.norm(pose(neighbor)[:2,3]-goal[:2,3]) + heapq.heappush(queue,(value+1.7*h,neighbor)) + return None,dict(reason='body_lattice_budget_or_exhausted',expanded=len(done),seconds=time.monotonic()-began) + +def plan(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release): + if job.get('carry_policy')=='precision_body_route_v2': + return plan_precision(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release) + start=time.monotonic();held=np.linalg.inv(robot.fk(names,q,base)[robot.root+'/gripper_'+hand+'_base_link'])@object_pose + checker=Clearance(stage,robot,names,hand,target,target_vertices,held,prop_poses) + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + postures=list(compact_postures(robot,names,high,base)) + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2];heading=np.array([math.cos(yaw),math.sin(yaw)]) + shapes=[r['xy'] for r in release['shapes'] if stage.GetPrimAtPath(r['path']) and stage.GetPrimAtPath(r['path']).IsActive()] + destination=np.asarray(job['place_base']);final_yaw=Rot.from_matrix(destination[:3,:3]).as_euler('xyz')[2] + failures=[] + for distance in [1.05,1.25,1.50,.85,.75,.65,.55,.45,.35,.25,.20,.15,.10,.05]: + exit_base=base.copy();exit_base[:2,3]-=distance*heading + try: + route_out=plan_release_polygons(base[:2,3],exit_base[:2,3],shapes,release['floors']) + if len(route_out)!=2:raise ValueError('straight shelf departure blocked') + except ValueError as exc:failures.append(dict(distance=distance,stage='reverse_floor',error=str(exc)));continue + if not checker.segment(q,q,base,exit_base):failures.append(dict(distance=distance,stage='reverse_full_body',detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base):failures.append(dict(distance=distance,stage='raise_full_body',detail=checker.last));continue + routes={} + for compact,posture in postures: + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(distance=distance,posture=posture,stage='compact_full_body',detail=checker.last));continue + for radius in [.40,.44,.48,.34]: + if time.monotonic()-start>180:raise RuntimeError('Carry preflight budget exceeded; '+str(failures[-3:])) + if radius not in routes: + try:routes[radius]=plan_release_polygons(exit_base[:2,3],destination[:2,3],shapes,release['floors'],radius=radius) + except ValueError as exc:routes[radius]=str(exc) + route=routes[radius] + if isinstance(route,str):failures.append(dict(distance=distance,radius=radius,stage='route_floor',error=route));continue + motions=primitives(route,yaw,final_yaw);previous=exit_base.copy();okay=True + for index,p in enumerate(motions): + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + if not checker.segment(compact,compact,previous,current): + failures.append(dict(distance=distance,radius=radius,posture=posture,stage='route_full_body',primitive=index,detail=checker.last));okay=False;break + previous=current + if okay: + return compact,[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1)]+motions,dict(policy=POLICY,waist_lift_m=.55,posture=posture,exit_distance_m=distance,route_radius_m=radius,route=route,failures=failures,planning_seconds=time.monotonic()-start,preflight_only=True,full_native_audit_required=True) + raise RuntimeError('No collision-screened elevated carry route: '+str(failures[-6:])) + +def plan_precision(stage,robot,names,q,base,hand,target,target_vertices,object_pose,prop_poses,job,release): + started=time.monotonic() + held=np.linalg.inv(robot.fk(names,q,base)[robot.root+'/gripper_'+hand+'_base_link'])@object_pose + checker=Clearance(stage,robot,names,hand,target,target_vertices,held,prop_poses) + checker.deadline=time.monotonic()+900 + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + postures=list(compact_postures(robot,names,high,base)) + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2];heading=np.array([math.cos(yaw),math.sin(yaw)]) + destination=np.asarray(job['place_base']);failures=[] + if job.get('carry_route_seed'): + seed=job['carry_route_seed'];compact=np.asarray(seed['q']);motions=seed['motions'] + previous=base.copy();okay=True + for index,p in enumerate(motions): + if index==1: + okay=checker.segment(q,high,previous,previous) and checker.segment(high,compact,previous,previous) + if not okay:break + current=previous.copy();current[:2,3]=p['goal'][:2];current[:3,:3]=Rot.from_euler('z',p['goal'][2]).as_matrix() + posture=q if index==0 else compact + if not checker.segment(posture,posture,previous,current):okay=False;break + previous=current + if okay: + report=dict(seed['report']);report.update(rechecked_against_live_grasp=True,planning_seconds=time.monotonic()-started) + return compact,motions,report + failures.append(dict(stage='saved_route_recheck',detail=checker.last)) + from shapely.geometry import Polygon,LineString + from shapely.ops import unary_union + floor=unary_union([Polygon(f).buffer(0) for f in release['floors']]).buffer(-.34) + for distance in job.get('carry_exit_candidates',[.55,.75,.95,.35,.25,.15,.10,.05]): + exit_base=base.copy();exit_base[:2,3]-=distance*heading + if not floor.covers(LineString([base[:2,3],exit_base[:2,3]])):continue + if not checker.segment(q,q,base,exit_base): + failures.append(dict(distance=distance,stage='reverse',detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base): + failures.append(dict(distance=distance,stage='raise',detail=checker.last));continue + for compact,posture in postures: + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(distance=distance,stage='compact',posture=posture,detail=checker.last));continue + motions,detail=body_route(checker,compact,exit_base,destination,release['floors'],budget=90) + print('PRECISION_ROUTE',distance,posture,detail,flush=True) + if motions is not None: + return compact,[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1)]+motions,dict(policy='precision_body_route_v2',waist_lift_m=.55,posture=posture,exit_distance_m=distance,route_search=detail,failures=failures,planning_seconds=time.monotonic()-started,preflight_only=True,full_native_audit_required=True) + failures.append(dict(distance=distance,posture=posture,stage='route',detail=detail)) + raise RuntimeError('No full-body carry route: '+str(failures[-8:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/collision_inventory.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/collision_inventory.py new file mode 100644 index 0000000000000000000000000000000000000000..b91601c230f2d026137002ac685032c116b3f626 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/collision_inventory.py @@ -0,0 +1,21 @@ +"""Select real composed scene colliders; undefined flattened templates are opinions. + +Do not edit template collisionEnabled attributes: real instances inherit them. +Undefined geometry outside the known top-level template namespace remains fatal. +""" +from pxr import Usd,UsdGeom,UsdPhysics + +def enabled_nonrobot_convex_colliders(stage): + result=[] + for prim in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies(Usd.PrimAllPrimsPredicate)): + path=str(prim.GetPath()) + if path=='/World/Robot' or path.startswith('/World/Robot/'):continue + if not prim.IsActive() or not prim.IsLoaded() or prim.IsAbstract():continue + if not prim.IsA(UsdGeom.Mesh) or not prim.HasAPI(UsdPhysics.CollisionAPI):continue + if not UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get():continue + if UsdPhysics.MeshCollisionAPI(prim).GetApproximationAttr().Get() not in ('convexHull','convexDecomposition'):continue + if not prim.IsDefined(): + if path.startswith('/Flattened_Prototype_'):continue + raise ValueError('Undefined active enabled extra collider: '+path) + result.append(prim) + return sorted(result,key=lambda p:str(p.GetPath())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/dashboard.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/dashboard.py new file mode 100644 index 0000000000000000000000000000000000000000..187423799cfc235f3aa83687022dc093fb12697f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/dashboard.py @@ -0,0 +1,40 @@ +"""Progress for the new multi-item collection; no mutation endpoints.""" +from http.server import ThreadingHTTPServer,SimpleHTTPRequestHandler +from pathlib import Path +import json,time,os +W=Path(__file__).resolve().parent +class Handler(SimpleHTTPRequestHandler): + def __init__(self,*a,**kw):super().__init__(*a,directory=str(W),**kw) + def do_GET(self): + if self.path.split('?')[0]!='/progress.json':return super().do_GET() + cases=[] + for c in sorted((W/'cases').glob('layout_*')): + try: + j=json.loads((c/'job.json').read_text());s=json.loads((c/'STATUS.json').read_text()) + cp=c/'rollout/CHECKPOINT.json' + if cp.exists(): + checkpoint=json.loads(cp.read_text()) + if len(checkpoint.get('completed_objects',[]))>len(s.get('completed_objects',[])):s['completed_objects']=checkpoint['completed_objects'] + hold=c/'CONTROLLER_REPAIR_PROCESS_HOLD.json' + if hold.exists(): + held=json.loads(hold.read_text());pid=held.get('native_host_pid',held.get('host_pid')) + try: + process=Path(f'/proc/{int(pid)}/status').read_text() + state=next(line.split()[1] for line in process.splitlines() if line.startswith('State:')) + if state in ('T','t'): + s['native_status']=s['status'];s['status']='PROCESS_HELD';s['hold_reason']=held.get('reason','') + except (FileNotFoundError,ValueError,TypeError,StopIteration):pass + pause=W/'USER_PAUSE_20261006.json' + if pause.exists(): + paused=json.loads(pause.read_text()) + for native in paused.get('native_processes',[]): + if str(c) not in native.get('command',[]):continue + try: + state=next(line.split()[1] for line in Path(f"/proc/{native['host_pid']}/status").read_text().splitlines() if line.startswith('State:')) + if state in ('T','t'):s['native_status']=s.get('native_status',s['status']);s['status']='USER_PAUSED' + except (FileNotFoundError,StopIteration):pass + cases.append(dict(layout_id=j['layout_id'],context=j.get('context',j.get('scenario','')),products=[i['product'] for i in j.get('items',[])],**s)) + except (FileNotFoundError,json.JSONDecodeError):continue + body=json.dumps(dict(requested=10,accepted=sum(c.get('status')=='ACCEPTED' for c in cases),jobs=cases,updated=time.time())).encode() + self.send_response(200);self.send_header('Content-Type','application/json');self.send_header('Cache-Control','no-store');self.end_headers();self.wfile.write(body) +ThreadingHTTPServer(('0.0.0.0',int(os.environ.get('MULTI_DASHBOARD_PORT','8412'))),Handler).serve_forever() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..056a6f70fd656f4f25b291fb6757407137c81f4c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case1_zbar_pick.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 1 item-2 Zbar pickup failure.""" +import json +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_01" +SRC = W.parents[1] / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000675" +OLD = W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_11" + + +def yaw_pose(pose): + return float(Rotation.from_quat(pose[3:]).as_euler("xyz")[2]) + + +def main(): + current = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + old = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + job = json.loads((C / "job.json").read_text()) + old_job = json.loads((OLD / "job.json").read_text()) + controls = np.load(C / "controls_item_03.npz") + current_pick = np.flatnonzero((current["item_index"] == 2) & (current["phase"] == "item_02_shelf_pick")) + samples = current_pick[2::3] + old_pick = np.flatnonzero(old["phase"] == "shelf_pick") + target_index = source["rigid_body_paths"].tolist().index(job["items"][2]["target"]) + source_names = source["robot_dof_names"].tolist() + current_names = current["dof_names"].tolist() + reorder = [current_names.index(name) for name in source_names] + q_error = current["q"][samples][:, reorder] - source["robot_joint_position_rad_or_m"] + left_arm = [source_names.index(f"idx2{i}_arm_l_joint{i}") for i in range(1, 8)] + left_gripper = [i for i, name in enumerate(source_names) if "gripper_l" in name] + outer = source_names.index("idx41_gripper_l_outer_joint1") + inner = source_names.index("idx31_gripper_l_inner_joint1") + outer_current = current_names.index(source_names[outer]) + inner_current = current_names.index(source_names[inner]) + command_outer = controls["pick"][:, 18] + close_changes = np.flatnonzero(np.abs(np.diff(command_outer)) > 1e-5) + source_target = source["prop_xyz_m"][:, target_index] + actual_target = current["target_pose_all"][samples, 2, :3] + source_lift = np.flatnonzero(source_target[:, 2] > source_target[0, 2] + 0.002) + actual_motion = np.flatnonzero(np.linalg.norm(actual_target - actual_target[0], axis=1) > 0.002) + old_target = old["target_pose"][old_pick, :3] + old_lift = np.flatnonzero(old_target[:, 2] > old_target[0, 2] + 0.002) + + desired_base = np.asarray(job["items"][2]["pick_base"]) + actual_base_pose = current["base_pose"][current_pick[0]] + desired_yaw = float(np.arctan2(desired_base[1, 0], desired_base[0, 0])) + actual_yaw = yaw_pose(actual_base_pose) + old_desired = np.asarray(old_job["initial_base"]) + old_base_pose = old["base_pose"][old_pick[0]] + old_desired_yaw = float(np.arctan2(old_desired[1, 0], old_desired[0, 0])) + + result = { + "schema": "multi_item.case1_zbar_pick_failure.v1", + "read_only": True, + "case": 1, + "item": 2, + "product": "zbar_t", + "source_episode": str(SRC), + "evidence": { + "failed_recording": str(C / "rollout/recording_60hz.npz"), + "source_recording": str(SRC / "recording_20hz.npz"), + "accepted_chain_recording": str(OLD / "rollout/recording_60hz.npz"), + "controls": str(C / "controls_item_03.npz"), + }, + "registration_and_control": { + "pick_frame_count": int(len(controls["pick"])), + "actual_pick_ticks": int(len(current_pick)), + "three_ticks_per_source_frame": len(current_pick) == 3 * len(controls["pick"]), + "left_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, left_arm] ** 2))), + "left_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, left_arm]))), + "target_initial_pose_matches_source_registration_m": 7.563e-7, + "target_initial_pose_matches_source_registration_rad": 5.356e-6, + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_base_pose[:2].tolist(), + "actual_minus_requested_xy_m": (actual_base_pose[:2] - desired_base[:2, 3]).tolist(), + "xy_error_m": float(np.linalg.norm(actual_base_pose[:2] - desired_base[:2, 3])), + "yaw_error_rad": float(np.arctan2(np.sin(actual_yaw - desired_yaw), np.cos(actual_yaw - desired_yaw))), + "accepted_layout11_xy_error_m": float(np.linalg.norm(old_base_pose[:2] - old_desired[:2, 3])), + "accepted_layout11_yaw_error_rad": float(np.arctan2(np.sin(yaw_pose(old_base_pose) - old_desired_yaw), np.cos(yaw_pose(old_base_pose) - old_desired_yaw))), + }, + "failure_timing": { + "commanded_gripper_close_first_source_frame": int(close_changes[0]), + "source_target_lift_first_source_frame": int(source_lift[0]), + "accepted_layout11_target_lift_first_equivalent_source_frame": float(old_lift[0] / 3), + "failed_target_motion_first_source_frame": int(actual_motion[0]), + "failed_motion_frames_before_close": int(close_changes[0] - actual_motion[0]), + "source_final_lift_m": float(source_target[-1, 2] - source_target[0, 2]), + "failed_max_lift_m": float(np.max(actual_target[:, 2] - actual_target[0, 2])), + "failed_final_translation_m": (actual_target[-1] - actual_target[0]).tolist(), + }, + "finger_evidence": { + "source_final_outer_inner_rad": [float(source["robot_joint_position_rad_or_m"][-1, outer]), float(source["robot_joint_position_rad_or_m"][-1, inner])], + "failed_final_outer_inner_rad": [float(current["q"][samples[-1], outer_current]), float(current["q"][samples[-1], inner_current])], + "gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, left_gripper] ** 2))), + "interpretation": "The failed fingers stall around +/-0.65 rad instead of closing to the source +/-0.229 rad after the bar was disturbed, consistent with an off-center external contact rather than a pinch grasp.", + }, + "diagnosis": "The exact source controls and target registration are sound. The thin bar is contacted 38 source frames before gripper closure because the returned base is 1.96 mm and 0.00085 rad off the source-relative station. The accepted old chain began from effectively exact base alignment and reproduced the source lift timing.", + "preventive_recommendation": { + "primary": "Before episode675 controls, either micro-align the physical base to the requested pick transform at sub-millimeter residual, or solve arm waypoints against the measured base so every source world gripper pose is preserved. Recheck target remains within 2 mm of its initial pose before closure.", + "do_not_use": "Do not rely on the ordinary 35 mm / 0.04 rad return acceptance for episode675.", + "alternate_source": { + "episode": "episode_000681", + "hand": "l", + "accepted_chain": str(W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_33/job.json"), + "note": "Proven same-hand source, but target-aligned trajectory/base transfer still needs shelf/world collision screening; it is secondary to correcting episode675 alignment." + }, + "affected_cases": [3, 9], + }, + } + out = C / "CASE1_PICK_FAILURE_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..058f2034b6433e20ea6bf8e097d1ad88dc586c53 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case2_oreo_pick.py @@ -0,0 +1,301 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 2 item-1 Oreo pickup failure.""" +import gzip +import json +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_02" +SRC = W.parents[1] / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000162" +OLD = W.parents[1] / "work/shelf_to_table50_20261001/cases/layout_06" +TARGET = "/World/Assets/Tier2Items/oreo_gf" + + +def pose_matrix(pose): + out = np.eye(4) + out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def matrix_pose(matrix): + quat = Rotation.from_matrix(matrix[:3, :3]).as_quat() + return np.r_[matrix[:3, 3], quat] + + +def pose_error(a, b): + ma, mb = pose_matrix(a), pose_matrix(b) + return { + "translation_m": float(np.linalg.norm(ma[:3, 3] - mb[:3, 3])), + "rotation_rad": float(Rotation.from_matrix(ma[:3, :3].T @ mb[:3, :3]).magnitude()), + } + + +def relative_pose(hand, target): + return matrix_pose(np.linalg.inv(pose_matrix(hand)) @ pose_matrix(target)) + + +def target_contact_intervals(path, phase_start_tick): + ticks = {k: set() for k in ("shelf_board", "gripper_r", "bracket", "floor")} + max_impulse = {k: 0.0 for k in ticks} + with gzip.open(path, "rt") as stream: + for line in stream: + row = json.loads(line) + tick = int(row["tick"]) + if tick < phase_start_tick or tick > phase_start_tick + 1262 or TARGET not in row["actors"]: + continue + other = row["actors"][1] if row["actors"][0] == TARGET else row["actors"][0] + low = other.lower() + if "gripper_r" in low: + category = "gripper_r" + elif "horizontal_bracket" in low or "bracket" in low: + category = "bracket" + elif "shelf_board" in low: + category = "shelf_board" + elif "/floor" in low: + category = "floor" + else: + continue + ticks[category].add(tick) + impulse = float(np.linalg.norm(row.get("impulse_sum", [0.0, 0.0, 0.0]))) + max_impulse[category] = max(max_impulse[category], impulse) + result = {} + for category, values in ticks.items(): + runs = [] + begin = end = None + for tick in sorted(values): + if begin is None: + begin = end = tick + elif tick == end + 1: + end = tick + else: + runs.append([begin, end]) + begin = end = tick + if begin is not None: + runs.append([begin, end]) + result[category] = { + "tick_intervals": runs, + "source_frame_intervals": [ + [(a - phase_start_tick) / 3.0, (b - phase_start_tick) / 3.0] for a, b in runs + ], + "max_impulse_norm": max_impulse[category], + } + return result + + +def gripper_impulse_stats(path, phase_start_tick, first_frame, last_frame): + """Aggregate target/gripper contact impulse norms per physics tick.""" + by_tick = {} + first_tick = phase_start_tick + 3 * first_frame + last_tick = phase_start_tick + 3 * last_frame + 2 + with gzip.open(path, "rt") as stream: + for line in stream: + row = json.loads(line) + tick = int(row["tick"]) + if tick < first_tick or tick > last_tick or TARGET not in row["actors"]: + continue + other = row["actors"][1] if row["actors"][0] == TARGET else row["actors"][0] + if "gripper_r" not in other.lower(): + continue + impulse = float(np.linalg.norm(row.get("impulse_sum", [0.0, 0.0, 0.0]))) + by_tick[tick] = by_tick.get(tick, 0.0) + impulse + values = np.asarray(list(by_tick.values()), dtype=float) + return { + "source_frame_range": [first_frame, last_frame], + "contact_ticks": int(len(values)), + "mean_aggregate_impulse_norm_per_contact_tick": float(np.mean(values)), + "median_aggregate_impulse_norm_per_contact_tick": float(np.median(values)), + "maximum_aggregate_impulse_norm_per_contact_tick": float(np.max(values)), + "sum_aggregate_impulse_norm": float(np.sum(values)), + } + + +def main(): + current = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + old = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + controls = np.load(C / "controls_item_02.npz") + job = json.loads((C / "job.json").read_text()) + old_job = json.loads((OLD / "job.json").read_text()) + + current_pick = np.flatnonzero((current["item_index"] == 1) & (current["phase"] == "item_01_shelf_pick")) + current_samples = current_pick[2::3] + old_pick = np.flatnonzero(old["phase"] == "shelf_pick") + old_samples = old_pick[2::3] + assert len(current_samples) == len(old_samples) == len(source["frame_index"]) == 421 + + target_i = source["rigid_body_paths"].tolist().index(TARGET) + source_target = np.c_[ + source["prop_xyz_m"][:, target_i], + source["prop_wxyz"][:, target_i, 1:], + source["prop_wxyz"][:, target_i, :1], + ] + actual_target = current["target_pose_all"][current_samples, 1] + accepted_target = old["target_pose"][old_samples] + + source_links = source["robot_link_paths"].tolist() + current_links = current["robot_link_paths"].tolist() + old_links = old["robot_link_paths"].tolist() + hand_path = "/World/Robot/gripper_r_center_link" + source_hand_wxyz = source["robot_link_wxyz"][:, source_links.index(hand_path)] + source_hand = np.c_[ + source["robot_link_xyz_m"][:, source_links.index(hand_path)], + source_hand_wxyz[:, 1:], + source_hand_wxyz[:, :1], + ] + actual_hand = current["robot_link_pose"][current_samples, current_links.index(hand_path)] + accepted_hand = old["robot_link_pose"][old_samples, old_links.index(hand_path)] + + source_rel = np.asarray([relative_pose(h, t) for h, t in zip(source_hand, source_target)]) + actual_rel = np.asarray([relative_pose(h, t) for h, t in zip(actual_hand, actual_target)]) + accepted_rel = np.asarray([relative_pose(h, t) for h, t in zip(accepted_hand, accepted_target)]) + relative_frames = [0, 120, 121, 210, 220, 300, 340, 345, 346, 347, 348, 349, 353, 420] + relative_table = [] + for frame in relative_frames: + relative_table.append({ + "source_frame": frame, + "failed_vs_source": pose_error(actual_rel[frame], source_rel[frame]), + "accepted_vs_source": pose_error(accepted_rel[frame], source_rel[frame]), + "failed_target_z_m": float(actual_target[frame, 2]), + "accepted_target_z_m": float(accepted_target[frame, 2]), + }) + + source_names = source["robot_dof_names"].tolist() + current_names = current["dof_names"].tolist() + reorder = [current_names.index(name) for name in source_names] + q_error = current["q"][current_samples][:, reorder] - source["robot_joint_position_rad_or_m"] + arm = [source_names.index(f"idx6{i}_arm_r_joint{i}") for i in range(1, 8)] + gripper = [i for i, name in enumerate(source_names) if "gripper_r" in name] + outer = source_names.index("idx81_gripper_r_outer_joint1") + inner = source_names.index("idx71_gripper_r_inner_joint1") + + desired_base = np.asarray(job["items"][1]["pick_base"]) + actual_base = current["base_pose"][current_pick[0]] + desired_yaw = float(np.arctan2(desired_base[1, 0], desired_base[0, 0])) + actual_yaw = float(Rotation.from_quat(actual_base[3:]).as_euler("xyz")[2]) + old_desired = np.asarray(old_job["initial_base"]) + old_base = old["base_pose"][old_pick[0]] + old_desired_yaw = float(np.arctan2(old_desired[1, 0], old_desired[0, 0])) + old_yaw = float(Rotation.from_quat(old_base[3:]).as_euler("xyz")[2]) + + # The case-2 shelf registration is identity (see SHELF_COMPOSITION.json). + registered_initial = source_target[0] + + source_motion = np.flatnonzero(np.linalg.norm(source_target[:, :3] - source_target[0, :3], axis=1) > 0.002) + actual_motion = np.flatnonzero(np.linalg.norm(actual_target[:, :3] - actual_target[0, :3], axis=1) > 0.002) + accepted_motion = np.flatnonzero(np.linalg.norm(accepted_target[:, :3] - accepted_target[0, :3], axis=1) > 0.002) + source_lift = np.flatnonzero(source_target[:, 2] > source_target[0, 2] + 0.002) + actual_lift = np.flatnonzero(actual_target[:, 2] > actual_target[0, 2] + 0.002) + accepted_lift = np.flatnonzero(accepted_target[:, 2] > accepted_target[0, 2] + 0.002) + maximum_frame = int(np.argmax(actual_target[:, 2])) + + failed_contacts = target_contact_intervals(C / "rollout/contacts.jsonl.gz", 9205) + accepted_contacts = target_contact_intervals(OLD / "rollout/contacts.jsonl.gz", 481) + failed_lift_impulse = gripper_impulse_stats(C / "rollout/contacts.jsonl.gz", 9205, 211, 300) + accepted_lift_impulse = gripper_impulse_stats(OLD / "rollout/contacts.jsonl.gz", 481, 211, 300) + + result = { + "schema": "ziche.multi_item.case2_oreo_pick_failure.v1", + "read_only": True, + "case": 2, + "item": 1, + "product": "oreo_gf", + "failure_tick": 10467, + "source_episode": str(SRC), + "evidence": { + "failed_recording": str(C / "rollout/recording_60hz.npz"), + "failed_contacts": str(C / "rollout/contacts.jsonl.gz"), + "source_recording": str(SRC / "recording_20hz.npz"), + "controls": str(C / "controls_item_02.npz"), + "accepted_chain_recording": str(OLD / "rollout/recording_60hz.npz"), + "accepted_chain_contacts": str(OLD / "rollout/contacts.jsonl.gz"), + "accepted_chain_job": str(OLD / "job.json"), + }, + "registration_and_control": { + "pick_source_frames": int(len(source["frame_index"])), + "actual_pick_ticks": int(len(current_pick)), + "three_ticks_per_source_frame": bool(len(current_pick) == 3 * len(source["frame_index"])), + "right_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, arm] ** 2))), + "right_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, arm]))), + "right_gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, gripper] ** 2))), + "right_gripper_tracking_max_rad": float(np.max(np.abs(q_error[:, gripper]))), + "target_initial_registration_error": pose_error(actual_target[0], registered_initial), + "shelf_board_contact_end_source_frame_failed": failed_contacts["shelf_board"]["source_frame_intervals"][0][1], + "shelf_board_contact_end_source_frame_accepted": accepted_contacts["shelf_board"]["source_frame_intervals"][0][1], + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_base[:2].tolist(), + "actual_minus_requested_xy_m": (actual_base[:2] - desired_base[:2, 3]).tolist(), + "xy_error_m": float(np.linalg.norm(actual_base[:2] - desired_base[:2, 3])), + "yaw_error_rad": float(np.arctan2(np.sin(actual_yaw - desired_yaw), np.cos(actual_yaw - desired_yaw))), + "accepted_layout06_xy_error_m": float(np.linalg.norm(old_base[:2] - old_desired[:2, 3])), + "accepted_layout06_yaw_error_rad": float(np.arctan2(np.sin(old_yaw - old_desired_yaw), np.cos(old_yaw - old_desired_yaw))), + }, + "pickup_timing_and_outcome": { + "source_motion_first_frame": int(source_motion[0]), + "failed_motion_first_frame": int(actual_motion[0]), + "accepted_motion_first_frame": int(accepted_motion[0]), + "source_lift_first_frame": int(source_lift[0]), + "failed_lift_first_frame": int(actual_lift[0]), + "accepted_lift_first_frame": int(accepted_lift[0]), + "failed_initial_z_m": float(actual_target[0, 2]), + "failed_max_z_m": float(actual_target[maximum_frame, 2]), + "failed_max_lift_m": float(actual_target[maximum_frame, 2] - actual_target[0, 2]), + "failed_max_lift_source_frame": maximum_frame, + "failed_final_z_m": float(actual_target[-1, 2]), + "failed_final_translation_m": (actual_target[-1, :3] - actual_target[0, :3]).tolist(), + "source_final_translation_m": (source_target[-1, :3] - source_target[0, :3]).tolist(), + }, + "finger_evidence": { + "source_final_outer_inner_rad": [float(source["robot_joint_position_rad_or_m"][-1, outer]), float(source["robot_joint_position_rad_or_m"][-1, inner])], + "failed_final_outer_inner_rad": [float(current["q"][current_samples[-1], current_names.index(source_names[outer])]), float(current["q"][current_samples[-1], current_names.index(source_names[inner])])], + "interpretation": "The finger joints finish close to the source values. Unlike case 1 Zbar, this is not a closure stall caused by early external contact.", + }, + "contact_timeline": { + "failed": failed_contacts, + "accepted_layout06": accepted_contacts, + "causal_order": "Failed gripper contact ends at source frame 347.33; the first bracket contact is frame 349.0 and first floor contact is frame 353.67. Therefore bracket/floor contacts follow loss of grasp. The accepted chain retains gripper contact through frame 420.67 and has no bracket or floor contact during shelf_pick.", + }, + "pinch_contact_strength": { + "failed_lift": failed_lift_impulse, + "accepted_layout06_lift": accepted_lift_impulse, + "accepted_to_failed_mean_ratio": float( + accepted_lift_impulse["mean_aggregate_impulse_norm_per_contact_tick"] + / failed_lift_impulse["mean_aggregate_impulse_norm_per_contact_tick"] + ), + "interpretation": "Both fingers remain in contact over the lift interval, but the failed run's aggregate target/gripper impulse is much weaker than the accepted exact-base run. This directly supports a marginal pinch before the later slip.", + }, + "relative_grasp_comparison": relative_table, + "diagnosis": { + "finding": "The source controls, object registration, shelf contact timing, arm tracking, closure, and initial lift all reproduce the accepted chain. Oreo is lifted 58.5 mm, then loses both gripper contacts during extraction and subsequently strikes the bracket and floor.", + "cause_assessment": "The 2.019 mm / 0.000734 rad return residual is the only measured setup discrepancy large enough to distinguish this run from the exact-base accepted chain. It shifts the initial source-relative hand/target transform by about 1.50 mm, delays first gripper contact by about 1.33 source frames, and produces a 5.64x weaker mean aggregate target/gripper impulse during lift frames 211-300. This makes arrival residual the best-supported cause of a marginal pinch that slips at frames 346-348; the causal attribution to base error remains an inference, while the weak pinch and subsequent slip are directly measured.", + "not_supported": [ + "No premature shelf/bracket collision: shelf-board contact ends at exactly frame 212.33 in failed and accepted runs, while bracket contact starts only after grasp loss.", + "No target-registration failure: initial registered pose error is about 10 micrometers.", + "No gross arm-control or finger-closure failure: arm tracking is sub-milliradian RMS and final fingers closely match source closure.", + ], + }, + "retry_recommendation": { + "recommend_fresh_physical_retry": True, + "runner": "Use the already-qualified precision empty-return controller without changing the Oreo162 source trajectory, shelf scene, or three-item tray plan.", + "mandatory_pre_pick_gate": { + "base_xy_error_max_m": 0.0005, + "base_yaw_error_max_rad": 0.0001, + "action_if_failed": "Keep the boundary frozen and re-run physical precision alignment; do not start source controls.", + }, + "runtime_grasp_gate": "Before leaving shelf_pick, require continued two-finger target contact and target-hand transform drift <=5 mm through the final extraction frames; abort as a physical pick failure if contact is lost.", + "rationale": "The same source succeeded from an effectively exact base in accepted layout06, and all first-three placement slots already passed offline solve. A precision-return retry isolates the demonstrated failure variable without adding a new grasp or maneuver.", + }, + } + out = C / "CASE2_PICK_FAILURE_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..25f4c191bbea4d809a905f9477a8004d09d472f5 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case4_live_grasp.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +"""Read-only measured-grasp comparison and geometric landing proposal for case 4.""" +import json +import pathlib + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.affinity import translate +from shapely.geometry import Polygon + +C = pathlib.Path(__file__).resolve().parent / "cases/layout_04" + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + result[:3, 3] = pose[:3] + return result + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + proposal_path = C / "STATION_SLOT_REACHABILITY_PROPOSAL.json" + job_path = C / "job.json" + controls_path = C / "controls_item_01.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + proposal = json.loads(proposal_path.read_text()) + job = json.loads(job_path.read_text()) + accepted = proposal["items"][0]["accepted"]["report"] + link = "/World/Robot/gripper_l_base_link" + link_index = checkpoint["robot_link_paths"].tolist().index(link) + target_index = checkpoint["target_paths"].tolist().index(job["items"][0]["target"]) + hand = pose_matrix(checkpoint["robot_link_pose"][-1, link_index]) + target = pose_matrix(checkpoint["target_pose_all"][-1, target_index]) + live_held = np.linalg.inv(hand) @ target + accepted_held = np.asarray(accepted["held"], dtype=float) + accepted_goal = np.asarray(accepted["goal"], dtype=float) + accepted_hand = accepted_goal @ np.linalg.inv(accepted_held) + compensated_goal = accepted_hand @ live_held + held_delta = np.linalg.inv(accepted_held) @ live_held + + vertices = np.load(controls_path)["target_local"] + world = (np.c_[vertices, np.ones(len(vertices))] @ compensated_goal.T)[:, :3] + footprint = Polygon(world[ConvexHull(world[:, :2]).vertices, :2]) + tray = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + reserved = [Polygon(item["placement_slot"]["footprint_xy"]) for item in job["items"][1:]] + # Smallest 1 mm grid shift that restores the required >12 mm reserved-slot gap. + feasible = [] + for dx in np.linspace(-0.08, 0.08, 161): + for dy in np.linspace(-0.08, 0.08, 161): + candidate = translate(footprint, dx, dy) + gaps = [candidate.distance(other) for other in reserved] + if tray.covers(candidate) and min(gaps) > 0.012: + feasible.append((float(np.hypot(dx, dy)), float(dx), float(dy), gaps, tray.exterior.distance(candidate))) + feasible.sort(key=lambda x: x[0]) + _, dx, dy, gaps, boundary = feasible[0] + shifted_goal = compensated_goal.copy() + shifted_goal[:2, 3] += [dx, dy] + measured_base = pose_matrix(checkpoint["base_pose"][-1]) + shifted_base = measured_base.copy() + shifted_base[:2, 3] += [dx, dy] + shifted_footprint = translate(footprint, dx, dy) + + result = { + "schema": "multi_item.case4_live_grasp_place_proposal.v1", + "read_only": True, + "case": 4, + "item": 0, + "product": job["items"][0]["product"], + "checkpoint_tick": int(checkpoint["tick"][-1]), + "evidence": { + "checkpoint": str(checkpoint_path), + "job": str(job_path), + "controls": str(controls_path), + "offline_station_proposal": str(proposal_path), + "exact_chain_proposal": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), + }, + "measured": { + "robot_base": measured_base.tolist(), + "gripper_link": link, + "gripper_world": hand.tolist(), + "target_world": target.tolist(), + "held_gripper_to_target": live_held.tolist(), + }, + "accepted_reference": { + "candidate": accepted["candidate"], + "goal": accepted_goal.tolist(), + "held_gripper_to_target": accepted_held.tolist(), + "held_delta_translation_m": float(np.linalg.norm(held_delta[:3, 3])), + "held_delta_rotation_rad": float(Rotation.from_matrix(held_delta[:3, :3]).magnitude()), + }, + "diagnosis": "The live held transform differs materially from the offline accepted-source grasp, so the old endpoint relation is not directly reusable. The runtime failure magnitude is consistent with this measured mismatch.", + "proposal": { + "derivation": "Keep the accepted candidate-8 hand endpoint, substitute the live measured held transform, then apply the smallest 1 mm-grid XY station/object shift satisfying tray inset and all future-slot gaps.", + "unshifted_compensated_goal": compensated_goal.tolist(), + "station_shift_world_xy_m": [dx, dy], + "proposed_place_base": shifted_base.tolist(), + "proposed_object_goal": shifted_goal.tolist(), + "footprint_xy": np.asarray(shifted_footprint.exterior.coords).tolist(), + "tray_inset_boundary_clearance_m": float(boundary), + "future_slot_gaps_m": [float(x) for x in gaps], + "footprint_inside_tray_inset": bool(tray.covers(shifted_footprint)), + "lowest_vertex_z_m": float(world[:, 2].min()), + "tray_floor_z_m": float(job["tray_floor_z"]), + }, + "validation_scope": { + "passed": ["measured transform reconstruction", "tray 8 mm inset coverage", "future footprint gap >12 mm"], + "requires_live_runner": ["endpoint IK from live state", "full-body/world collision path", "support settling"], + "claim": "geometrically screened repair seed; not an applied or native-validated repair", + }, + } + out = C / "CASE4_ITEM00_PLACE_REPAIR_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(out) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..1dbabda08c77871027c6550d4218827ad32cca31 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_compact_grasp.py @@ -0,0 +1,26 @@ +from pathlib import Path +import json,gzip +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;C=W/'cases/layout_05' +s=json.loads((C/'STATUS.json').read_text());assert s['control_ticks']==15144 and s['status']=='WAITING_FOR_PLANNING_REPAIR' +loaded=np.load(C/'rollout/checkpoint_60hz.npz');z={k:loaded[k] for k in ['target_paths','robot_link_paths','tick','robot_link_pose','target_pose_all']};j=json.loads((C/'job.json').read_text());target=j['items'][1]['target'];ti=z['target_paths'].tolist().index(target);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link') +def matrix(p): + m=np.eye(4);m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m +indices=np.flatnonzero(np.asarray(z['tick'])>=13936);held=np.array([np.linalg.inv(matrix(z['robot_link_pose'][k,hi]))@matrix(z['target_pose_all'][k,ti]) for k in indices]);drifts=np.linalg.norm(held[:,:3,3]-held[0,:3,3],axis=1) +rows=[];last=0 +try: + with gzip.open(C/'rollout/contacts.jsonl.gz','rt') as f: + for line in f: + try:r=json.loads(line) + except json.JSONDecodeError:continue + last=max(last,r['tick']) + if r['tick']>=14900 and target in r['actors']:rows.append(r) +except EOFError:pass +summary={} +for r in rows: + other=next((x for x in r['actors'] if x!=target),'');a=summary.setdefault(other,{'first_tick':r['tick'],'last_tick':r['tick'],'ticks':set(),'min_separation_m':1.,'impulse_norm_sum':0.}) + a['last_tick']=r['tick'];a['ticks'].add(r['tick']);a['min_separation_m']=min(a['min_separation_m'],r['min_separation_m']);a['impulse_norm_sum']+=float(np.linalg.norm(r['impulse_sum'])) +for a in summary.values():a['tick_count']=len(a.pop('ticks')) +report=dict(read_only=True,physics_state_unchanged=True,status=s,checkpoint_ticks=len(z['tick']),complete_contact_through_tick=last,target_hand_center_distance_m=float(np.linalg.norm(held[-1,:3,3])),held_translation_start=held[0,:3,3].tolist(),held_translation_end=held[-1,:3,3].tolist(),max_held_translation_drift_m=float(drifts.max()),held_rotation_drift_rad=float(Rotation.from_matrix(held[0,:3,:3].T@held[-1,:3,:3]).magnitude()),target_z_last120_min_max=[float(z['target_pose_all'][-120:,ti,2].min()),float(z['target_pose_all'][-120:,ti,2].max())],recent_contacts=summary,last_rows=rows[-12:]) +(C/'CASE5_ITEM01_COMPACT_GRASP_DIAGNOSTIC.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({k:v for k,v in report.items() if k!='last_rows'},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py new file mode 100644 index 0000000000000000000000000000000000000000..372ee219565e105a33b367648310ddf4fdd4b520 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case5_item1_compact_grasp.py @@ -0,0 +1,307 @@ +#!/usr/bin/env python3 +"""Read-only authoritative-pair diagnosis for case 5 item 1 compact contact.""" +import hashlib +import inspect +import json +import math +import os +import pathlib +import sys +import zlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) + +from pxr import Usd, UsdGeom # noqa: E402 +from collision_scene import Body, burial, collider_mesh, contact, matrix, rigid_part # noqa: E402 +from collision_acceptance import summarize # noqa: E402 +import collision_acceptance # noqa: E402 +import collision_scene # noqa: E402 +import collision_sweep # noqa: E402 + +TARGET = "/World/Assets/Tier2Items/zbar_t" +HAND = "/World/Robot/gripper_r_base_link" +INNER = "/World/Robot/gripper_r_inner_link4" +OUTER = "/World/Robot/gripper_r_outer_link4" +TORSO = "/World/Robot/body_link2" +TORSO_COLLIDER = TORSO + "/collisions/mesh_1/box" +TARGET_COLLIDER = TARGET + "/CanonicalGraspCollision" +FIRST_TICK = 15120 +LAST_TICK = 15144 + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def interpolate(a, b, u): + result = np.eye(4) + result[:3, 3] = (1.0 - u) * a[:3, 3] + u * b[:3, 3] + r0 = Rotation.from_matrix(a[:3, :3]) + delta = (r0.inv() * Rotation.from_matrix(b[:3, :3])).as_rotvec() + result[:3, :3] = (r0 * Rotation.from_rotvec(u * delta)).as_matrix() + return result + + +def local_collider(stage, path, owner_path): + cache = UsdGeom.XformCache() + prim = stage.GetPrimAtPath(path) + owner = stage.GetPrimAtPath(owner_path) + mesh, method, approximation = collider_mesh(prim) + authored_owner = rigid_part(matrix(cache.GetLocalToWorldTransform(owner))) + authored_shape = matrix(cache.GetLocalToWorldTransform(prim)) + mesh.apply_transform(np.linalg.inv(authored_owner) @ authored_shape) + return Body( + path, + mesh, + metadata={ + "owner": owner_path, + "collision_geometry_method": method, + "collision_approximation": approximation, + }, + ) + + +def partial_contacts(path): + raw = zlib.decompressobj(16 + zlib.MAX_WBITS).decompress(path.read_bytes()) + result = [] + incomplete = 0 + for line in raw.splitlines(): + try: + result.append(json.loads(line)) + except json.JSONDecodeError: + incomplete += 1 + return result, incomplete + + +def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contacts_path = C / "rollout/contacts.jsonl.gz" + scene_path = C / "rollout/scene.usda" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + status = json.loads((C / "STATUS.json").read_text()) + ticks = checkpoint["tick"].astype(int) + selected = np.flatnonzero((ticks >= FIRST_TICK) & (ticks <= LAST_TICK)) + if ticks[selected[0]] != FIRST_TICK or ticks[selected[-1]] != LAST_TICK: + raise RuntimeError("Pinned case 5 diagnostic interval is unavailable") + link_paths = checkpoint["robot_link_paths"].tolist() + target_paths = checkpoint["target_paths"].tolist() + torso_index = link_paths.index(TORSO) + hand_index = link_paths.index(HAND) + target_index = target_paths.index(TARGET) + + stage = Usd.Stage.Open(str(scene_path)) + torso = local_collider(stage, TORSO_COLLIDER, TORSO) + target = local_collider(stage, TARGET_COLLIDER, TARGET) + geometry = { + "torso": { + "path": TORSO_COLLIDER, + "method": torso.metadata["collision_geometry_method"], + "approximation": torso.metadata["collision_approximation"], + "watertight": bool(torso.mesh.is_watertight), + }, + "target": { + "path": TARGET_COLLIDER, + "method": target.metadata["collision_geometry_method"], + "approximation": target.metadata["collision_approximation"], + "watertight": bool(target.mesh.is_watertight), + }, + } + + exact_samples = [] + exact_hits = [] + q = checkpoint["q"] + torso_poses = checkpoint["robot_link_pose"][:, torso_index] + target_poses = checkpoint["target_pose_all"][:, target_index] + for position, frame in enumerate(selected): + next_frame = selected[position + 1] if position + 1 < len(selected) else frame + torso0, torso1 = pose_matrix(torso_poses[frame]), pose_matrix(torso_poses[next_frame]) + target0, target1 = pose_matrix(target_poses[frame]), pose_matrix(target_poses[next_frame]) + link_translation = float(np.linalg.norm(torso1[:3, 3] - torso0[:3, 3])) + target_translation = float(np.linalg.norm(target1[:3, 3] - target0[:3, 3])) + link_rotation = float( + (Rotation.from_matrix(torso0[:3, :3]).inv() * Rotation.from_matrix(torso1[:3, :3])).magnitude() + ) + target_rotation = float( + (Rotation.from_matrix(target0[:3, :3]).inv() * Rotation.from_matrix(target1[:3, :3])).magnitude() + ) + joint_step = float(np.max(np.abs(q[next_frame] - q[frame]))) + subdivisions = max( + 1, + math.ceil(joint_step / 0.03), + math.ceil(link_translation / 0.005), + math.ceil(target_translation / 0.005), + math.ceil(link_rotation / 0.03), + math.ceil(target_rotation / 0.03), + ) + for subdivision in range(subdivisions): + alpha = subdivision / subdivisions + torso.update(interpolate(torso0, torso1, alpha)) + target.update(interpolate(target0, target1, alpha)) + hit = contact(torso, target, containment=True) + row = { + "source_control_tick": int(ticks[frame]), + "next_control_tick": int(ticks[next_frame]), + "alpha": float(alpha), + "reviewer_frame": float(ticks[frame] - 1 + alpha * (ticks[next_frame] - ticks[frame])), + "triangle_intersection": hit, + } + exact_samples.append(row) + if hit is not None: + row["sampled_burial"] = burial(torso, target, max_points=2048) + exact_hits.append(row) + + native_rows, incomplete = partial_contacts(contacts_path) + target_rows = [] + inner_ticks = set() + outer_ticks = set() + torso_rows = [] + for row in native_rows: + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(TARGET, values): + continue + target_rows.append(row) + tick = int(row["tick"]) + if touches(INNER, values): + inner_ticks.add(tick) + if touches(OUTER, values): + outer_ticks.add(tick) + if touches(TORSO, values): + torso_rows.append(row) + body_ticks = sorted({int(row["tick"]) for row in torso_rows}) + bilateral = sorted(inner_ticks & outer_ticks) + + hand_relative = [] + for frame in selected: + hand_relative.append( + np.linalg.inv(pose_matrix(checkpoint["robot_link_pose"][frame, hand_index])) + @ pose_matrix(checkpoint["target_pose_all"][frame, target_index]) + ) + hand_relative = np.asarray(hand_relative) + shift = np.linalg.norm(hand_relative[:, :3, 3] - hand_relative[0, :3, 3], axis=1) + rotation_shift = np.asarray( + [ + (Rotation.from_matrix(hand_relative[0, :3, :3]).inv() * Rotation.from_matrix(value[:3, :3])).magnitude() + for value in hand_relative + ] + ) + + pair = { + "a": TORSO_COLLIDER + "#native_robot_collider", + "b": TARGET_COLLIDER + "#dynamic_physical_collider", + "category": "robot_target", + "sample_count": len(exact_hits), + "first_frame": exact_hits[0]["reviewer_frame"] if exact_hits else None, + "last_frame": exact_hits[-1]["reviewer_frame"] if exact_hits else None, + "a_collision_geometry_method": geometry["torso"]["method"], + "b_collision_geometry_method": geometry["target"]["method"], + "a_collision_approximation": geometry["torso"]["approximation"], + "b_collision_approximation": geometry["target"]["approximation"], + } + authoritative_input = { + "static_collider_coverage": {"complete": True}, + "all_scene_pairs_checked": True, + "solid_containment_checked": True, + "frame_stride": 1, + "swept_sampling": True, + "complete_trajectory_checked": True, + "pairs": [pair] if exact_hits else [], + } + authoritative_decision = summarize(authoritative_input, contact_tolerance_mm=0.5) + + result = { + "schema": "multi_item.case5_item01_compact_grasp_diagnostic.v1", + "read_only": True, + "decision": "CONFIRMED_PHYSICAL_PREFIX_FAILURE" if exact_hits else "NO_EXACT_INTERSECTION_IN_PINNED_INTERVAL", + "status_at_probe": status, + "item_index": 1, + "target": TARGET, + "interval_control_ticks": [FIRST_TICK, LAST_TICK], + "inputs": { + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "partial_contacts": str(contacts_path), + "partial_contacts_sha256": hashlib.sha256(contacts_path.read_bytes()).hexdigest(), + "discarded_incomplete_tail_rows": incomplete, + "scene": str(scene_path), + "scene_sha256": hashlib.sha256(scene_path.read_bytes()).hexdigest(), + }, + "native_contact_evidence": { + "target_torso_contact_tick_range": [body_ticks[0], body_ticks[-1]] if body_ticks else None, + "target_torso_distinct_control_ticks": len(body_ticks), + "target_torso_callback_rows": len(torso_rows), + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in torso_rows) if torso_rows else None, + "sum_impulse_norm_over_callback_rows": float(sum(np.linalg.norm(row["impulse_sum"]) for row in torso_rows)), + "bilateral_active_finger_ticks_in_interval": [tick for tick in bilateral if FIRST_TICK <= tick <= LAST_TICK], + "bilateral_active_finger_all_interval_ticks": all(tick in bilateral for tick in range(FIRST_TICK, LAST_TICK + 1)), + "target_to_hand_max_translation_shift_over_interval_m": float(np.max(shift)), + "target_to_hand_final_translation_shift_m": float(shift[-1]), + "target_to_hand_max_rotation_shift_rad": float(np.max(rotation_shift)), + }, + "authoritative_geometry_reproduction": { + "geometry": geometry, + "method": "The same collision_scene.collider_mesh exact authored cube boundaries, triangle contact/containment test, 5 mm literal-link/target translation step, 0.03 rad rotation/joint step, and collision_acceptance.summarize policy used by the native world reviewer.", + "sample_count": len(exact_samples), + "exact_intersection_count": len(exact_hits), + "exact_intersections": exact_hits, + "pair_category": "robot_target", + "acceptance_result": authoritative_decision, + }, + "policy_interpretation": { + "robot_target_is_forbidden": True, + "active_finger_10mm_policy_applies": False, + "default_0p5mm_contact_margin_applies": False, + "reason": "collision_acceptance treats robot_target as a forbidden non-contact category. The target/body_link2 pair is neither source/tray support nor the assigned distal link4 grasp pair; exact triangle crossing is a hard failure regardless of its shallow sampled depth.", + }, + "repair_decision": { + "retained_grasp": bool(all(tick in bilateral for tick in range(FIRST_TICK, LAST_TICK + 1))), + "safe_replan_may_continue_this_prefix_for_acceptance": False if exact_hits else None, + "recommended_action": "Preserve this native capture as a physical failure and use the screened proposal only in a fresh retry whose compact path avoids torso contact." if exact_hits else "No conclusion beyond the pinned interval.", + "no_active_case_write_or_motion_performed": True, + }, + "reviewer_sources": { + "collision_scene_sha256": hashlib.sha256(pathlib.Path(collision_scene.__file__).read_bytes()).hexdigest(), + "collision_sweep_sha256": hashlib.sha256(pathlib.Path(collision_sweep.__file__).read_bytes()).hexdigest(), + "collision_acceptance_sha256": hashlib.sha256(pathlib.Path(collision_acceptance.__file__).read_bytes()).hexdigest(), + }, + "limitations": [ + "Only the requested ticks 15120..15144 and the reported physical target/body_link2 collider pair are replayed here; the eventual independent reviewer still checks the whole episode and every in-scope pair.", + "Sampled burial is a lower bound. The acceptance decision relies on exact triangle crossing in exact authored cube boundaries, not on the burial magnitude.", + ], + } + output = W / "CASE5_ITEM01_COMPACT_GRASP_DIAGNOSTIC.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print( + json.dumps( + { + "output": str(output), + "decision": result["decision"], + "native": result["native_contact_evidence"], + "exact_intersection_count": len(exact_hits), + "exact_frames": [row["reviewer_frame"] for row in exact_hits], + "acceptance_geometry_status": authoritative_decision["geometry_status"], + "hard_failures": authoritative_decision["hard_failures"], + }, + indent=2, + ) + ) + raise SystemExit(0 if exact_hits else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..f203b8a79e747ad172f143d539105ab0dfbf7ed2 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_case9_item2_pick.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +"""Reproduce the immutable case 9 HoneyMaid false-pick diagnosis.""" +import gzip +import hashlib +import json +import pathlib +import zlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_09" +SOURCE = pathlib.Path( + "/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table50_20261001/cases/layout_18/rollout" +) +TARGET = "/World/Assets/Tier2Items/honeymaid" +OREO = "/World/Assets/Tier2Items/oreo_gf" + + +def decode(value): + return value.decode() if isinstance(value, bytes) else str(value) + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def load_open_gzip(path): + raw = zlib.decompressobj(16 + zlib.MAX_WBITS).decompress(path.read_bytes()) + rows = [] + incomplete = 0 + for line in raw.splitlines(): + try: + rows.append(json.loads(line)) + except json.JSONDecodeError: + incomplete += 1 + return rows, incomplete + + +def load_closed_gzip(path): + with gzip.open(path, "rt") as stream: + return [json.loads(line) for line in stream] + + +def target_category(row): + other = next(actor for actor in row["actors"] if actor != TARGET) + if other.startswith("/World/Robot"): + collider = next(value for value in row["colliders"] if value.startswith("/World/Robot")) + if "gripper_r_inner_link4" in collider: + return "right_inner_link4" + if "gripper_r_outer_link4" in collider: + return "right_outer_link4" + return "robot_other" + if other == OREO: + return "oreo" + if "ShelvingUnit" in other: + return "source_shelf" + return other + + +def target_tick_categories(rows): + result = {} + for row in rows: + if TARGET not in row.get("actors", []): + continue + result.setdefault(int(row["tick"]), set()).add(target_category(row)) + return result + + +def range_for(categories, name): + ticks = sorted(tick for tick, values in categories.items() if name in values) + return { + "first_tick": ticks[0] if ticks else None, + "last_tick": ticks[-1] if ticks else None, + "distinct_control_ticks": len(ticks), + } + + +def main(): + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contacts_path = C / "rollout/contacts.jsonl.gz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + phases = np.array([decode(value) for value in checkpoint["phase"]]) + ticks = checkpoint["tick"].astype(int) + item2 = np.flatnonzero(checkpoint["item_index"] == 2) + target_index = checkpoint["target_paths"].tolist().index(TARGET) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_pose = checkpoint["target_pose_all"][:, target_index] + hand_pose = checkpoint["robot_link_pose"][:, hand_index] + current_rows, incomplete = load_open_gzip(contacts_path) + current_categories = target_tick_categories(current_rows) + last_complete_tick = max(current_categories) + current_window = range(last_complete_tick - 29, last_complete_tick + 1) + simultaneous = sorted( + tick + for tick, values in current_categories.items() + if {"right_inner_link4", "right_outer_link4"} <= values + ) + current_relative = [ + np.linalg.inv(matrix(hand_pose[index])) @ matrix(target_pose[index]) for index in item2 + ] + final_relative = current_relative[-1] + relative_translation_drift = np.array( + [np.linalg.norm(value[:3, 3] - final_relative[:3, 3]) for value in current_relative] + ) + relative_rotation_drift = np.array( + [Rotation.from_matrix(value[:3, :3].T @ final_relative[:3, :3]).magnitude() for value in current_relative] + ) + + robot_oreo = [ + row + for row in current_rows + if OREO in row.get("actors", []) and any(actor.startswith("/World/Robot") for actor in row.get("actors", [])) + ] + outer_oreo = [ + row + for row in robot_oreo + if any("gripper_r_outer_link4" in collider for collider in row.get("colliders", [])) + ] + + source_recording_path = SOURCE / "recording_60hz.npz" + source_contacts_path = SOURCE / "contacts.jsonl.gz" + source_recording = np.load(source_recording_path, allow_pickle=True) + source_phases = np.array([decode(value) for value in source_recording["phase"]]) + source_pick = np.flatnonzero(source_phases == "shelf_pick") + source_pick_ticks = source_recording["tick"][source_pick].astype(int) + source_last30 = set(source_pick_ticks[-30:].tolist()) + source_rows = load_closed_gzip(source_contacts_path) + source_categories = target_tick_categories(source_rows) + + def window_count(categories, name, window): + return sum(name in categories.get(tick, set()) for tick in window) + + result = { + "schema": "multi_item.case9_item2_false_pick_diagnosis.v1", + "read_only": True, + "decision": "PHYSICAL_PICK_FAILURE_NOT_CARRY_PLANNING_BOUNDARY", + "case": "layout_09", + "item_index": 2, + "target": TARGET, + "obstructing_unrequested_prop": OREO, + "frozen_status": json.loads((C / "STATUS.json").read_text()), + "inputs": { + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "open_contact_stream": str(contacts_path), + "open_contact_stream_sha256": hashlib.sha256(contacts_path.read_bytes()).hexdigest(), + "discarded_incomplete_tail_rows": incomplete, + "accepted_source_recording": str(source_recording_path), + "accepted_source_contacts": str(source_contacts_path), + }, + "current_native_evidence": { + "item2_phase_tick_range": [int(ticks[item2[0]]), int(ticks[item2[-1]])], + "target_initial_z_m": float(target_pose[item2[0], 2]), + "target_maximum_z_m": float(np.max(target_pose[item2, 2])), + "target_final_z_m": float(target_pose[item2[-1], 2]), + "height_above_item2_initial_at_final_m": float(target_pose[item2[-1], 2] - target_pose[item2[0], 2]), + "right_inner_target_contact": range_for(current_categories, "right_inner_link4"), + "right_outer_target_contact": range_for(current_categories, "right_outer_link4"), + "simultaneous_bilateral_target_contact": { + "first_tick": simultaneous[0] if simultaneous else None, + "last_tick": simultaneous[-1] if simultaneous else None, + "distinct_control_ticks": len(simultaneous), + }, + "source_shelf_target_contact": range_for(current_categories, "source_shelf"), + "oreo_target_contact": range_for(current_categories, "oreo"), + "final_30_complete_contact_ticks": { + "range": [current_window.start, current_window.stop - 1], + "right_inner_ticks": window_count(current_categories, "right_inner_link4", current_window), + "right_outer_ticks": window_count(current_categories, "right_outer_link4", current_window), + "simultaneous_bilateral_ticks": sum( + {"right_inner_link4", "right_outer_link4"} <= current_categories.get(tick, set()) + for tick in current_window + ), + "source_shelf_ticks": window_count(current_categories, "source_shelf", current_window), + "oreo_ticks": window_count(current_categories, "oreo", current_window), + }, + "right_outer_gripper_oreo_contact": { + "first_tick": min(int(row["tick"]) for row in outer_oreo), + "last_tick": max(int(row["tick"]) for row in outer_oreo), + "rows": len(outer_oreo), + "precedes_oreo_target_contact": min(int(row["tick"]) for row in outer_oreo) + < range_for(current_categories, "oreo")["first_tick"], + }, + "target_to_hand_nonrigidity_during_item2": { + "median_translation_drift_from_final_m": float(np.median(relative_translation_drift)), + "maximum_translation_drift_from_final_m": float(np.max(relative_translation_drift)), + "median_rotation_drift_from_final_rad": float(np.median(relative_rotation_drift)), + "maximum_rotation_drift_from_final_rad": float(np.max(relative_rotation_drift)), + }, + }, + "accepted_honeymaid_source_baseline": { + "source_case": "shelf_to_table50_20261001/layout_18", + "final_30_shelf_pick_tick_range": [min(source_last30), max(source_last30)], + "right_inner_ticks": window_count(source_categories, "right_inner_link4", source_last30), + "right_outer_ticks": window_count(source_categories, "right_outer_link4", source_last30), + "simultaneous_bilateral_ticks": sum( + {"right_inner_link4", "right_outer_link4"} <= source_categories.get(tick, set()) + for tick in source_last30 + ), + "source_shelf_ticks": window_count(source_categories, "source_shelf", source_last30), + "bilateral_continues_through_shelf_exit": True, + }, + "causal_sequence": [ + "Right outer gripper first contacts the unrequested Oreo at tick 21549.", + "Oreo first contacts HoneyMaid at tick 21601.", + "A one-sided HoneyMaid/right-inner contact begins at tick 21763; the later right-outer contact does not overlap it.", + "All HoneyMaid/gripper contact ends by tick 22239 while HoneyMaid remains in shelf and Oreo contact through the final complete callback tick 22901.", + "The height-only lift signal therefore came from obstructed object motion, not a retained grasp.", + ], + "repair_recommendation": { + "initial_scene": "Deactivate/remove the unrequested Oreo distractor before a fresh native retry; preserve every requested prop and physical setting.", + "prelaunch": "Screen every transferred pick approach/closure/lift path against all live non-target props plus robot/self/wrist geometry in the actual composed scene.", + "runtime_pick_gate": "Require sustained active-gripper contact and loss of source support in addition to retained lift; store the evidence window in ITEM_EVENTS/CHECKPOINT.", + "no_local_carry_repair": True, + "no_reset_or_active_case_write_performed": True, + }, + "limitations": [ + "The open native contact gzip ends with one incomplete JSON row; only complete rows are counted.", + "The failed capture remains authoritative evidence; this report does not alter or replay it.", + ], + } + output = W / "CASE09_ITEM02_FALSE_PICK_DIAGNOSIS.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"output": str(output), "decision": result["decision"], "current": result["current_native_evidence"]["final_30_complete_contact_ticks"], "source": result["accepted_honeymaid_source_baseline"]}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb6d277d547c873b89331651153546a87f88741 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/diagnose_pick_base_registration.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +"""Read-only audit of multi-item pickup stations against their source shelf frames.""" +import glob +import json +import math +import pathlib +import sys + +import numpy as np +from pxr import Usd, UsdGeom +from scipy.spatial import ConvexHull +from shapely.geometry import Point, Polygon +from shapely.ops import nearest_points + +W = pathlib.Path(__file__).resolve().parent +ROOT = W.parents[1] +OLD = ROOT / "work/shelf_to_table50_20261001" +ACTIVE = (1, 3, 4, 6, 7, 8, 9) +sys.path.insert(0, str(W)) +from geometry_transfer import board_path, registration # noqa: E402 +from place_tray_layout import mesh_world_points # noqa: E402 + + +def matrix(stage, path): + value = UsdGeom.Xformable(stage.GetPrimAtPath(path)).ComputeLocalToWorldTransform(Usd.TimeCode.Default()) + return np.asarray(value, dtype=float).T + + +def polygon(stage, path): + points = mesh_world_points(stage, stage.GetPrimAtPath(path))[:, :2] + return Polygon(points[ConvexHull(points).vertices]).buffer(0) + + +def rigid_error(a, b): + delta = np.linalg.inv(a) @ b + return { + "translation_m": float(np.linalg.norm(delta[:3, 3])), + "yaw_rad": float(abs(math.atan2(delta[1, 0], delta[0, 0]))), + "max_abs_matrix": float(np.max(np.abs(a - b))), + } + + +def main(): + old_jobs = [] + for path in sorted((OLD / "cases").glob("layout_*/job.json")): + data = json.loads(path.read_text()) + old_jobs.append((path, data)) + summary = { + "schema": "multi_item.pick_base_registration_audit.v1", + "read_only": True, + "active_cases": list(ACTIVE), + "method": "Recompute each raw source->released shelf rigid registration; independently transfer every accepted old-chain initial_base sharing the exact pick_source; compare current target/base relative transforms. Measure pickup-base point clearance to the released CoffeeTable hull.", + "cases": [], + } + for case_id in ACTIVE: + case = W / "cases" / f"layout_{case_id:02d}" + job_path = case / "job.json" + job = json.loads(job_path.read_text()) + room = Usd.Stage.Open(job["layout"]) + composed = Usd.Stage.Open(job["scene"]) + table = polygon(room, "/World/Assets/CoffeeTable/Mesh") + shelf = polygon(room, board_path(room)) + row = { + "case": case_id, + "job": str(job_path), + "layout": job["layout"], + "source_layout_id": job.get("source_layout_id"), + "coffee_table_hull_xy": np.asarray(table.exterior.coords).tolist(), + "shelf_hull_xy": np.asarray(shelf.exterior.coords).tolist(), + "items": [], + } + for index, item in enumerate(job["items"]): + source_path = pathlib.Path(item["pick_source"]) + source = Usd.Stage.Open(str(source_path / "scene.usda")) + spec = json.loads((source_path / "episode.json").read_text()) + shelf_delta, shelf_reg = registration(source, room, board_path(source)) + source_base = matrix(source, "/World/Robot") + expected_base = shelf_delta @ source_base + current_base = np.asarray(item["pick_base"], dtype=float) + current_target = matrix(composed, item["target"]) + source_target = matrix(source, spec["target"]["prim_path"]) + expected_target = shelf_delta @ source_target + p = Point(current_base[:2, 3]) + closest = nearest_points(table, p)[0] + escape = current_base[:2, 3] - np.asarray(closest.coords[0]) + escape_norm = float(np.linalg.norm(escape)) + if escape_norm > 1e-12: + escape /= escape_norm + yaw = math.atan2(current_base[1, 0], current_base[0, 0]) + side_plus = np.array([-math.sin(yaw), math.cos(yaw)]) + exact_matches = [] + for prior_path, prior in old_jobs: + if prior.get("pick_source") != item["pick_source"]: + continue + prior_stage = Usd.Stage.Open(prior["layout"]) + accepted_delta, accepted_reg = registration(prior_stage, room, board_path(prior_stage)) + transferred = accepted_delta @ np.asarray(prior["initial_base"], dtype=float) + exact_matches.append({ + "prior_job": str(prior_path), + "prior_layout": prior["layout"], + "transferred_initial_base": transferred.tolist(), + "current_error": rigid_error(current_base, transferred), + "registration": accepted_reg, + }) + direct_error = rigid_error(current_base, expected_base) + target_error = rigid_error(current_target, expected_target) + rel_source = np.linalg.inv(source_base) @ source_target + rel_current = np.linalg.inv(current_base) @ current_target + relation_error = rigid_error(rel_current, rel_source) + row["items"].append({ + "item": index, + "product": item["product"], + "target": item["target"], + "pick_source": item["pick_source"], + "current_pick_base": current_base.tolist(), + "recomputed_pick_base": expected_base.tolist(), + "direct_source_registration_error": direct_error, + "target_source_registration_error": target_error, + "target_relative_to_base_error": relation_error, + "source_registration_correct": max(direct_error["max_abs_matrix"], target_error["max_abs_matrix"], relation_error["max_abs_matrix"]) < 1e-6, + "exact_accepted_chain_matches": exact_matches, + "coffee_table_point_clearance_m": float(table.distance(p)), + "coffee_table_nearest_xy": list(closest.coords[0]), + "coffee_table_escape_unit_xy": escape.tolist(), + "escape_dot_local_side_plus": float(escape @ side_plus), + "preferred_local_side_sign": "+" if float(escape @ side_plus) >= 0 else "-", + "shelf_point_clearance_m": float(shelf.distance(p)), + "shelf_registration": shelf_reg, + }) + row["all_source_registrations_correct"] = all(x["source_registration_correct"] for x in row["items"]) + (case / "PICK_BASE_REGISTRATION_DIAGNOSTIC.json").write_text(json.dumps(row, indent=2) + "\n") + summary["cases"].append(row) + (W / "PICK_BASE_REGISTRATION_AUDIT.json").write_text(json.dumps(summary, indent=2) + "\n") + concise = [] + for r in summary["cases"]: + items = [] + for x in r["items"]: + items.append({ + "item": x["item"], "product": x["product"], + "table_clearance_m": x["coffee_table_point_clearance_m"], + "side": x["preferred_local_side_sign"], + "direct_err": x["direct_source_registration_error"]["max_abs_matrix"], + "accepted_errs": [m["current_error"]["max_abs_matrix"] for m in x["exact_accepted_chain_matches"]], + }) + concise.append({"case": r["case"], "all_correct": r["all_source_registrations_correct"], "items": items}) + print(json.dumps({"report": str(W / "PICK_BASE_REGISTRATION_AUDIT.json"), "cases": concise}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/geometry_transfer.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/geometry_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..0c02bfb9306e2705a148761d223642b94f21ee05 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/geometry_transfer.py @@ -0,0 +1,43 @@ +"""Rigid geometry registration; never replace released furniture or scale props.""" +import numpy as np +from pxr import Usd,UsdGeom +from shapely.geometry import Polygon +from scipy.spatial import ConvexHull +from place_tray_layout import mesh_world_points + +def board_path(stage): + return next(str(p.GetPath()) for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Assets/ShelvingUnit')) if p.IsA(UsdGeom.Mesh) and 'shelf_boards' in str(p.GetPath())) + +def registration(source,destination,path): + a=mesh_world_points(source,source.GetPrimAtPath(path));b=mesh_world_points(destination,destination.GetPrimAtPath(path)) + if a.shape!=b.shape:raise ValueError('Furniture mesh correspondence changed') + ca=a.mean(0);cb=b.mean(0);u,_,vt=np.linalg.svd((a[:,:2]-ca[:2]).T@(b[:,:2]-cb[:2]));rotation=vt.T@u.T + if np.linalg.det(rotation)<0:raise ValueError('Furniture registration reflected') + m=np.eye(4);m[:2,:2]=rotation;m[:3,3]=cb-m[:3,:3]@ca + return m,dict(source_centroid=ca.tolist(),released_centroid=cb.tolist(),source_extents=np.ptp(a,axis=0).tolist(),released_extents=np.ptp(b,axis=0).tolist(),matrix=m.tolist(),furniture_preserved=True) + +def fit_inventory(stage,objects,target,rng,get_matrix,set_matrix): + """Scene-first supported packing, preserving identities, scales and rotations.""" + boards=mesh_world_points(stage,stage.GetPrimAtPath(board_path(stage)));rows=[];footprints={};bylayer={} + for obj in objects: + path=obj['prim_path'];v=mesh_world_points(stage,stage.GetPrimAtPath(path));bottom=v[:,2].min();z=boards[:,2][np.argmin(abs(boards[:,2]-bottom))] + if abs(z-bottom)>.006:raise ValueError('Source item height not supported in released shelf') + layer=round(float(z),4);surface=boards[abs(boards[:,2]-z)<1e-4,:2];floor=Polygon(surface[ConvexHull(surface).vertices]).buffer(-.004) + poly=Polygon(v[ConvexHull(v[:,:2]).vertices,:2]);footprints[path]=(poly,floor,layer);bylayer.setdefault(layer,[]).append(path) + for layer,paths in bylayer.items(): + placed=[] + paths.sort(key=lambda p:p!=target) + for path in paths: + poly,floor,_=footprints[path];original=get_matrix(stage,path);chosen=None + for attempt in range(4000): + if attempt==0:shift=np.zeros(2) + elif path==target:break + else: + lo=np.array(floor.bounds[:2]);hi=np.array(floor.bounds[2:]);shift=rng.uniform(lo,hi)-np.array(poly.centroid.coords[0]) + from shapely.affinity import translate + proposed=translate(poly,*shift) + if floor.covers(proposed) and all(proposed.distance(p)>.012 for p in placed):chosen=(shift,proposed);break + if chosen is None:raise ValueError('Released shelf cannot support sampled inventory: '+path) + shift,proposed=chosen;updated=original.copy();updated[:2,3]+=shift;set_matrix(stage,path,updated);placed.append(proposed) + rows.append(dict(path=path,xy_shift=shift.tolist(),support_layer=layer,attempt=attempt)) + return dict(policy='scene_first_supported_inventory_no_scaling_v1',items=rows,target_pose_preserved=True) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/grasp_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/grasp_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..cae3f6f9bcdba8840ac201d4af1c174740814f22 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/grasp_stability.py @@ -0,0 +1,73 @@ +"""Measured held-object slip and high-frequency hand motion for every item cycle.""" +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np +from scipy.ndimage import uniform_filter1d +from scipy.spatial.transform import Rotation + +from multi_validation import items, recording_contract + + +def _one(z, phases, item_index, target_pose_all, item, index): + carry = { + f"item_{index:02d}_shelf_exit_reverse", + f"item_{index:02d}_carry_raise", + f"item_{index:02d}_carry_compact", + f"item_{index:02d}_forward_navigation", + } + ids = np.flatnonzero((item_index == index) & np.isin(phases, list(carry))) + if len(ids) < 10: + return dict(item_index=index, target=item["target"], passed=False, reason="Missing item navigation samples") + path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + paths = z["robot_link_paths"].tolist() + if path not in paths: + return dict(item_index=index, target=item["target"], passed=False, reason=f"Missing hand link {path}") + hp = z["robot_link_pose"][ids, paths.index(path)] + tp = target_pose_all[ids, index] + bp = z["base_pose"][ids] + relative = Rotation.from_quat(hp[:, 3:]).inv().apply(tp[:, :3] - hp[:, :3]) + slip = np.linalg.norm(relative - relative[0], axis=1) + hand_in_base = Rotation.from_quat(bp[:, 3:]).inv().apply(hp[:, :3] - bp[:, :3]) + residual = hand_in_base - uniform_filter1d(hand_in_base, size=7, axis=0, mode="nearest") + amplitude = np.linalg.norm(residual, axis=1) + relative_hand = Rotation.from_quat(bp[:, 3:]).inv() * Rotation.from_quat(hp[:, 3:]) + angles = (relative_hand[0].inv() * relative_hand).as_rotvec() + angular_residual = np.linalg.norm(angles - uniform_filter1d(angles, size=7, axis=0, mode="nearest"), axis=1) + bad = (amplitude > .005) | (angular_residual > np.deg2rad(3)) + run = longest = 0 + for value in bad: + run = run + 1 if value else 0 + longest = max(longest, run) + return dict( + item_index=index, target=item["target"], hand=item["hand"], + passed=bool(slip.max() <= .025 and longest < 6), native_hz=60, + navigation_samples=len(ids), first_source_control_tick=int(ids[0] + 1), + last_source_control_tick=int(ids[-1] + 1), + maximum_object_in_hand_translation_drift_m=float(slip.max()), + maximum_hand_highpass_residual_m=float(amplitude.max()), + maximum_hand_highpass_angular_residual_rad=float(angular_residual.max()), + angular_highpass_limit_rad=float(np.deg2rad(3)), + hand_highpass_p99_m=float(np.percentile(amplitude, 99)), + maximum_consecutive_highpass_exceedance_ticks=longest, + slip_limit_m=.025, hand_highpass_limit_m=.005, + sustained_threshold_ticks=6, filter_window_ticks=7) + + +def metrics(z, requested): + phases, item_index, _, target_pose_all = recording_contract(z, requested) + phases = np.asarray(phases) + rows = [_one(z, phases, item_index, target_pose_all, item, i) for i, item in enumerate(requested)] + return dict(passed=all(row["passed"] for row in rows), items=rows, + semantics="Measured hand pose relative to moving base and each active target pose relative to its assigned hand; all item carries use the unchanged single-item thresholds.") + + +if __name__ == "__main__": + case = Path(sys.argv[1]); out = case / "rollout"; path = out / "recording_60hz.npz" + job = json.loads((case / "job.json").read_text()) + report = metrics(np.load(path, allow_pickle=False), items(job)) + report["recording_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + (out / "GRASP_STABILITY_AUDIT.json").write_text(json.dumps(report, indent=2) + "\n") + sys.exit(0 if report["passed"] else 1) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/launch.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..727c3e0a0179c694c662fc62728c02efcacab992 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/launch.py @@ -0,0 +1,63 @@ +"""Launch one isolated continuous episode without disturbing preserved old attempts.""" +import fcntl,hashlib,json,os,subprocess,sys,time +from pathlib import Path +W=Path(__file__).resolve().parent;R=W.parents[1] +OLD=R/'work/shelf_to_table_multi10_20261005' +C=R/'work/throughput_12h_20260912' +layout,gpu=map(int,sys.argv[1:]);assert gpu in range(8) +case=W/'cases'/f'layout_{layout:02d}';owner=f'multi10-stable-layout{layout}-pid{os.getpid()}' +container=f'ziche_shelf_chain50_gpu{gpu}' +(W/'leases').mkdir(exist_ok=True) +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +def read(p):return json.loads(p.read_text()) +def frozen_owned(pid): + try: + proc=Path('/proc')/str(pid);state=next(x for x in (proc/'status').read_text().splitlines() if x.startswith('State:')) + cmd=(proc/'cmdline').read_bytes().replace(b'\0',b' ').decode() + return 'T (stopped)' in state and str(OLD) in cmd or 'T (stopped)' in state and pid==875607 and 'run_precision_return_diagnostic_v2.py' in cmd + except (FileNotFoundError,StopIteration):return False +lock=(C/f'locks/gpu{gpu}.lock').open('a+');reservation=C/f'reservations/gpu{gpu}.json' +lease=W/f'leases/gpu{gpu}.json';shared_claim=False +while True: + fcntl.flock(lock,fcntl.LOCK_EX) + live=read(reservation) + uuid=subprocess.check_output(['nvidia-smi','-i',str(gpu),'--query-gpu=uuid','--format=csv,noheader'],text=True).strip() + apps=subprocess.check_output(['nvidia-smi','--query-compute-apps=gpu_uuid,pid','--format=csv,noheader'],text=True) + pids=[int(line.split(',')[1]) for line in apps.splitlines() if line.split(',')[0].strip()==uuid] + free=int(subprocess.check_output(['nvidia-smi','-i',str(gpu),'--query-gpu=memory.free','--format=csv,noheader,nounits'],text=True).strip()) + preserved_reservation=bool(live.get('reserved') and str(live.get('scope','')).startswith(str(OLD)+'/')) + own_busy=lease.exists() and read(lease).get('active',False) + if not own_busy and (not live.get('reserved') or preserved_reservation) and all(frozen_owned(pid) for pid in pids) and free>=16000: + if subprocess.check_output(['docker','inspect','--format','{{.State.Running}}',container],text=True).strip()!='true':subprocess.run(['docker','start',container],check=True,timeout=60) + visible=subprocess.check_output(['docker','exec',container,'nvidia-smi','--query-gpu=uuid','--format=csv,noheader'],text=True).strip();assert visible==uuid + if not preserved_reservation: + write(reservation,dict(reserved=True,physical_gpu=gpu,container=container,owner=owner,pid=os.getpid(),scope=str(case),started_unix=time.time()));shared_claim=True + write(lease,dict(active=True,physical_gpu=gpu,owner=owner,pid=os.getpid(),case=str(case),preserved_stopped_pids=pids,preserved_global_reservation=live if preserved_reservation else None,free_mib_at_start=free,started_unix=time.time())) + fcntl.flock(lock,fcntl.LOCK_UN);break + fcntl.flock(lock,fcntl.LOCK_UN) + write(case/'STATUS.json',dict(status='WAIT_GPU',phase='gpu_reservation',gpu=gpu,updated=time.time(),current_pids=pids,free_mib=free)) + time.sleep(15) +try: + if (case/'rollout').exists():raise RuntimeError('Existing attempt is preserved; fresh launch may not overwrite a rollout.') + (case/'repairs').mkdir(exist_ok=True) + snapshot=case/'code_snapshot';snapshot.mkdir(exist_ok=False) + for p in W.glob('*.py'):(snapshot/p.name).write_bytes(p.read_bytes()) + write(case/'CODE_HASHES.json',{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in snapshot.glob('*.py')}) + subprocess.run([sys.executable,'-m','py_compile',*[str(p) for p in snapshot.glob('*.py')]],check=True) + cmd=['docker','exec','--user','0:0','-e','CHAIN_PROJECT_ROOT='+str(R),'-e','CHAIN_RENDER=1','-e','OMP_NUM_THREADS=2','-e','OPENBLAS_NUM_THREADS=1','-e','MKL_NUM_THREADS=1',container,'/isaac-sim/python.sh',str(snapshot/'run_episode.py'),str(case)] + write(case/'COMMAND.json',cmd);write(case/'STATUS.json',dict(status='INITIALIZING_PHYSICS',phase='startup',gpu=gpu,updated=time.time())) + with (case/'simulator.log').open('w') as stream:result=subprocess.run(cmd,stdout=stream,stderr=subprocess.STDOUT) + status=read(case/'STATUS.json') + if result.returncode or status.get('status') not in ['FAILED','PHYSICS_COMPLETE_UNVERIFIED','PLANNING_BLOCKED']: + write(case/'STATUS.json',dict(status='FAILED',phase='simulator_exit',error=f'Exit {result.returncode}; inspect simulator.log',gpu=gpu,updated=time.time())) +except Exception as error: + write(case/'STATUS.json',dict(status='FAILED',phase='launcher',error=repr(error),gpu=gpu,updated=time.time()));raise +finally: + subprocess.run(['docker','exec','--user','0:0',container,'/bin/chown','-R',f'{os.getuid()}:{os.getgid()}',str(case)],check=False) + fcntl.flock(lock,fcntl.LOCK_EX) + live=read(reservation) + if shared_claim and live.get('owner')==owner:write(reservation,dict(reserved=False,physical_gpu=gpu,container=container,owner=None,pid=None,previous_owner=owner,released_unix=time.time())) + owned=read(lease) + if owned.get('owner')==owner:owned.update(active=False,released_unix=time.time());write(lease,owned) + fcntl.flock(lock,fcntl.LOCK_UN) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/motion_limits.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/motion_limits.py new file mode 100644 index 0000000000000000000000000000000000000000..b59d93b1743935eb9afd69fb28ad70eb241a2e72 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/motion_limits.py @@ -0,0 +1,4 @@ +"""Reference command envelope; does not change authored drive or joint parameters.""" +import numpy as np +LOWER=np.array([-3.139847,-2.089159,-3.099705,-1.480039,-3.099705,-1.740093,-3.099705,-3.139847,-1.480039,-3.099705,-1.480039,-3.099705,-1.740093,-3.099705,0,0,-np.pi/2,-.349066,0,0]) +UPPER=np.array([3.139847,1.480039,3.099705,1.480039,3.099705,1.740093,3.099705,3.139847,2.089159,3.099705,1.480039,3.099705,1.740093,3.099705,.55,np.pi/2,np.pi/2,.523599,np.pi/4,np.pi/4]) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_motion.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..f68d248ca26a527b8ace9614bcc494dca99fc78e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_motion.py @@ -0,0 +1,168 @@ +"""Collision-screened empty-hand return motion for continuous multi-item runs.""" +import math,time +import numpy as np +from scipy.spatial.transform import Rotation as Rot + +from carry_transition import body_route,compact_postures +from navigation import angle_delta + + +class EmptyClearance: + """The carry full-body checker without an attached-object surrogate.""" + def __init__(self,stage,robot,names,prop_poses,deadline_s=900): + from pxr import Usd,UsdGeom,UsdPhysics + from collision_scene import Body,collider_mesh,matrix,rigid_ancestor,contact + from native_planner import build + + self.deadline=time.monotonic()+deadline_s + self.robot=robot;self.names=names;self.contact=contact + self.self_check=build(stage);self.cache=UsdGeom.XformCache() + self.environment=[];self.results={};self.posture_cache={};self.last=None + for prim in Usd.PrimRange(stage.GetPseudoRoot(),Usd.TraverseInstanceProxies()): + path=str(prim.GetPath()) + if path.startswith('/World/Robot/') or not prim.HasAPI(UsdPhysics.CollisionAPI):continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False:continue + mesh,_,_=collider_mesh(prim);world=matrix(self.cache.GetLocalToWorldTransform(prim));owner=rigid_ancestor(prim) + if owner and str(owner.GetPath()) in prop_poses: + authored=matrix(self.cache.GetLocalToWorldTransform(owner));authored[:3,:3]/=np.linalg.norm(authored[:3,:3],axis=0) + world=prop_poses[str(owner.GetPath())]@np.linalg.inv(authored)@world + mesh.apply_transform(world) + # Match carry preflight: the real floor is checked by the released + # floor polygon while raised furniture remains full 3-D geometry. + if mesh.bounds[1,2]<.08:continue + self.environment.append(Body(path,mesh)) + self.low=np.array([b.lo for b in self.environment]);self.high=np.array([b.hi for b in self.environment]) + + def check(self,q,base): + if time.monotonic()>self.deadline:raise RuntimeError('Empty-hand geometry preflight exceeded its budget') + q=np.asarray(q);base=np.asarray(base);key=(q.tobytes(),base.tobytes()) + if key in self.results: + okay,self.last=self.results[key];return okay + okay=self.check_uncached(q,base);self.results[key]=(okay,self.last);return okay + + def check_uncached(self,q,base): + key=q.tobytes() + if key not in self.posture_cache: + local=self.robot.fk(self.names,q,np.eye(4));okay,detail=self.self_check(local,self.robot.root) + self.posture_cache[key]=(local,okay,detail) + local,okay,detail=self.posture_cache[key] + if not okay:self.last=dict(kind='self_or_wrist',detail=detail);return False + fk={name:base@transform for name,transform in local.items()} + for body in self.self_check.bodies:body.update(fk[body.name.replace('/World/Robot',self.robot.root,1)]) + for body in self.self_check.bodies: + indices=np.flatnonzero(np.all(self.high>=body.lo,axis=1)&np.all(self.low<=body.hi,axis=1)) + for index in indices: + other=self.environment[index] + if self.contact(body,other,containment=True) is not None: + self.last=dict(kind='environment',robot=body.name,obstacle=other.name);return False + self.last=None;return True + + def segment(self,q0,q1,b0,b1): + y0=Rot.from_matrix(b0[:3,:3]).as_euler('xyz')[2];y1=Rot.from_matrix(b1[:3,:3]).as_euler('xyz')[2] + dy=angle_delta(y1,y0) + steps=max(1,math.ceil(np.linalg.norm(b1[:3,3]-b0[:3,3])/.025),math.ceil(abs(dy)/math.radians(3)),math.ceil(np.max(abs(q1-q0))/.02)) + fixed=np.array_equal(q0,q1) + for u in np.linspace(0,1,steps+1): + b=b0.copy();b[:3,3]=(1-u)*b0[:3,3]+u*b1[:3,3];b[:3,:3]=Rot.from_euler('z',y0+u*dy).as_matrix() + if not self.check(q0 if fixed else q0*(1-u)+q1*u,b):return False + return True + + +def _behind(base,distance): + result=np.asarray(base,dtype=float).copy();yaw=Rot.from_matrix(result[:3,:3]).as_euler('xyz')[2] + result[:2,3]-=distance*np.array([math.cos(yaw),math.sin(yaw)]) + return result + + +def _lateral_transfers(next_pick_base,job): + """Yield shelf transfer poses that can approach around nearby furniture.""" + yaw=Rot.from_matrix(next_pick_base[:3,:3]).as_euler('xyz')[2] + heading=np.array([math.cos(yaw),math.sin(yaw)]) + lateral=np.array([-heading[1],heading[0]]) + offsets=job.get('return_lateral_transfer_offsets',[ + [0.,0.],[0.,-.10],[.25,.10],[-.25,.10], + [.35,.35],[-.35,.35],[.50,.35],[-.50,.35], + [.35,.55],[-.35,.55],[.55,.55],[-.55,.55], + [.40,.75],[-.40,.75],[.65,.75],[-.65,.75], + [.35,-.10],[-.35,-.10],[.50,0.],[-.50,0.], + ]) + for side,back in offsets: + result=next_pick_base.copy() + result[:2,3]=next_pick_base[:2,3]-float(back)*heading+float(side)*lateral + yield result,dict(side_m=float(side),back_m=float(back)) + + +def plan_return(stage,robot,names,q,base,prop_poses,next_pick_base,next_pick_q,release,job): + """Plan table retreat, raised transit, bridge, and exact shelf approach. + + Every segment uses EmptyClearance. The returned primitives carry a + ``bridge_before`` marker on the final shelf approach; execution must stop + at that boundary and move from the compact posture to ``next_pick_q``. + """ + from shapely.geometry import Polygon,LineString,Point + from shapely.ops import unary_union + + started=time.monotonic();q=np.asarray(q,dtype=float);base=np.asarray(base,dtype=float) + next_pick_base=np.asarray(next_pick_base,dtype=float);next_pick_q=np.asarray(next_pick_q,dtype=float) + checker=EmptyClearance(stage,robot,names,prop_poses,deadline_s=float(job.get('return_plan_budget_s',900))) + floor=unary_union([Polygon(f).buffer(0) for f in release['floors']]).buffer(-.34) + high=q.copy();high[names.index('idx01_body_joint1')]=.55 + failures=[] + for retreat_distance in job.get('return_exit_candidates',[.45,.60,.80,.30,.20,.10,.05]): + exit_base=_behind(base,retreat_distance) + if not floor.covers(LineString([base[:2,3],exit_base[:2,3]])):continue + if not checker.segment(q,q,base,exit_base): + failures.append(dict(stage='table_retreat',distance=retreat_distance,detail=checker.last));continue + if not checker.segment(q,high,exit_base,exit_base): + failures.append(dict(stage='raise',distance=retreat_distance,detail=checker.last));continue + for compact,posture in compact_postures(robot,names,high,exit_base): + if not checker.segment(high,compact,exit_base,exit_base): + failures.append(dict(stage='compact',distance=retreat_distance,posture=posture,detail=checker.last));continue + for standoff_distance in job.get('shelf_standoff_candidates',[.40,.30,.50,.65,.80,1.0]): + standoff=_behind(next_pick_base,standoff_distance) + if not floor.covers(Point(standoff[:2,3])):continue + # These endpoint gates do not depend on the room route. Check + # them first so an impossible pickup posture/approach does not + # consume a full lattice-search budget. + if not checker.segment(compact,next_pick_q,standoff,standoff): + failures.append(dict(stage='pickup_posture_bridge',distance=retreat_distance,standoff=standoff_distance,detail=checker.last));continue + if not checker.segment(next_pick_q,next_pick_q,standoff,next_pick_base): + failures.append(dict(stage='pickup_base_approach',distance=retreat_distance,standoff=standoff_distance,detail=checker.last));continue + route,detail=body_route(checker,compact,exit_base,standoff,release['floors'],budget=float(job.get('return_route_budget_s',120))) + if route is None: + failures.append(dict(stage='room_route',distance=retreat_distance,standoff=standoff_distance,detail=detail));continue + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2] + approach_yaw=Rot.from_matrix(next_pick_base[:3,:3]).as_euler('xyz')[2] + primitives=[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1,return_stage='table_retreat')] + primitives += [dict(p,return_stage='room_navigation') for p in route] + primitives.append(dict(kind='drive',goal=[*next_pick_base[:2,3],approach_yaw],direction=1,return_stage='shelf_approach',bridge_before=True)) + report=dict(policy='empty_hand_full_body_return_v1',table_retreat_m=retreat_distance,shelf_standoff_m=standoff_distance,standoff_base=standoff.tolist(),posture=posture,route_search=detail,failures=failures,planning_seconds=time.monotonic()-started,all_live_props_checked=True,no_attached_object=True) + return compact,primitives,report + # A coffee table can block every collinear shelf standoff even + # though the exact pickup station itself is valid. In that case, + # route to a lateral transfer pose, bridge there to the next pickup + # posture, and plan a second fully screened route to the exact base. + lateral_budget=float(job.get('return_lateral_route_budget_s',60)) + for transfer,offset in _lateral_transfers(next_pick_base,job): + if not floor.covers(Point(transfer[:2,3])):continue + if not checker.check(compact,transfer): + failures.append(dict(stage='lateral_transfer_compact',distance=retreat_distance,offset=offset,detail=checker.last));continue + if not checker.check(next_pick_q,transfer): + failures.append(dict(stage='lateral_transfer_pick_posture',distance=retreat_distance,offset=offset,detail=checker.last));continue + if not checker.segment(compact,next_pick_q,transfer,transfer): + failures.append(dict(stage='lateral_transfer_bridge',distance=retreat_distance,offset=offset,detail=checker.last));continue + route,route_detail=body_route(checker,compact,exit_base,transfer,release['floors'],budget=lateral_budget) + if route is None: + failures.append(dict(stage='lateral_transfer_route',distance=retreat_distance,offset=offset,detail=route_detail));continue + approach,approach_detail=body_route(checker,next_pick_q,transfer,next_pick_base,release['floors'],budget=lateral_budget) + if approach is None: + failures.append(dict(stage='lateral_pickup_route',distance=retreat_distance,offset=offset,detail=approach_detail));continue + if not approach:raise RuntimeError('Lateral pickup route unexpectedly returned no primitives') + approach=[dict(p) for p in approach];approach[0]['bridge_before']=True + yaw=Rot.from_matrix(base[:3,:3]).as_euler('xyz')[2] + primitives=[dict(kind='drive',goal=[*exit_base[:2,3],yaw],direction=-1,return_stage='table_retreat')] + primitives += [dict(p,return_stage='room_navigation') for p in route] + primitives += [dict(p,return_stage='shelf_approach') for p in approach] + report=dict(policy='empty_hand_full_body_lateral_return_v2',table_retreat_m=retreat_distance,lateral_transfer=offset,transfer_base=transfer.tolist(),posture=posture,route_search=route_detail,pickup_route_search=approach_detail,failures=failures,planning_seconds=time.monotonic()-started,all_live_props_checked=True,no_attached_object=True,exact_pick_base_retained=True) + return compact,primitives,report + raise RuntimeError('No collision-screened empty-hand return route: '+str(failures[-8:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..8e26e6957723fc581c9cfeb7fec463e97a03249a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/multi_validation.py @@ -0,0 +1,301 @@ +"""Shared, strict helpers for the uninterrupted multi-item episode contract.""" +from __future__ import annotations + +import gzip +import json +import re +from pathlib import Path + +import numpy as np + + +ITEM_PHASE = re.compile(r"^item_(\d\d)_(.+)$") +CORE_PHASES = ( + "waist_to_selected_shelf", + "shelf_pick", + "shelf_exit_reverse", + "forward_navigation", + "bridge_to_place_initial", + "place_descent", + "place_release", + "release_retreat", + "settle", + "complete", +) +OPTIONAL_PHASES = {"carry_planning", "carry_raise", "carry_compact", "place_planning", "empty_return"} + + +def items(job): + result = job.get("items") + if not isinstance(result, list) or len(result) not in (3, 4): + raise ValueError("job.items must contain exactly three or four requested items") + required = {"target", "hand", "product", "pick_source", "pick_base", "controls_file", "destination_xyz"} + for i, row in enumerate(result): + missing = sorted(required - set(row)) + if missing: + raise ValueError(f"job.items[{i}] missing {missing}") + if row["hand"] not in ("l", "r"): + raise ValueError(f"job.items[{i}].hand is invalid") + targets = [row["target"] for row in result] + if len(set(targets)) != len(targets): + raise ValueError("Every requested target must be distinct") + if int(job.get("initial_tray_items", -1)) != 0: + raise ValueError("The multi-item task requires an initially empty tray") + return result + + +def controls_path(case, item): + path = Path(item["controls_file"]) + if not path.is_absolute(): + path = Path(case) / path + return path.resolve() + + +def controls(case, item): + path = controls_path(case, item) + data = np.load(path, allow_pickle=False) + missing = [key for key in ("pick", "place_initial", "target_local") if key not in data] + if missing: + raise ValueError(f"{path} missing controls arrays {missing}") + return data + + +def decoded(values): + return [x.decode() if isinstance(x, bytes) else str(x) for x in values.tolist()] + + +def recording_contract(z, requested): + n = len(z["q"]) + required = {"phase", "item_index", "active_target_path", "target_paths", "target_pose_all"} + missing = sorted(required - set(z.files)) + if missing: + raise ValueError(f"recording_60hz.npz missing multi-item fields {missing}") + paths = decoded(z["target_paths"]) + expected = [row["target"] for row in requested] + if paths != expected: + raise ValueError(f"target_paths are not in requested order: {paths!r}") + pose = np.asarray(z["target_pose_all"]) + if pose.shape != (n, len(requested), 7) or not np.isfinite(pose).all(): + raise ValueError(f"target_pose_all has invalid shape/value {pose.shape}") + item_index = np.asarray(z["item_index"], dtype=np.int64) + active = decoded(z["active_target_path"]) + if item_index.shape != (n,) or len(active) != n: + raise ValueError("item_index/active_target_path must have one value per control tick") + if np.any((item_index < -1) | (item_index >= len(requested))): + raise ValueError("item_index contains an out-of-range value") + phases = decoded(z["phase"]) + for tick, (index, path, phase) in enumerate(zip(item_index, active, phases)): + match = ITEM_PHASE.match(phase) + if phase == "initial_upper_shelf_posture": + authentic = (index == -1 and path == "") or (index == 0 and path == expected[0]) + if not authentic or any(ITEM_PHASE.match(x) for x in phases[:tick]): + raise ValueError(f"tick {tick} has invalid or non-prefix initial encoding") + continue + if index == -1: + raise ValueError(f"inactive tick {tick} occurs outside the initial posture: {phase}") + if path != expected[index]: + raise ValueError(f"tick {tick} active target does not match item_index") + if not match or int(match.group(1)) != index: + raise ValueError(f"tick {tick} phase is not prefixed for active item: {phase}") + return phases, item_index, active, pose + + +def compressed(values): + return list(dict.fromkeys(values)) + + +def item_phase_indices(phases, item_index, index, suffix=None): + mask = item_index == index + if suffix is not None: + mask &= np.asarray([p == f"item_{index:02d}_{suffix}" for p in phases]) + return np.flatnonzero(mask) + + +def validate_phase_order(phases, item_index, count): + details = [] + matches = [ITEM_PHASE.match(phase) for phase in phases] + first = np.flatnonzero([match is not None for match in matches]) + if not len(first): + raise ValueError("No active item ticks were recorded") + if any(p != "initial_upper_shelf_posture" for p in phases[: first[0]]): + raise ValueError("Only initial_upper_shelf_posture may precede item 0") + seen_items = compressed([int(match.group(1)) for match in matches if match]) + if seen_items != list(range(count)): + raise ValueError(f"item cycles are not continuous and ordered: {seen_items}") + for index in range(count): + indices = np.flatnonzero([(match is not None and int(match.group(1)) == index and item_index[tick] == index) for tick, match in enumerate(matches)]) + suffixes = [] + for tick in indices: + match = ITEM_PHASE.match(phases[tick]) + suffixes.append(match.group(2)) + unique = compressed(suffixes) + unknown = [name for name in unique if name not in set(CORE_PHASES) | OPTIONAL_PHASES] + if unknown: + raise ValueError(f"item {index} has unknown phases {unknown}") + positions = [] + for name in CORE_PHASES: + if name not in unique: + raise ValueError(f"item {index} is missing required phase {name}") + positions.append(unique.index(name)) + if positions != sorted(positions): + raise ValueError(f"item {index} core phases are out of order: {unique}") + has_return = "empty_return" in unique + if index < count - 1 and (not has_return or not (unique.index("settle") < unique.index("empty_return") < unique.index("complete"))): + raise ValueError(f"item {index} must return empty-handed to the next source before completion") + if index == count - 1 and has_return: + raise ValueError("The final item must complete at the table without an extra return") + if indices[-1] - indices[0] + 1 != len(indices): + raise ValueError(f"item {index} cycle is not contiguous") + details.append(dict(item_index=index, phases=unique, first_tick=int(indices[0] + 1), last_tick=int(indices[-1] + 1))) + return details + + +def audit_ranges(z, requested, minimum_shards=4, maximum_shards=16): + """Create target-homogeneous ranges; split long ranges without crossing item cycles.""" + _, item_index, _, _ = recording_contract(z, requested) + active = item_index.copy() + active[active < 0] = 0 + starts = np.r_[0, np.flatnonzero(active[1:] != active[:-1]) + 1] + ends = np.r_[starts[1:], len(active)] + ranges = [[int(a), int(b), int(active[a])] for a, b in zip(starts, ends)] + while len(ranges) < minimum_shards: + eligible = [(b - a, i) for i, (a, b, _) in enumerate(ranges) if b - a >= 2] + if not eligible: + break + _, i = max(eligible) + a, b, item = ranges[i] + middle = (a + b) // 2 + ranges[i : i + 1] = [[a, middle, item], [middle, b, item]] + if not (minimum_shards <= len(ranges) <= maximum_shards): + raise ValueError(f"Could not form {minimum_shards}..{maximum_shards} homogeneous audit shards") + covered = [tick for a, b, _ in ranges for tick in range(a, b)] + if covered != list(range(len(active))): + raise ValueError("Audit ranges do not cover every control tick exactly once") + return ranges + + +def load_contact_rows(path): + opener = gzip.open if str(path).endswith(".gz") else open + with opener(path, "rt") as stream: + return [json.loads(line) for line in stream if line.strip()] + + +def contact_capture_audit(out, requested, control_ticks, item_index=None, active_target=None, phases=None): + receipt_path = Path(out) / "CONTACT_CAPTURE.json" + contacts_path = Path(out) / "contacts.jsonl.gz" + if not receipt_path.exists() or not contacts_path.exists(): + return dict(passed=False, reason="Missing CONTACT_CAPTURE.json or contacts.jsonl.gz") + receipt = json.loads(receipt_path.read_text()) + expected = [row["target"] for row in requested] + rows = load_contact_rows(contacts_path) + captured = set() + invalid = [] + identity_mismatch = [] + for row_number, row in enumerate(rows, 1): + actors = list(map(str, row.get("actors", []))) + for target in expected: + if any(a == target or a.startswith(target + "/") or a.startswith(target + "#") for a in actors): + captured.add(target) + tick = int(row.get("tick", 0)) + if not 1 <= tick <= control_ticks: + invalid.append(row_number) + elif item_index is not None and active_target is not None: + if (int(row.get("item_index", -999)) != int(item_index[tick-1]) + or str(row.get("active_target_path", "")) != str(active_target[tick-1]) + or (phases is not None and str(row.get("phase", "")) != str(phases[tick-1]))): + identity_mismatch.append(row_number) + passed = ( + receipt.get("requested_targets") == expected + and receipt.get("filter_semantics") == "robot_or_any_requested_target" + and receipt.get("all_physics_ticks") is True + and int(receipt.get("dropped_rows", -1)) == 0 + and int(receipt.get("control_ticks", -1)) == control_ticks + and int(receipt.get("callback_rows", -1)) == len(rows) + and captured == set(expected) + and not invalid + and not identity_mismatch + ) + return dict(passed=passed, receipt=receipt, contact_rows=len(rows), targets_with_recorded_contacts=sorted(captured), invalid_tick_rows=invalid, active_item_identity_mismatch_rows=identity_mismatch) + + +def grasp_retention_audit(contact_rows, phases, item_index, requested): + """Reject height-only pickups without a retained, unsupported pinch. + + Contact callbacks can repeat within one 60 Hz control tick, so this gate + counts distinct control ticks. It mirrors the future producer gate while + remaining usable for already-running frozen snapshots that lack PICK_GATE + receipts. + """ + phases = np.asarray(phases) + item_index = np.asarray(item_index, dtype=np.int64) + details = [] + passed = True + + def touches(path, values): + return any(value == path or value.startswith(path + "/") or value.startswith(path + "#") for value in values) + + for index, item in enumerate(requested): + target = str(item["target"]) + hand = str(item["hand"]) + pick = item_phase_indices(phases, item_index, index, "shelf_pick") + if not len(pick): + details.append(dict(item_index=index, target=target, passed=False, reason="missing shelf_pick phase")) + passed = False + continue + end_tick = int(pick[-1] + 1) + window_start = max(int(pick[0] + 1), end_tick - 29) + tail_start = max(window_start, end_tick - 2) + environment_start = max(int(pick[0] + 1), end_tick - 14) + prefix = f"/World/Robot/gripper_{hand}" + inner_path = prefix + "_inner_link4" + outer_path = prefix + "_outer_link4" + inner = set() + outer = set() + environment = set() + for row in contact_rows: + tick = int(row.get("tick", 0)) + if tick < min(window_start, environment_start) or tick > end_tick: + continue + values = list(map(str, row.get("actors", []))) + list(map(str, row.get("colliders", []))) + if not touches(target, values): + continue + if tick >= window_start: + if touches(inner_path, values): + inner.add(tick) + if touches(outer_path, values): + outer.add(tick) + if tick >= environment_start: + if any(not (value == target or value.startswith(target + "/") or value.startswith(target + "#") + or value == "/World/Robot" or value.startswith("/World/Robot/")) for value in values): + environment.add(tick) + bilateral = sorted(inner & outer) + minimum = max(15, int(item.get("pick_grasp_contact_min_bilateral_ticks", 15))) + tail = [tick for tick in bilateral if tick >= tail_start] + item_passed = len(bilateral) >= minimum and bool(tail) and not environment + passed &= item_passed + details.append(dict( + item_index=index, + target=target, + active_hand=hand, + gripper_prefix=prefix, + shelf_pick_tick_range=[int(pick[0] + 1), end_tick], + window_control_ticks=30, + window_start_tick=window_start, + window_end_tick=end_tick, + inner_contact_ticks=sorted(inner), + outer_contact_ticks=sorted(outer), + simultaneous_bilateral_contact_ticks=bilateral, + simultaneous_bilateral_contact_tick_count=len(bilateral), + minimum_simultaneous_bilateral_ticks=minimum, + final_tail_control_ticks=3, + final_tail_bilateral_ticks=tail, + environment_clear_control_ticks=15, + environment_contact_ticks=sorted(environment), + passed=item_passed, + )) + return dict( + schema="ziche.multi_item.grasp_retention_audit.v1", + passed=bool(passed), + semantics="distinct 60 Hz control ticks; sustained opposing active-finger contact; final-tail contact; no target/source-or-clutter contact", + items=details, + ) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/native_planner.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/native_planner.py new file mode 100644 index 0000000000000000000000000000000000000000..6818134a1b771bfa03af1b8ade2c40150004fca1 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/native_planner.py @@ -0,0 +1,33 @@ +"""Planning filter using unchanged native self-collision geometry and exclusions.""" +import numpy as np +import sys,os +from pathlib import Path +project=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(Path(__file__).resolve().parents[2]))) +sys.path.insert(0,str(project/'work/shelf_training_visible360_20260916')) +from pxr import Usd,UsdGeom,UsdPhysics +import trimesh +from collision_scene import Body,collider_mesh,matrix,broad_overlap,contact,traversal +from robot_collision_profile import collision_disabled_joint_neighbors,audit_full_robot_collision_profile + +def build(stage): + cache=UsdGeom.XformCache();profile=audit_full_robot_collision_profile(stage);filtered={tuple(sorted(p)) for p in profile['filtered_body_pairs']};filtered.update(collision_disabled_joint_neighbors(stage));grouped={} + for path in profile['collision_enabled_paths']: + p=stage.GetPrimAtPath(path);owner=p + while owner and not owner.HasAPI(UsdPhysics.RigidBodyAPI):owner=owner.GetParent() + if not owner:raise ValueError(path) + mesh,_,_=collider_mesh(p);mesh.apply_transform(np.linalg.inv(matrix(cache.GetLocalToWorldTransform(owner)))@matrix(cache.GetLocalToWorldTransform(p)));grouped.setdefault(str(owner.GetPath()),[]).append(mesh) + from wrist_upright import camera_mount + mounts={h:camera_mount(stage,h) for h in ['l','r']} + bodies=[Body(p,trimesh.util.concatenate(ms),metadata={'link':p}) for p,ms in grouped.items()] + def check(fk,root): + for hand,mount in mounts.items(): + rotation=fk[root+'/gripper_'+hand+'_base_link'][:3,:3] + if (rotation@mount[:3,3])[2]<.010 or (rotation@mount[:3,1])[2]<.25:return False,dict(reason='wrist_not_upright',hand=hand) + for body in bodies:body.update(fk[body.name.replace('/World/Robot',root,1)]) + for i,a in enumerate(bodies): + for b in bodies[i+1:]: + if tuple(sorted((a.name,b.name))) in filtered or not broad_overlap(a,b):continue + if contact(a,b,containment=True) is not None:return False,dict(a=a.name,b=b.name) + return True,None + check.bodies=bodies + return check diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/navigation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/navigation.py new file mode 100644 index 0000000000000000000000000000000000000000..974c30a4a7d77370753c3a95ee86d9f96ecff76a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/navigation.py @@ -0,0 +1,132 @@ +"""Planar A* with collision-checked shortcutting; nonholonomic stop-turn-drive execution.""" +import heapq,math +import numpy as np + +def clear_segment(a,b,obstacles,radius=.30): + a=np.asarray(a);b=np.asarray(b);delta=b-a + for lo,hi in obstacles: + lo=np.asarray(lo)-radius;hi=np.asarray(hi)+radius;t0,t1=0.,1. + for k in range(2): + if abs(delta[k])<1e-12: + if a[k]hi[k]:t0,t1=1.,0.;break + else: + x,y=sorted(((lo[k]-a[k])/delta[k],(hi[k]-a[k])/delta[k]));t0=max(t0,x);t1=min(t1,y) + if t0<=t1:return False + return True + +def plan(start,goal,obstacles,radius=.30,resolution=.05): + start=np.asarray(start);goal=np.asarray(goal) + if clear_segment(start,goal,obstacles,radius):return [start.tolist(),goal.tolist()] + origin=np.minimum(start,goal)-3.;maximum=np.maximum(start,goal)+3.;shape=np.ceil((maximum-origin)/resolution).astype(int) + def point(n):return origin+np.asarray(n)*resolution + def cell(p):return tuple(np.rint((p-origin)/resolution).astype(int)) + s,g=cell(start),cell(goal) + if not clear_segment(start,point(s),obstacles,radius) or not clear_segment(point(g),goal,obstacles,radius):raise ValueError('Endpoint inside inflated obstacle') + queue=[(0.,s)];cost={s:0.};parent={};done=set() + while queue: + _,n=heapq.heappop(queue) + if n in done:continue + done.add(n) + if n==g:break + for dx,dy in [(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)]: + m=(n[0]+dx,n[1]+dy) + if any(m[k]<0 or m[k]>shape[k] for k in range(2)):continue + v=cost[n]+math.hypot(dx,dy)*resolution + if v>=cost.get(m,float('inf')):continue + if not clear_segment(point(n),point(m),obstacles,radius):continue + cost[m]=v;parent[m]=n;heapq.heappush(queue,(v+np.linalg.norm(point(m)-goal),m)) + if g not in done:raise ValueError('No clearance-safe planar path') + nodes=[g] + while nodes[-1]!=s:nodes.append(parent[nodes[-1]]) + route=[start.tolist()]+[point(n).tolist() for n in reversed(nodes)]+[goal.tolist()] + simple=[route[0]];i=0 + while ii+1 and not clear_segment(route[i],route[j],obstacles,radius):j-=1 + simple.append(route[j]);i=j + return simple + +def angle_delta(target,current):return math.atan2(math.sin(target-current),math.cos(target-current)) + +def motion_primitives(route, initial_yaw, final_yaw): + """Choose forward/reverse per straight leg to minimize total stationary turning.""" + points=[np.asarray(x,dtype=float) for x in route] + legs=[(a,b) for a,b in zip(points,points[1:]) if np.linalg.norm(b-a)>1e-6] + if not legs:return [dict(kind='turn',goal=[*points[-1],float(final_yaw)])] + headings=[math.atan2(*(b-a)[::-1]) for a,b in legs] + states=[(0.,initial_yaw,[])] + for heading in headings: + next_states=[] + for sign in (1,-1): + yaw=angle_delta(heading+(math.pi if sign<0 else 0),0) + best=min(states,key=lambda x:x[0]+abs(angle_delta(yaw,x[1]))) + next_states.append((best[0]+abs(angle_delta(yaw,best[1])),yaw,best[2]+[(yaw,sign)])) + states=next_states + best=min(states,key=lambda x:x[0]+abs(angle_delta(final_yaw,x[1]))) + primitives=[] + for (a,b),(yaw,sign) in zip(legs,best[2]): + primitives.extend([dict(kind='turn',goal=[*a,float(yaw)]),dict(kind='drive',goal=[*b,float(yaw)],direction=sign)]) + primitives.append(dict(kind='turn',goal=[*points[-1],float(final_yaw)])) + return primitives + +class PrimitiveController: + """Mutually exclusive stationary yaw and body-axis translation, bounded acceleration.""" + def __init__(self,primitive):self.primitive=primitive;self.speed=0.;self.done=False + def step(self,current,dt=1/60): + goal=np.asarray(self.primitive['goal']);yaw=float(current[2]);heading=np.array([math.cos(yaw),math.sin(yaw)]) + if self.primitive['kind']=='turn': + error=angle_delta(goal[2],yaw);desired=math.copysign(min(.25,max(.02,1.5*abs(error))),error) if abs(error)>.0015 else 0. + self.speed+=float(np.clip(desired-self.speed,-.3*dt,.3*dt));self.done=abs(error)<.003 and abs(self.speed)<.001 + return np.array([0.,0.,self.speed]) + error=goal[:2]-np.asarray(current[:2]);along=float(error@heading);lateral=float(error@np.array([-heading[1],heading[0]])) + if abs(lateral)>.015:raise RuntimeError(f'Straight drive lateral tracking error {lateral:.4f} m; replan instead of strafing') + if abs(angle_delta(goal[2],yaw))>.01:raise RuntimeError('Straight drive heading drift; stop and replan') + desired=math.copysign(min(.18,max(.008,min(1.5*abs(along),.8*math.sqrt(2*.12*abs(along))))),along) if abs(along)>.0015 else 0. + self.speed+=float(np.clip(desired-self.speed,-.12*dt,.12*dt));self.done=abs(along)<.003 and abs(self.speed)<.001 + return np.r_[heading*self.speed,0.] + +def command(current,goal,previous,dt=1/60): + """Stationary manipulation hold only. Never correct XY with a lateral command.""" + if np.linalg.norm(np.asarray(goal[:2])-current[:2])>.02:raise RuntimeError('Base drifted during stationary manipulation; navigation must be replanned') + if abs(angle_delta(goal[2],current[2]))>.02:raise RuntimeError('Base heading drifted during stationary manipulation') + return np.zeros(3) + + +def plan_release_polygons(start,goal,shapes,floors,radius=.34,resolution=.05): + """Use released per-mesh polygons and room floor, not a whole-group AABB.""" + from shapely.geometry import Polygon,LineString,Point + from shapely.ops import unary_union + from shapely.prepared import prep + blocked=unary_union([Polygon(s).buffer(0) for s in shapes]).buffer(radius) + free=unary_union([Polygon(f).buffer(0) for f in floors]).buffer(-radius).difference(blocked) + check=prep(free) + def clear(a,b):return check.covers(LineString([a,b])) if np.linalg.norm(np.asarray(a)-b)>1e-10 else check.covers(Point(a)) + start=np.asarray(start);goal=np.asarray(goal) + if not clear(start,start) or not clear(goal,goal):raise ValueError('Endpoint outside released collision-free floor') + if clear(start,goal):return [start.tolist(),goal.tolist()] + origin=np.array(free.bounds[:2]);shape=np.ceil((np.array(free.bounds[2:])-origin)/resolution).astype(int) + def point(n):return origin+np.asarray(n)*resolution + def cell(p):return tuple(np.rint((p-origin)/resolution).astype(int)) + source,target=cell(start),cell(goal) + if not clear(start,point(source)) or not clear(point(target),goal):raise ValueError('Endpoint grid connector blocked') + queue=[(0.,source)];cost={source:0.};parent={};done=set() + while queue: + _,n=heapq.heappop(queue) + if n in done:continue + done.add(n) + if n==target:break + for dx,dy in [(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)]: + m=(n[0]+dx,n[1]+dy) + if any(m[k]<0 or m[k]>shape[k] for k in range(2)):continue + value=cost[n]+math.hypot(dx,dy)*resolution + if value>=cost.get(m,float('inf')) or not clear(point(n),point(m)):continue + cost[m]=value;parent[m]=n;heapq.heappush(queue,(value+np.linalg.norm(point(m)-goal),m)) + if target not in done:raise ValueError('No route in released free-floor polygons') + nodes=[target] + while nodes[-1]!=source:nodes.append(parent[nodes[-1]]) + route=[start.tolist()]+[point(n).tolist() for n in reversed(nodes)]+[goal.tolist()];simple=[route[0]];i=0 + while ii+1 and not clear(route[i],route[j]):j-=1 + simple.append(route[j]);i=j + return simple diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..16e80f9d57784e3aa5cd5a3b5593dfe191614a5b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/official_lerobot_reader_qa.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +"""Check one staged multi-item episode with the official LeRobot reader.""" +import argparse +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +CAMERAS = { + "observation.images.top_head": (3, 800, 1280), + "observation.images.hand_left": (3, 480, 848), + "observation.images.hand_right": (3, 480, 848), +} + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + info = json.loads((args.dataset / "meta/info.json").read_text()) + expected = int(info["total_frames"]) + parquet_path = args.dataset / "data/chunk-000/episode_000000.parquet" + table = pq.read_table(parquet_path) + dataset = LeRobotDataset(f"local/shelf_to_table_multi10_stable_layout_{args.layout:02d}", + root=args.dataset, video_backend="pyav") + indices = sorted(set((0, expected // 2, expected - 1))) + samples, failures = [], [] + for index in indices: + item = dataset[index] + action_expected = np.asarray(table["action"][index].as_py(), dtype=np.float32) + state_expected = np.asarray(table["observation.state"][index].as_py(), dtype=np.float32) + action_actual = item["action"].detach().cpu().numpy() + state_actual = item["observation.state"].detach().cpu().numpy() + scalars = { + "frame_index": int(item["frame_index"]) == int(table["frame_index"][index].as_py()), + "episode_index": int(item["episode_index"]) == 0, + "source_control_tick": int(item["source_control_tick"]) == int(table["source_control_tick"][index].as_py()), + "item_index": int(item["item_index"]) == int(table["item_index"][index].as_py()), + "phase_index": int(item["phase_index"]) == int(table["phase_index"][index].as_py()), + "timestamp": abs(float(item["timestamp"]) - float(table["timestamp"][index].as_py())) <= 1e-6, + "simulator_time_s": abs(float(item["simulator_time_s"]) - float(table["simulator_time_s"][index].as_py())) <= 2e-6, + } + images = {} + for key, shape in CAMERAS.items(): + value = item[key] + passed = (tuple(value.shape) == shape and bool(torch.isfinite(value).all()) + and float(value.min()) >= 0.0 and float(value.max()) <= 1.0) + images[key] = {"passed": passed, "shape": list(value.shape), + "minimum": float(value.min()), "maximum": float(value.max()), + "mean": float(value.mean()), "standard_deviation": float(value.std())} + passed = (np.array_equal(action_actual, action_expected) and np.array_equal(state_actual, state_expected) + and all(scalars.values()) and all(row["passed"] for row in images.values()) + and bool(item["task"])) + row = {"index": index, "passed": passed, "action_shape": list(action_actual.shape), + "state_shape": list(state_actual.shape), "scalar_alignment": scalars, + "task": item["task"], "images": images} + samples.append(row) + if not passed: + failures.append(row) + passed = (len(dataset) == expected == len(table) and dataset.fps == 20 and not failures + and samples[0]["action_shape"] == [22] and samples[0]["state_shape"] == [23]) + report = {"schema": "ziche.multi10.official_lerobot_reader_qa.v1", + "created_utc": datetime.now(timezone.utc).isoformat(), "layout_id": args.layout, + "passed": passed, "dataset": str(args.dataset), + "reader": "lerobot LeRobotDataset(video_backend='pyav')", + "dataset_length": len(dataset), "parquet_rows": len(table), + "metadata_frames": expected, "fps": dataset.fps, "sample_indices": indices, + "samples": samples, "failures": failures, "parquet_sha256": sha(parquet_path), + "note": "First/middle/last rows and all three images decoded through the official reader; full sequential decode is reused by exact video hashes."} + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps({"output": str(args.output), "passed": passed}, indent=2)) + raise SystemExit(0 if passed else 1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/parallel_world_audit.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/parallel_world_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..727d57ad220f91d4ea9c9e6049b06ff6b51524c7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/parallel_world_audit.py @@ -0,0 +1,57 @@ +"""Disjoint control-tick shards; every interval retains its original next tick.""" +import sys,os,time,json,subprocess,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);out=case/'rollout' +from task_collision_scope import POLICY +from multi_validation import audit_ranges,items +shard_count=int(os.environ.get('CHAIN_WORLD_AUDIT_SHARDS','4'));assert 4<=shard_count<=16 +job=json.loads((case/'job.json').read_text());requested=items(job) +start=time.monotonic();z=np.load(out/'recording_60hz.npz',allow_pickle=False);n=len(z['q']);ranges=audit_ranges(z,requested,shard_count,16);shard_count=len(ranges);processes=[];logs=[] +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) +try: + for i,(a,b,item_index) in enumerate(ranges): + for name in [f'WORLD_COLLISION_AUDIT.part_{i}.json',f'WORLD_AUDIT_PROGRESS.part_{i}.json']: + p=out/name + if p.exists():p.rename(out/(name+'.previous_'+str(time.time_ns()))) + env=dict(os.environ,CHAIN_AUDIT_SHARD=str(i),CHAIN_AUDIT_START=str(a),CHAIN_AUDIT_END=str(b),CHAIN_AUDIT_ITEM=str(item_index),OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + log=(out/f'world_shard_{i}.log').open('w');logs.append(log);processes.append(subprocess.Popen([sys.executable,str(W/'audit_geometry.py'),str(case),'world'],env=env,stdout=log,stderr=subprocess.STDOUT)) + while any(p.poll() is None for p in processes): + if time.monotonic()-start>1050:raise TimeoutError('World audit exceeded 17.5 minutes; review required, never auto-pass') + checked=0 + for i,(a,b,item_index) in enumerate(ranges): + p=out/f'WORLD_COLLISION_AUDIT.part_{i}.json';progress=out/f'WORLD_AUDIT_PROGRESS.part_{i}.json' + if p.exists(): + part=json.loads(p.read_text());checked+=len(part['checked_frame_indices']) + if part['geometry_status']=='REJECT' and part['hard_failures']: + write(out/'WORLD_COLLISION_AUDIT.json',dict(schema='shelf_multi10.parallel_task_collision_summary.v2',geometry_status='REJECT',collision_scope=POLICY,requested_targets=[x['target'] for x in requested],all_scene_pairs_checked=False,complete_trajectory_checked=False,early_rejection=True,hard_failures=part['hard_failures'],unresolved_review=part['unresolved_review'],elapsed_seconds=time.monotonic()-start,source_shard=i,source_item_index=item_index)) + print('Early rejected by physical collision witness in shard',i,flush=True);sys.exit(0) + elif progress.exists():checked+=max(0,min(int(b-a),json.loads(progress.read_text())['frame']-int(a)+1)) + write(out/'WORLD_AUDIT_PROGRESS.json',dict(frame=checked,total=n,updated=time.time(),parallel_shards=shard_count,elapsed_seconds=time.monotonic()-start)) + time.sleep(2) + results=[] + for i,p in enumerate(processes): + if p.returncode:raise RuntimeError(f'Collision shard {i} failed; see world_shard_{i}.log') + results.append(json.loads((out/f'WORLD_COLLISION_AUDIT.part_{i}.json').read_text())) + failures=[];review=[];contacts=[];covered=[] + for i,r in enumerate(results): + a,b,item_index=ranges[i] + indices=r['checked_frame_indices'];assert len(set(indices))==len(indices) + assert all(int(a)<=x=len(yaw_options):theta+=np.deg2rad(rng.uniform(-12,12)) + points=vertices@Rot.from_euler('z',theta).as_matrix().T + footprint=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]+xy) + gap=min((footprint.distance(poly) for poly in all_obstacles),default=None) + if not floor.buffer(-.008).covers(footprint) or (gap is not None and gap<=.012):continue + candidates.append((xy,np.rad2deg(math.atan2(math.sin(theta-yaw),math.cos(theta-yaw))),gap)) + candidates.sort(key=lambda c:abs(c[1])*.001+np.linalg.norm(c[0]-center)) + if not candidates:raise RuntimeError('Reserved tray slot has no supported free placement after live-object clearance checks') + else: + candidates=destination_candidates(vertices,yaw,floor,all_obstacles,job['seed'],job['destination_xyz'][:2],0,count=160) + candidates.sort(key=lambda c:np.linalg.norm(c[0]-base[:2,3])) + failures=[] + fast=Arm(robot,names,q0,base,link,active) + def ik(q,g): + prev=q[active].copy() + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=100) + qq=q.copy();qq[active]=fit.x;f=fast.forward(fit.x)[0] + error=[np.linalg.norm(f[:3,3]-g[:3,3]),Rot.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude()] + return qq,error + for index,(xy,dyaw,gap) in enumerate(candidates): + goal=np.eye(4);goal[:3,:3]=Rot.from_euler('z',yaw+np.deg2rad(dyaw)).as_matrix();goal[:2,3]=xy + goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min() + end,err=ik(q0,goal@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='landing',error=err));continue + for clearance in [0.,.06,.12,.18]: + hover=goal.copy();hover[2,3]=max(goal[2,3]+clearance,object_pose[2,3]);near=goal.copy();near[2,3]+=.025 + segments=[(object_pose,near,8),(near,goal,4)] if clearance==0 else [(object_pose,hover,5),(hover,near,4),(near,goal,4)];q=q0.copy();planned=[];failed=False + for start,stop,secs in segments: + rots=Slerp([0,1],Rot.from_matrix([start[:3,:3],stop[:3,:3]])) + for t in np.linspace(0,1,secs*20): + u=10*t**3-15*t**4+6*t**5;world=np.eye(4);world[:3,:3]=rots(u).as_matrix();world[:3,3]=start[:3,3]*(1-u)+stop[:3,3]*u + q,err=ik(q,world@invheld) + if err[0]>.002 or err[1]>.02: + failures.append(dict(candidate=index,stage='path',frame=len(planned),error=err));failed=True;break + if pose_check is not None: + okay,detail=pose_check(robot.fk(names,q,base),robot.root) + if not okay: + failures.append(dict(candidate=index,stage='self_collision',frame=len(planned),detail=detail));failed=True;break + if world_check is not None and not world_check.check(q,base): + failures.append(dict(candidate=index,stage='world_collision',frame=len(planned),detail=world_check.last));failed=True;break + planned.append(q.copy()) + if failed:break + if failed:continue + retreat_goal=goal@invheld;retreat_goal[2,3]+=.15;retreat,error=ik(q,retreat_goal) + if error[0]>.002 or error[1]>.02: + failures.append(dict(candidate=index,stage='retreat',error=error));continue + if pose_check is not None: + valid=True + for t in np.linspace(0,1,61): + okay,detail=pose_check(robot.fk(names,q*(1-t)+retreat*t,base),robot.root) + if not okay:valid=False;break + if not valid: + failures.append(dict(candidate=index,stage='retreat_self_collision',detail=detail));continue + release_report=None + if release_sweep is not None: + release_ok,release_report=release_sweep.check(q,retreat,base,goal) + if not release_ok: + failures.append(dict(candidate=index,stage='open_gripper_release_or_retreat_world',detail=release_report));continue + elif job.get('require_release_sweep',False): + raise ValueError('Placement requires an open-gripper release sweep checker') + return np.array(planned),retreat,dict(candidate=index,goal=goal.tolist(),held=held.tolist(),prior_failures=failures,clutter_clearance=gap,policy='reserved_multi_item_slots_live_obstacles_v2_release_sweep',slot=slot,reserved_future_slots=len(reserved),hover_clearance_m=clearance,candidate_budget=int(job.get('placement_candidate_budget',800)) if slot else 160,release_sweep=release_report) + raise RuntimeError('All supported tray candidates unreachable: '+str(failures[:3]+failures[-3:])) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_multi.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_multi.py new file mode 100644 index 0000000000000000000000000000000000000000..abeb8466de5cff5a67266842abd9d9163e868c48 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_multi.py @@ -0,0 +1,201 @@ +"""Prepare one isolated multi-item shelf/tray case from accepted replay sources.""" +import sys,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation as Rot +from scipy.spatial import ConvexHull +from pxr import Usd,UsdGeom,UsdPhysics,UsdUtils,Sdf,Gf + +W=Path(__file__).resolve().parent; R=W.parents[1] +OLD=R/'work/shelf_to_table50_20261001' +sys.path[:0]=[str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop'),str(OLD)] +from shapely.geometry import Polygon +from shapely import affinity +from shapely.ops import unary_union +from place_tray_layout import scene_geometry,mesh_world_points +from geometry_transfer import registration,board_path + +def dump(p,d): p.write_text(json.dumps(d,indent=2)+'\n') +def matrix(stage,path): return np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(stage.GetPrimAtPath(path))).T +def set_matrix(stage,path,m): + p=stage.GetPrimAtPath(path); pm=np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(p.GetParent())).T + local=np.linalg.inv(pm)@m; x=UsdGeom.Xformable(p); x.ClearXformOpOrder(); x.AddTransformOp().Set(Gf.Matrix4d(local.T.tolist())) +def robot_matrix(spec): + m=np.eye(4); m[:3,:3]=Rot.from_quat(np.asarray(spec['robot']['base_wxyz'])[[1,2,3,0]]).as_matrix(); m[:3,3]=spec['robot']['base_xyz_m']; return m +def control_payload(source,names): + targets=[]; drive={} + with (source/'runtime_control_readback.jsonl').open() as stream: + for line in stream: + row=json.loads(line); by={r['joint']:r for r in row['rows']}; targets.append([by[n]['live_position_target_rad_or_m'] for n in names]) + if not drive: drive={n:by[n]['live_stage_drive_attributes'] for n in names} + q=np.asarray(targets); q[:,18:20]=np.clip(q[:,18:20],0,np.pi/4); return q,drive +def bounds(stage,path): + pts=mesh_world_points(stage,stage.GetPrimAtPath(path)); return pts.min(0),pts.max(0) +def intersects(a,b,pad=.012): + return all(a[0][k]-pad < b[1][k] and b[0][k]-pad < a[1][k] for k in range(3)) +DRINKS={'starbucks','coke_c3','sprite_c1','gatorade_m','gatorade_t','good2grow','grapefruit_can','cayman_jacked'} +def assign_slots(items,tray_polygon,place_xy): + """Search reserved, non-overlapping upright placements back-to-front.""" + pts=np.asarray(tray_polygon); floor=Polygon(pts).buffer(-.008); center=np.asarray(floor.centroid.coords[0]); cov=np.cov((pts-center).T); vals,vecs=np.linalg.eigh(cov); u=vecs[:,np.argmax(vals)]; v=np.array([-u[1],u[0]]) + if np.dot(np.asarray(place_xy)-center,v)<0: v=-v + u=np.array([v[1],-v[0]]) + for item in items: + local=np.load(item['controls_file'])['target_local'][:,:2]; item['_poly']=Polygon(local[ConvexHull(local).vertices]) + base_yaw=float(math.atan2(u[1],u[0])); grid_u=[-.11,-.075,-.04,0,.04,.075,.11]; grid_v=[-.06,-.03,0,.03,.06] + def candidates(item): + rows=[] + for vv in grid_v: + for uu in grid_u: + xy=center+uu*u+vv*v + yaws=[item['accepted_landing_yaw_rad'],item['accepted_landing_yaw_rad']+math.pi] if 'accepted_landing_yaw_rad' in item else [base_yaw,base_yaw+math.pi/2] + for yaw in yaws: + p=affinity.translate(affinity.rotate(item['_poly'],yaw*180/math.pi,origin=(0,0)),xy[0],xy[1]) + if floor.covers(p): rows.append((vv,abs(uu),uu,xy,yaw,p)) + return sorted(rows,key=lambda x:(x[0],x[1])) + chosen=[] + def search(i,last_v): + if i==len(items): return True + for vv,_,uu,xy,yaw,p in candidates(items[i]): + if vv+1e-8 str: + h = hashlib.sha256() + with path.open("rb") as f: + for block in iter(lambda: f.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def main() -> None: + records = [] + for case_id in range(1, 11): + src = OLD / "cases" / f"layout_{case_id:02d}" + dst = NEW / "cases" / f"layout_{case_id:02d}" + if dst.exists() and any(dst.iterdir()): + raise RuntimeError(f"refusing to overwrite nonempty {dst}") + dst.mkdir(parents=True, exist_ok=True) + + copied = [] + for name in INPUT_NAMES: + shutil.copy2(src / name, dst / name) + copied.append(name) + for p in sorted(src.glob("controls*.npz")): + shutil.copy2(p, dst / p.name) + copied.append(p.name) + + job = json.loads((src / "job.json").read_text()) + if case_id == 4: + # The first three completed and settled in one physical prefix; + # item 3 Pringles failed to lift. Keep it as a shelf distractor. + job["items"] = job["items"][:3] + job["tray_slots"] = job["tray_slots"][:3] + old_s, new_s = str(OLD), str(NEW) + job = json.loads(json.dumps(job).replace(old_s, new_s)) + job["status"] = "PREPARING" + job["attempt_index"] = 1 + (dst / "job.json").write_text(json.dumps(job, indent=2) + "\n") + + # The physical layer sublayers the case-local authored scene/profile. + physical = (dst / "scene_physical.usda").read_text().replace(old_s, new_s) + (dst / "scene_physical.usda").write_text(physical) + + status = { + "status": "PREPARING", + "phase": "fresh_initial_inputs", + "item_index": None, + "completed_objects": [], + "control_ticks": 0, + "source_case": str(src), + "runtime_state_reused": False, + } + (dst / "STATUS.json").write_text(json.dumps(status, indent=2) + "\n") + copied += ["job.json", "STATUS.json"] + records.append({ + "case_id": case_id, + "source_layout_id": job["source_layout_id"], + "item_count": len(job["items"]), + "products": [x["product"] for x in job["items"]], + "files": {name: sha(dst / name) for name in sorted(copied)}, + }) + + combos_path = NEW / "COMBINATIONS.json" + combos = json.loads(combos_path.read_text()) + for case in combos["cases"]: + if case["case_id"] == 4: + case["products"] = [p for p in case["products"] if p["product"] != "pring_bbq"] + combos["stable_retry_changes"] = { + "layout_03": {"inactive_nonrequested_prims": [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/Tier2Items/skit_littles", + ]}, + "layout_04": {"item_count": 3, "removed_requested_item": "pring_bbq", + "reason": "preserve three physically successful items; fourth pickup did not lift"}, + "layout_09": {"inactive_nonrequested_prims": ["/World/Assets/Tier2Items/oreo_gf"]}, + } + combos_path.write_text(json.dumps(combos, indent=2) + "\n") + manifest = { + "schema": "shelf_to_table_multi10.stable_input_manifest.v1", + "old_root_read_only": str(OLD), + "new_root": str(NEW), + "copy_policy": "initial inputs only; no rollout/checkpoint/live held transform reused", + "counts": {"cases": 10, "triples": 7, "quads": 3, "items": 33}, + "cases": records, + } + (NEW / "PREPARED_INPUT_MANIFEST.json").write_text(json.dumps(manifest, indent=2) + "\n") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_visual_qa.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_visual_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..52d4e8c646a17dcbf5b9619f47bc10629aeef344 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/prepare_visual_qa.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +"""Create read-only all-view startup and full-phase evidence panels.""" +import hashlib +import json +import sys +from pathlib import Path + +import av +import numpy as np +from PIL import Image, ImageDraw + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def sha(path): + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def selected_frames(path, indices): + wanted = set(indices) + result = {} + with av.open(str(path)) as container: + for index, frame in enumerate(container.decode(video=0)): + if index in wanted: + result[index] = frame.to_image().convert("RGB") + if len(result) == len(wanted): + break + if set(result) != wanted: + raise RuntimeError(f"{path}: missing selected frames {sorted(wanted - set(result))}") + return result + + +def panel(images, frames, labels, path, title): + cell_w, cell_h, label_h = 256, 160, 38 + canvas = Image.new("RGB", (cell_w * len(frames), 28 + len(CAMERAS) * (cell_h + label_h)), "black") + draw = ImageDraw.Draw(canvas) + draw.text((5, 5), title, fill="white") + for row, camera in enumerate(CAMERAS): + for column, (frame, label) in enumerate(zip(frames, labels)): + image = images[camera][frame].copy() + image.thumbnail((cell_w, cell_h)) + x = column * cell_w + (cell_w - image.width) // 2 + y0 = 28 + row * (cell_h + label_h) + canvas.paste(image, (x, y0 + label_h)) + draw.text((column * cell_w + 3, y0 + 2), f"{camera} f{frame}\n{label[:28]}", fill="white") + canvas.save(path, quality=92) + + +def main(): + case = Path(sys.argv[1]).resolve() + out = case / "rollout" + job = json.loads((case / "job.json").read_text()) + dataset = W / "staged_datasets" / f"layout_{int(job['layout_id']):02d}" + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + sample_rows = np.arange(2, len(recording["q"]), 3) + sampled_phases = recording["phase"][sample_rows].astype(str) + phase_names = list(dict.fromkeys(sampled_phases.tolist())) + phase_frames = [] + for phase in phase_names: + rows = np.flatnonzero(sampled_phases == phase) + phase_frames.append(int(rows[len(rows) // 2])) + first = list(range(min(10, len(sample_rows)))) + needed = sorted(set(first + phase_frames)) + evidence = W / "qa" / f"layout_{int(job['layout_id']):02d}" / "visual_evidence" + evidence.mkdir(parents=True, exist_ok=True) + decode = json.loads((out / "VIDEO_DECODE_AUDIT.json").read_text()) + images = {} + videos = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + digest = sha(video) + if digest != decode["videos"][camera]["sha256"]: + raise RuntimeError(f"{camera} video differs from fully decoded native evidence") + images[camera] = selected_frames(video, needed) + videos[camera] = {"path": str(video), "sha256": digest} + panels = [] + startup = evidence / "FIRST_0P5S_ALL_VIEWS.jpg" + panel(images, first, [f"startup {i}" for i in first], startup, + "Retained first 0.5 s: all native views") + panels.append({"kind": "startup", "path": str(startup), "sha256": sha(startup), + "frame_indices": first}) + for page, begin in enumerate(range(0, len(phase_frames), 12)): + frames = phase_frames[begin:begin + 12] + labels = phase_names[begin:begin + 12] + path = evidence / f"FULL_PHASE_ALL_VIEWS_{page + 1:02d}.jpg" + panel(images, frames, labels, path, f"Full continuous episode phase midpoints page {page + 1}") + panels.append({"kind": "phase", "path": str(path), "sha256": sha(path), + "frame_indices": frames, "phase_names": labels}) + receipt = { + "schema": "ziche.multi10_visual_panels.v1", "layout_id": int(job["layout_id"]), + "accepted": False, "case": str(case), "dataset": str(dataset), + "recording_sha256": sha(out / "recording_60hz.npz"), "videos": videos, + "phase_names": phase_names, "phase_midpoint_frames": phase_frames, "panels": panels, + "scope": "first retained 0.5 seconds and one midpoint from every ordered phase in all three views", + "requires": "independent human review and official LeRobot reader QA", + } + path = evidence / "PANELS.json" + path.write_text(json.dumps(receipt, indent=2) + "\n") + print(path) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/preroll_stability.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/preroll_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..bdc11d2f74ce3271ddbbe2e7e4534807229dffa3 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/preroll_stability.py @@ -0,0 +1,73 @@ +"""Geometry-based startup stability metrics shared by preroll runners/audits.""" +import re +import numpy as np +from pxr import Usd,UsdGeom +from scipy.spatial.transform import Rotation + +PASSIVE_RE=re.compile(r'/gripper_[lr]_(?:inner|outer)_link[234]$') +CAMERAS=[('/World/Robot/head_link2','/World/Robot/head_link2/Head_Camera',1280,800), + ('/World/Robot/gripper_l_base_link','/World/Robot/gripper_l_base_link/Left_Camera',848,480), + ('/World/Robot/gripper_r_base_link','/World/Robot/gripper_r_base_link/Right_Camera',848,480)] + +def _Mpose(p): + m=np.eye(4);m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m + +def build_projection_model(stage,link_paths): + cache=UsdGeom.XformCache() + def mw(prim):return np.asarray(cache.GetLocalToWorldTransform(prim),float).T + cameras=[] + for link_path,cam_path,w,h in CAMERAS: + cam=UsdGeom.Camera(stage.GetPrimAtPath(cam_path));cprim=stage.GetPrimAtPath(cam_path) + cameras.append(dict(link_index=link_paths.index(link_path),path=cam_path,width=w,height=h, + relative=np.linalg.inv(mw(stage.GetPrimAtPath(link_path)))@mw(cprim),focal=float(cam.GetFocalLengthAttr().Get()), + horizontal_aperture=float(cam.GetHorizontalApertureAttr().Get()),vertical_aperture=float(cam.GetVerticalApertureAttr().Get()))) + bodies=[] + for i,path in enumerate(link_paths): + if not PASSIVE_RE.search(path):continue + body=stage.GetPrimAtPath(path);bm=mw(body);chunks=[];mesh_paths=[] + for prim in Usd.PrimRange(body): + if not prim.IsA(UsdGeom.Mesh):continue + pts=np.asarray(UsdGeom.Mesh(prim).GetPointsAttr().Get(),float) + if not len(pts):continue + world=(np.c_[pts,np.ones(len(pts))]@mw(prim).T)[:,:3] + chunks.append((np.c_[world,np.ones(len(world))]@np.linalg.inv(bm).T)[:,:3]);mesh_paths.append(str(prim.GetPath())) + local=np.unique(np.round(np.concatenate(chunks),9),axis=0) if chunks else np.empty((0,3)) + bodies.append(dict(index=i,path=path,local_vertices=local,mesh_paths=mesh_paths, + empty_geometry_exclusion=not len(local),descendant_mesh_count=len(mesh_paths))) + driven=[i for i,p in enumerate(link_paths) if not PASSIVE_RE.search(p)] + return dict(cameras=cameras,bodies=bodies,driven_indices=driven) + +def _project(world,cm,c): + loc=(np.c_[world,np.ones(len(world))]@np.linalg.inv(cm).T)[:,:3];depth=-loc[:,2] + uv=np.c_[c['width']/2+(loc[:,0]/depth)*(c['focal']/c['horizontal_aperture'])*c['width'], + c['height']/2-(loc[:,1]/depth)*(c['focal']/c['vertical_aperture'])*c['height']] + valid=(depth>.001)&(uv[:,0]>=0)&(uv[:,0]=0)&(uv[:,1]=2 + if np.any(seen): + span=np.nanmax(masked[:,seen],axis=0)-np.nanmin(masked[:,seen],axis=0) + # The diagonal of each vertex's UV bounding box is a rigorous upper bound + # on every pairwise Euclidean displacement in the window. + mx=float(np.linalg.norm(span,axis=1).max()) + else:mx=0. + overall=max(overall,mx);cams.append(dict(camera=c['path'],max_projected_motion_px_upper_bound=mx, + vertices_visible_at_least_twice=int(seen.sum()))) + records.append(dict(path=b['path'],status='MEASURED',descendant_mesh_count=b['descendant_mesh_count'], + mesh_paths=b['mesh_paths'],unique_mesh_vertex_count=len(local),cameras=cams, + max_projected_motion_px_upper_bound=max(x['max_projected_motion_px_upper_bound'] for x in cams))) + return dict(status='PASSED' if overall= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name in allowed_release_links: + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_arrival_release.v7", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py new file mode 100644 index 0000000000000000000000000000000000000000..1864ef7f799748666a8519fea1c548fb06bb7246 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_arrival_release_one_tmp.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 2's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_02/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # First try an exact rigid compensation for the measured navigation + # residual: keep the chassis at its current live arrival and apply the + # same SE(2) residual to the source-grounded object goal/slot. This + # preserves the accepted base-to-hand/object relation without asking the + # base controller to repeat an already demonstrated 1.37 mm miss. + nominal = candidates[0] + _, _, _, _, _, _, nominal_goal, nominal_base, nominal_transfer = nominal + residual = live_base @ np.linalg.inv(nominal_base) + compensated_goal = residual @ nominal_goal + compensated_base = live_base.copy() + compensated_transfer = compensated_goal @ np.linalg.inv(accepted_goal) + compensated_yaw = float(math.atan2(compensated_goal[1, 0], compensated_goal[0, 0])) + compensated_x, compensated_y = map(float, compensated_goal[:2, 3]) + # The exact residual-shifted footprint is only about 0.2 mm short of the + # 12 mm Coke clearance. Search a bounded +/-6 mm posture correction while + # holding the base exactly at the measured arrival. `solve` below must + # still find and screen the resulting arm adjustment. + fixed_base_candidates = [] + rotation = Rotation.from_euler("z", compensated_yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for dx in np.arange(-0.006, 0.0061, 0.001): + for dy in np.arange(-0.006, 0.0061, 0.001): + x, y = compensated_x + float(dx), compensated_y + float(dy) + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = compensated_goal.copy() + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + transfer = goal @ np.linalg.inv(accepted_goal) + score = -1.0 + float(np.hypot(dx, dy)) + fixed_base_candidates.append((score, x, y, compensated_yaw, footprint, + gaps, goal, compensated_base.copy(), transfer)) + fixed_base_candidates.sort(key=lambda entry: entry[0]) + candidates = fixed_base_candidates + candidates + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:1]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + # Explicit post-release screen. The placed target is stationary at the + # solved goal while the gripper opens and the hand follows retreat. + from collision_scene import Body, broad_overlap, contact + import trimesh + landed = Body(item["target"], trimesh.convex.convex_hull(vertices)) + landed.update(np.asarray(report["goal"], dtype=float)) + release_environment = checker.environment + [landed] + release_low = np.asarray([b.lo for b in release_environment]) + release_high = np.asarray([b.hi for b in release_environment]) + release_failure = None + allowed_release_contacts = [] + allowed_release_links = { + f"/World/Robot/gripper_{item['hand']}_inner_link4", + f"/World/Robot/gripper_{item['hand']}_outer_link4", + } + def release_check(qq, phase, sample_index, fraction): + nonlocal release_failure + fk = robot.fk(names, qq, base) + okay, detail = pose_check(fk, robot.root) + if not okay: + release_failure = {"kind": "self_or_wrist", "detail": detail}; return False + for body in checker.self_check.bodies: + body.update(fk[body.name.replace('/World/Robot', robot.root, 1)]) + indices = np.flatnonzero(np.all(release_high >= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name in allowed_release_links: + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_arrival_release.v7", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_FIXED_ONE_TMP.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..e008dd529557876d2588ffa3e939c8fdc764ff92 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 2's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_02/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case2_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT02_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_last_slot.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_last_slot.py new file mode 100644 index 0000000000000000000000000000000000000000..1790ceaddd70b92ae78ec1b4ea2922356980c9f9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case2_last_slot.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +"""Offline full-body search for case 2's final Skittles tray slot.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_02" + + +def pose_matrix(xyz, wxyz): + result = np.eye(4) + result[:3, 3] = xyz + result[:3, :3] = Rotation.from_quat(np.asarray(wxyz)[[1, 2, 3, 0]]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "PREPARED": + raise RuntimeError(f"case2 is no longer PREPARED: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][3] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][3] + data = np.load(item["controls_file"]) + source = pathlib.Path(item["pick_source"]) + recording = np.load(source / "recording_20hz.npz") + names = recording["robot_dof_names"].tolist() + ids = [names.index(name) for name in data["names"]] + target_index = recording["rigid_body_paths"].tolist().index(item["target"]) + hand_path = "/World/Robot/gripper_" + item["hand"] + "_base_link" + hand_index = recording["robot_link_paths"].tolist().index(hand_path) + held = np.linalg.inv(pose_matrix(recording["robot_link_xyz_m"][-1, hand_index], recording["robot_link_wxyz"][-1, hand_index])) @ pose_matrix(recording["prop_xyz_m"][-1, target_index], recording["prop_wxyz"][-1, target_index]) + q = recording["robot_joint_position_rad_or_m"][-1].copy() + q[ids] = data["place_initial"] + q[ids[-2:]] = data["pick"][-1, -2:] + + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + obstacles = [Polygon(prior["placement_slot"]["footprint_xy"]) for prior in job["items"][:3]] + vertices = data["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + accepted_yaw = float(exact["accepted_yaw_rad"]) + minx, miny, maxx, maxy = floor.bounds + geometry = [] + # Include exact accepted orientation and nearby rotations; every candidate + # rigidly rotates/translates the accepted goal/base pair together. + yaw_offsets = [0, math.pi, math.radians(-15), math.radians(15), math.radians(-30), math.radians(30)] + for yaw_offset in yaw_offsets: + yaw = accepted_yaw + yaw_offset + rotated = vertices @ Rotation.from_euler("z", yaw).as_matrix().T + local_hull = Polygon(rotated[ConvexHull(rotated[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local_hull.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(other) for other in obstacles] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = Rotation.from_euler("z", yaw).as_matrix() + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ goal[:3, :3].T)[:, 2].min() + transform = goal @ np.linalg.inv(accepted_goal) + base = transform @ accepted_base + station_shift = float(np.linalg.norm(base[:2, 3] - np.asarray(item["place_base"])[:2, 3])) + goal_shift = float(np.linalg.norm(goal[:2, 3] - accepted_goal[:2, 3])) + geometry.append((station_shift + 0.15 * goal_shift + 0.02 * abs(yaw_offset), x, y, yaw, footprint, gaps, goal, base, transform)) + geometry.sort(key=lambda candidate: candidate[0]) + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + selfcheck = build(stage) + attempts = [] + accepted = None + for rank, candidate in enumerate(geometry[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transform = candidate + object_pose = robot.fk(names, q, base)[robot.root + hand_path.removeprefix("/World/Robot")] @ held + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, held, {}) + if not checker.check(q, base): + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "station_clearance", "detail": checker.last}) + continue + slot = { + "xy": [float(x), float(y)], + "yaw_rad": float(yaw), + "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), + "tolerance_m": 0.001, + } + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": []} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, q, base, object_pose, vertices, item_job, obstacles, selfcheck, world_check=checker) + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "accepted_pair_transform": transform.tolist(), + "geometry": {"tray_inset_m": 0.008, "future_obstacle_gaps_m": [float(g) for g in gaps], "inside": True}, + "full_body_station_clearance": True, + "plan_frames": int(len(plan)), + "retreat_q": retreat.tolist(), + "placement_report": report, + "elapsed_s": time.monotonic() - began, + } + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "passed", "frames": len(plan)}) + break + except Exception as exc: + attempts.append({"rank": rank, "xy": [x, y], "yaw_rad": yaw, "stage": "solve", "error": str(exc), "elapsed_s": time.monotonic() - began}) + result = { + "schema": "multi_item.case2_last_slot_repair.v2", + "read_only": True, + "case_status_at_probe": status, + "method": "Rigidly transform the accepted-chain goal and robot station together; preserve the source held transform and place_initial; require tray geometry, stationary full-body/world clearance, and placement.solve full path/world checks.", + "evidence": {"job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "pick_recording": str(source / "recording_20hz.npz")}, + "candidate_count_geometry": len(geometry), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "LAST_SLOT_REPAIR_PROPOSAL_V2.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "candidate_count": len(geometry), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "geometry", "full_body_station_clearance", "plan_frames", "elapsed_s")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bcb4fa1120790b9a0d86ded6172ba9f6f571c0 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 3's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_03" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case3 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_03/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case3_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT03_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..61e450de1472cc5023d95c91edb0a273a555e3ac --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item1_live_place.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 3's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_03" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus both future reservations. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future_items = job["items"][2:] + futures = [Polygon(entry["placement_slot"]["footprint_xy"]) for entry in future_items] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(future) for future in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [entry["placement_slot"]["footprint_xy"] for entry in future_items]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(value) for value in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case3_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE3_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..16835e1dabdf7dbd9375269696285954ab62a078 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_carry_boundary.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis and abort-retreat qualification for case 3 item 2.""" +import gzip +import json +import math +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +SRC = R / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000556" +OLD = R / "work/shelf_to_table50_20261001/cases/layout_44" +TARGET = "/World/Assets/ShelfItems/gatorade_t" +CAYMAN = "/World/Assets/ShelfItems/cayman_jacked" +BOARD_TOKEN = "shelf_boards_0/Object_4/Object_0" + +sys.path[:0] = [ + str(W), + str(R / "tools/family3"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "code/family3_repair/real2sim/teleop"), +] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from shapely.geometry import LineString, Polygon # noqa: E402 +from shapely.ops import unary_union # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + + +def matrix(pose): + out = np.eye(4) + out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def yaw(mat): + return float(Rotation.from_matrix(mat[:3, :3]).as_euler("xyz")[2]) + + +def angle_error(a, b): + return float(Rotation.from_matrix(a[:3, :3].T @ b[:3, :3]).magnitude()) + + +def parse_contacts(path, first_tick, last_tick): + result = { + "target_board": [], + "target_gripper": [], + "target_cayman": [], + "target_other": [], + "cayman_gripper": [], + } + try: + with gzip.open(path, "rt") as stream: + while True: + try: + line = stream.readline() + except EOFError: + break # Frozen native process has not written the gzip footer. + if not line: + break + row = json.loads(line) + tick = int(row["tick"]) + if tick < first_tick or tick > last_tick: + continue + actors = row.get("actors", []) + if TARGET in actors: + other = actors[1] if actors[0] == TARGET else actors[0] + if BOARD_TOKEN in other: + key = "target_board" + elif "/World/Robot/gripper_r" in other: + key = "target_gripper" + elif other == CAYMAN: + key = "target_cayman" + else: + key = "target_other" + result[key].append((tick, other, row)) + if CAYMAN in actors: + other = actors[1] if actors[0] == CAYMAN else actors[0] + if "/World/Robot/gripper_r" in other: + result["cayman_gripper"].append((tick, other, row)) + except EOFError: + pass + + summary = {} + for key, rows in result.items(): + ticks = sorted({entry[0] for entry in rows}) + impulse = sum(float(np.linalg.norm(entry[2].get("impulse_sum", [0, 0, 0]))) for entry in rows) + summary[key] = { + "row_count": len(rows), + "contact_tick_count": len(ticks), + "first_tick": ticks[0] if ticks else None, + "last_tick": ticks[-1] if ticks else None, + "minimum_separation_m": min((float(entry[2]["min_separation_m"]) for entry in rows), default=None), + "sum_impulse_norm": impulse, + "other_paths": sorted({entry[1] for entry in rows}), + } + return summary + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case3 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + accepted = np.load(OLD / "rollout/recording_60hz.npz", allow_pickle=True) + controls = np.load(item["controls_file"]) + + phase_ids = np.flatnonzero((checkpoint["item_index"] == 2) & (checkpoint["phase"] == "item_02_shelf_pick")) + first_tick = int(checkpoint["tick"][phase_ids[0]]) + last_tick = int(checkpoint["tick"][phase_ids[-1]]) + samples = phase_ids[2::3] + names = checkpoint["dof_names"].tolist() + q = checkpoint["q"][-1].astype(float) + base = matrix(checkpoint["base_pose"][-1]) + target_index = checkpoint["target_paths"].tolist().index(TARGET) + target_poses = checkpoint["target_pose_all"][phase_ids, target_index] + target = matrix(checkpoint["target_pose_all"][-1, target_index]) + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + hand = matrix(checkpoint["robot_link_pose"][-1, hand_index]) + held = np.linalg.inv(hand) @ target + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1]) + } + + contacts = parse_contacts(C / "rollout/contacts.jsonl.gz", first_tick, last_tick) + motion = np.linalg.norm(target_poses[:, :3] - target_poses[0, :3], axis=1) + rotation = ( + Rotation.from_quat(target_poses[0, 3:]).inv() * Rotation.from_quat(target_poses[:, 3:]) + ).magnitude() + first_motion = int(np.flatnonzero(motion > 0.001)[0]) + first_rotation = int(np.flatnonzero(rotation > 0.05)[0]) + final_stability_translation = float(np.max(np.linalg.norm(target_poses[-120:, :3] - target_poses[-1, :3], axis=1))) + final_stability_rotation = float( + np.max((Rotation.from_quat(target_poses[-1, 3:]).inv() * Rotation.from_quat(target_poses[-120:, 3:])).magnitude()) + ) + + desired_base = np.asarray(item["pick_base"], dtype=float) + actual_start_base = matrix(checkpoint["base_pose"][phase_ids[0]]) + source_names = source["robot_dof_names"].tolist() + reorder = [names.index(name) for name in source_names] + q_error = checkpoint["q"][samples][:, reorder] - source["robot_joint_position_rad_or_m"] + arm_ids = [source_names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + gripper_ids = [i for i, name in enumerate(source_names) if "gripper_r" in name] + + source_target_index = source["rigid_body_paths"].tolist().index(TARGET) + source_hand_index = source["robot_link_paths"].tolist().index(hand_path) + source_hand_target_distance = float( + np.linalg.norm( + source["robot_link_xyz_m"][-1, source_hand_index] + - source["prop_xyz_m"][-1, source_target_index] + ) + ) + accepted_pick = np.flatnonzero(accepted["phase"] == "shelf_pick") + accepted_hand_index = accepted["robot_link_paths"].tolist().index(hand_path) + accepted_hand_target_distance = float( + np.linalg.norm( + accepted["robot_link_pose"][accepted_pick[-1], accepted_hand_index, :3] + - accepted["target_pose"][accepted_pick[-1], :3] + ) + ) + source_contact = json.loads((SRC / "runtime_contact_readback.json").read_text()) + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + carried_checker = Clearance( + stage, robot, names, item["hand"], TARGET, controls["target_local"], held, prop_poses + ) + carried_checker.deadline = time.monotonic() + 900 + carried_start_clear = bool(carried_checker.check(q, base)) + carried_start_detail = carried_checker.last + + empty_checker = EmptyClearance(stage, robot, names, prop_poses, deadline_s=900) + empty_start_clear = bool(empty_checker.check(q, base)) + empty_start_detail = empty_checker.last + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / "03.json" + release = json.loads(release_path.read_text()) + floor = unary_union([Polygon(points).buffer(0) for points in release["floors"]]).buffer(-0.34) + heading = np.array([math.cos(yaw(base)), math.sin(yaw(base))]) + retreat_checks = [] + for distance in (0.05, 0.10, 0.15, 0.25, 0.35): + endpoint = base.copy() + endpoint[:2, 3] -= distance * heading + floor_clear = bool(floor.covers(LineString([base[:2, 3], endpoint[:2, 3]]))) + geometry_clear = bool(empty_checker.segment(q, q, base, endpoint)) if floor_clear else False + retreat_checks.append({ + "distance_m": distance, + "endpoint_base": endpoint.tolist(), + "floor_corridor_clear": floor_clear, + "robot_world_segment_clear": geometry_clear, + "detail": None if geometry_clear else empty_checker.last, + }) + + current_paths = checkpoint["rigid_body_paths"].tolist() + cayman_i = current_paths.index(CAYMAN) + target_prop_i = current_paths.index(TARGET) + cayman_initial = checkpoint["prop_pose"][0, cayman_i] + target_initial = checkpoint["prop_pose"][0, target_prop_i] + cayman_final = checkpoint["prop_pose"][-1, cayman_i] + + result = { + "schema": "multi_item.case3_item2_carry_boundary.v1", + "read_only": True, + "status_at_probe": status, + "evidence": { + "checkpoint": str(checkpoint_path), + "contacts": str(C / "rollout/contacts.jsonl.gz"), + "source": str(SRC), + "accepted_chain": str(OLD), + "controls": item["controls_file"], + "release_floor": str(release_path), + }, + "pickup_phase": { + "tick_range": [first_tick, last_tick], + "ticks": int(len(phase_ids)), + "source_frames": int(len(source["frame_index"])), + "three_ticks_per_source_frame": bool(len(phase_ids) == 3 * len(source["frame_index"])), + "right_arm_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, arm_ids] ** 2))), + "right_arm_tracking_max_rad": float(np.max(np.abs(q_error[:, arm_ids]))), + "right_gripper_tracking_rms_rad": float(np.sqrt(np.mean(q_error[:, gripper_ids] ** 2))), + "right_gripper_tracking_max_rad": float(np.max(np.abs(q_error[:, gripper_ids]))), + }, + "base_alignment": { + "requested_xy": desired_base[:2, 3].tolist(), + "actual_xy": actual_start_base[:2, 3].tolist(), + "xy_error_m": float(np.linalg.norm(actual_start_base[:2, 3] - desired_base[:2, 3])), + "yaw_error_rad": float(math.atan2(math.sin(yaw(actual_start_base) - yaw(desired_base)), math.cos(yaw(actual_start_base) - yaw(desired_base)))), + }, + "measured_target_outcome": { + "initial_pose": target_poses[0].tolist(), + "frozen_pose": target_poses[-1].tolist(), + "translation_m": (target_poses[-1, :3] - target_poses[0, :3]).tolist(), + "rotation_rad": float(rotation[-1]), + "maximum_center_lift_m": float(np.max(target_poses[:, 2] - target_poses[0, 2])), + "first_translation_over_1mm": {"phase_offset_ticks": first_motion, "tick": int(checkpoint["tick"][phase_ids[first_motion]])}, + "first_rotation_over_0_05rad": {"phase_offset_ticks": first_rotation, "tick": int(checkpoint["tick"][phase_ids[first_rotation]])}, + "frozen_hand_target_center_distance_m": float(np.linalg.norm(hand[:3, 3] - target[:3, 3])), + "source_endpoint_hand_target_center_distance_m": source_hand_target_distance, + "accepted_layout44_endpoint_hand_target_center_distance_m": accepted_hand_target_distance, + "last_120_ticks_translation_range_m": final_stability_translation, + "last_120_ticks_rotation_range_rad": final_stability_rotation, + }, + "physical_contact_timeline": contacts, + "source_contact_evidence": { + "pads": source_contact["pads"], + "source_target_contact_count": source_contact["target_contact_count"], + "current_target_gripper_contact_rows": contacts["target_gripper"]["row_count"], + }, + "distractor_causality": { + "cayman_absent_from_episode556_source": bool(CAYMAN not in source["rigid_body_paths"].tolist()), + "initial_target_cayman_center_distance_m": float(np.linalg.norm(target_initial[:3] - cayman_initial[:3])), + "cayman_translation_by_boundary_m": (cayman_final[:3] - cayman_initial[:3]).tolist(), + "sequence": "right gripper contacts Cayman Jack at tick 20017; Cayman Jack contacts Gatorade at tick 20041; Gatorade begins rotating at tick 20047 and translating >1 mm at tick 20057. The source episode contains no Cayman Jack.", + }, + "planner_collision_assessment": { + "carried_surrogate_clear_at_frozen_start": carried_start_clear, + "carried_surrogate_failure": carried_start_detail, + "empty_robot_clear_at_frozen_start": empty_start_clear, + "empty_robot_failure": empty_start_detail, + "classification": "real_physical_support_contact_not_convex_only", + "reason": "Physics records target/shelf-board contact on every pickup tick through tick 21250. The carry surrogate fails at its initial state because it incorrectly treats the ungrasped, shelf-supported bottle as attached to the hand.", + }, + "continuation_qualification": { + "carry_continuation": None, + "qualified": False, + "reason": "There is no live grasp to preserve. The bottle is tipped 1.581 rad, displaced, resting on the shelf, and 0.474 m from the gripper base. A carry continuation would spoof attachment of an ungrasped target.", + "safe_abort_retreat_only": { + "qualified": bool(empty_start_clear and all(entry["floor_corridor_clear"] and entry["robot_world_segment_clear"] for entry in retreat_checks)), + "motion": "Hold all 34 joint targets (including all gripper commands) fixed and execute one physical reverse drive primitive from the measured base. No target attachment, pose write, reset, or arm command.", + "tested_distances": retreat_checks, + "recommended_distance_m": 0.15, + "semantics": "This only withdraws the empty robot safely and must end the attempt as a physical pickup failure; it does not complete item 2.", + }, + }, + "recommendation": { + "action": "Do not apply a carry repair. End this attempt as a real pickup failure and perform a fresh physical retry from the released scene/prefix policy.", + "scene_prevention": "Use CASE3_FRESH_RETRY_INPUTS.json. Full four-source qualification shows that strict all-world clearance requires removing the three nonrequested blockers Cayman Jack, Oreo, and Skittles from a newly prepared scene; Cayman-only removal is insufficient.", + "pickup_gate": "Require bilateral target/gripper contact and bounded target-to-hand transform at the endpoint. Center lift alone is insufficient because tipping on the shelf produced a false >25 mm lift.", + "safe_state_option": "If the native process must be parked before termination, the qualified 0.15 m constant-posture physical reverse is safe-abort motion only.", + }, + "native_motion_executed": False, + "scene_or_job_modified": False, + } + out = C / "CASE3_ITEM02_CARRY_BOUNDARY_DIAGNOSTIC.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({ + "out": str(out), + "carry_continuation_qualified": result["continuation_qualification"]["qualified"], + "safe_abort_retreat_qualified": result["continuation_qualification"]["safe_abort_retreat_only"]["qualified"], + "contact_timeline": contacts, + "planner_collision": result["planner_collision_assessment"], + "recommendation": result["recommendation"], + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..d39ed23425852c345f5252db69a5617c1ef81b3f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case3_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_03' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_03/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case3_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT03_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..239a690d8ac96b94da435886957077fde9fcffa0 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 4's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_04" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case4 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_04/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case4_item1_fresh_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT04_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..a768edbf209de66516ed5f027c5e3f375c246b6b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case4_item1_live_place.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +"""Read-only full-path Honey Maid place repair probe from case 4's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_04" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case4 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][2:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:300]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][2:]]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank":rank,"stage":"actual_compact_to_place_bridge","detail":checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_gaps_m": [float(x) for x in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case4_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE4_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py new file mode 100644 index 0000000000000000000000000000000000000000..2f4b5185330b6009131987ac920e152bb4a8560a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_actual_arrival.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Qualify a slot rigidly compensated to case 5's measured station arrival.""" +import hashlib +import json +import math +import os +from pathlib import Path +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), + str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def planar(base): + result = np.eye(4) + yaw = Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2] + result[:3, :3] = Rotation.from_euler("z", yaw).as_matrix() + result[:3, 3] = base[:3, 3] + return result + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 1 and status["control_ticks"] == 16072 +job = json.loads((C / "job.json").read_text()) +item = job["items"][1] +source_path = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" +source = json.loads(source_path.read_text())["accepted"] +checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = checkpoint["q"][-1].astype(float) +names = checkpoint["dof_names"].tolist() +actual_base = matrix(checkpoint["base_pose"][-1]) +hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") +target_index = checkpoint["target_paths"].tolist().index(item["target"]) +target = matrix(checkpoint["target_pose_all"][-1, target_index]) +held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ target +controls = np.load(item["controls_file"]) +ids = [names.index(name) for name in controls["names"].tolist()] +exact_q = q.copy() +exact_q[ids] = np.asarray(source["place_initial20"]) +grippers = [index for index, name in enumerate(names) if "gripper_" in name] +exact_q[grippers] = q[grippers] + +desired_base = np.asarray(source["place_base"], dtype=float) +transfer = planar(actual_base) @ np.linalg.inv(planar(desired_base)) +center = transfer @ np.r_[source["slot"]["xy"], 0.0, 1.0] +footprint = [] +for point in source["slot"]["footprint_xy"]: + footprint.append((transfer @ np.r_[point, 0.0, 1.0])[:2].tolist()) +dyaw = Rotation.from_matrix(transfer[:3, :3]).as_euler("xyz")[2] +slot = {"xy": center[:2].tolist(), "yaw_rad": float(source["slot"]["yaw_rad"] + dyaw), + "footprint_xy": footprint, "tolerance_m": 0.001} + +stage = Usd.Stage.Open(job["scene"]) +robot = Robot(ROBOT) +robot.bodies = [] +pose_check = build(stage) +props = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} +checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) +checker.deadline = time.monotonic() + 1800 +assert checker.segment(q, exact_q, actual_base, actual_base), checker.last + +item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) +item0_local = np.load(job["items"][0]["controls_file"])["target_local"] +item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] +obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] +floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) +current_poly = Polygon(footprint) +future = [Polygon(entry["placement_slot"]["footprint_xy"]) for entry in job["items"][2:]] +gaps = [current_poly.distance(obstacles[0])] + [current_poly.distance(other) for other in future] +assert floor.covers(current_poly) and min(gaps) > 0.012, (gaps, floor.covers(current_poly)) + +object_pose = robot.fk(names, exact_q, actual_base)[robot.root + "/gripper_r_base_link"] @ held +item_job = {**job, **item, "placement_slot": slot, "place_base": actual_base.tolist(), + "destination_xyz": [float(center[0]), float(center[1]), float(job["tray_floor_z"])], + "reserved_tray_footprints": [entry["placement_slot"]["footprint_xy"] for entry in job["items"][2:]], + "require_release_sweep": True} +release = ReleaseSweep(stage, robot, names, item["hand"], item["target"], controls["target_local"], props, + item["place_source"], pose_check=pose_check, deadline_s=1800) +planned, retreat, report = solve(robot, names, exact_q, actual_base, object_pose, controls["target_local"], item_job, + obstacles, pose_check, world_check=checker, release_sweep=release) +result = {"schema": "multi_item.case5_item1_actual_arrival_place.v1", "read_only": True, + "status_tick": status["control_ticks"], "source_proposal": str(source_path), + "source_proposal_sha256": hashlib.sha256(source_path.read_bytes()).hexdigest(), + "measured_base": actual_base.tolist(), "desired_base": desired_base.tolist(), + "actual_from_desired_planar_transform": transfer.tolist(), "slot": slot, + "destination_xyz": item_job["destination_xyz"], "geometry": {"inside": True, "gaps_m": gaps}, + "place_initial20": source["place_initial20"], "bridge_clear": True, + "placement_frames": len(planned), "retreat_q": retreat.tolist(), "placement_report": report, + "full_world_checks": True, "native_validation_required": True} +output = W / "diagnostics/LAYOUT05_ITEM01_ACTUAL_ARRIVAL_PLACE_PROPOSAL.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest(), + "slot": slot, "gaps_m": gaps, "frames": len(planned), "release": report["release_sweep"]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py new file mode 100644 index 0000000000000000000000000000000000000000..3e4ace8b26fced60ce8c04056f4d054f5e6e8ac7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_command_prebias.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""Read-only carried-route and endpoint qualification for case 5 station prebias.""" +import hashlib +import json +import math +import os +from pathlib import Path +import sys +import time + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance, plan as plan_carry + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def yaw(base): + return float(Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2]) + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 1 and status["control_ticks"] == 14645 +job = json.loads((C / "job.json").read_text()) +item = job["items"][1] +proposal_path = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" +proposal = json.loads(proposal_path.read_text())["accepted"] +checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = checkpoint["q"][-1].astype(float) +names = checkpoint["dof_names"].tolist() +base = matrix(checkpoint["base_pose"][-1]) +hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") +target_index = checkpoint["target_paths"].tolist().index(item["target"]) +target = matrix(checkpoint["target_pose_all"][-1, target_index]) +held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ target +controls = np.load(item["controls_file"]) +ids = [names.index(name) for name in controls["names"].tolist()] +exact_q = q.copy() +exact_q[ids] = np.asarray(proposal["place_initial20"]) +grippers = [index for index, name in enumerate(names) if "gripper_" in name] +exact_q[grippers] = q[grippers] + +old_desired = np.asarray(item["place_base"], dtype=float) +desired = np.asarray(proposal["place_base"], dtype=float) +current = np.r_[base[:2, 3], yaw(base)] +old_endpoint = np.r_[old_desired[:2, 3], yaw(old_desired)] +residual = current - old_endpoint +residual[2] = math.atan2(math.sin(residual[2]), math.cos(residual[2])) +command = desired.copy() +command[:2, 3] -= residual[:2] +command[:3, :3] = Rotation.from_euler("z", yaw(desired) - residual[2]).as_matrix() + +props = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]) +robot = Robot(ROBOT) +robot.bodies = [] +checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) +checker.deadline = time.monotonic() + 900 + +source_layout = int(job.get("source_layout_id", job["layout_id"])) +release = json.loads((Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout:02d}.json").read_text()) +repair_job = {**job, **item, "place_base": command.tolist(), "placement_slot": proposal["slot"], + "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + "reserved_tray_footprints": [future["placement_slot"]["footprint_xy"] for future in job["items"][2:]]} +repair_job.pop("carry_route_seed", None) +compact, primitives, carry_report = plan_carry(stage, robot, names, q, base, item["hand"], item["target"], controls["target_local"], target, props, repair_job, release) +checks = {} +for label, endpoint in (("command", command), ("desired", desired)): + checks[label] = { + "compact_clear": bool(checker.check(compact, endpoint)), + "compact_detail": checker.last, + "exact_clear": bool(checker.check(exact_q, endpoint)), + "exact_detail": checker.last, + "compact_to_exact_clear": bool(checker.segment(compact, exact_q, endpoint, endpoint)), + "segment_detail": checker.last, + } + assert checks[label]["compact_clear"] and checks[label]["exact_clear"] and checks[label]["compact_to_exact_clear"] + +result = { + "schema": "multi_item.case5_item1_station_command_prebias.v1", + "read_only": True, + "status_tick": status["control_ticks"], + "source_proposal": str(proposal_path), + "source_proposal_sha256": hashlib.sha256(proposal_path.read_bytes()).hexdigest(), + "observed_prior_arrival_residual": {"world_xy_m": residual[:2].tolist(), "yaw_rad": float(residual[2])}, + "desired_qualified_base4x4": desired.tolist(), + "command_base4x4_inverse_residual": command.tolist(), + "prediction": "If the measured prior endpoint residual repeats, physical arrival equals desired_qualified_base4x4.", + "checks": checks, + "carry": {"primitive_count": len(primitives), "compact_q": compact.tolist(), "primitives": primitives, "report": carry_report}, + "required_live_arrival_gate": {"translation_m": 0.0005, "yaw_rad": 0.001, "relative_to": "desired_qualified_base4x4", + "then_recompute_live_bridge_and_place": True}, + "no_motion": True, + "native_validation_required": True, +} +output = W / "diagnostics/LAYOUT05_ITEM01_STATION_COMMAND_PREBIAS.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest(), + "residual": result["observed_prior_arrival_residual"], "checks": checks, + "carry_primitives": len(primitives)})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py new file mode 100644 index 0000000000000000000000000000000000000000..e828420f634346f50903f91fa9df70ac6113c495 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_direct_old_slot.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [2.9377098028668382] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in [-1.378553647218034]: + for y in [-0.16699245336876373]: + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_DIRECT_OLD_SLOT_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..9a3f4eac7b3b5041ac0addd952bc95a4bed5bc35 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_fresh_live_place.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..18e1bac3878737d9e78405875017b16e8352796a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item1_targeted_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: (np.linalg.norm(np.asarray(entry[1:3])-np.asarray([-1.378553647218034,-0.16699245336876373])), entry[0])) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM01_TARGETED_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py new file mode 100644 index 0000000000000000000000000000000000000000..bf46cd53cf57bf8b307f451dcd23fdf56df4193b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_arrival_release.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 5 item 2 frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_05" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case5 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed items 0 and 1, plus future reservations. + obstacles = [] + for completed_index in range(2): + completed_pose = matrix(checkpoint["target_pose_all"][-1, completed_index]) + completed_local = np.load(job["items"][completed_index]["controls_file"])["target_local"] + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles.append(Polygon(completed_world[ConvexHull(completed_world).vertices])) + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(3, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # Recheck the prepared source-grounded slot that passed the case-level + # offline solve, now against the actual two supported objects and live + # held transform. + prepared_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_05/SLOT_REACHABILITY_PROPOSAL.json" + prepared = json.loads(prepared_path.read_text())["items"][2]["report"] + ps = prepared["slot"] + px, py, pyaw = float(ps["xy"][0]), float(ps["xy"][1]), float(ps["yaw_rad"]) + prot = Rotation.from_euler("z", pyaw).as_matrix() + ppts = vertices @ prot.T + plocal = Polygon(ppts[ConvexHull(ppts[:, :2]).vertices, :2]) + pfoot = Polygon(np.asarray(plocal.exterior.coords)[:-1] + [px, py]) + pgaps = [pfoot.distance(o) for o in obstacles] + [pfoot.distance(f) for f in futures] + pgoal = np.asarray(prepared["goal"], dtype=float) + pbase = np.asarray(item["place_base"], dtype=float) + pbase[2, 3] = live_base[2, 3] + ptransfer = pgoal @ np.linalg.inv(accepted_goal) + if not floor.covers(pfoot) or (pgaps and min(pgaps) <= 0.012): + raise RuntimeError(f"prepared item2 footprint no longer valid: {pgaps}") + candidates = [(-1.0, px, py, pyaw, pfoot, pgaps, pgoal, pbase, ptransfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(3, len(job["items"]))]} + began = time.monotonic() + try: + release_checker = ReleaseSweep(stage, robot, names, item["hand"], item["target"], + vertices, prop_poses, item["place_source"], + pose_check=pose_check, deadline_s=900) + item_job["require_release_sweep"] = True + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, + item_job, obstacles, pose_check, + world_check=checker, release_sweep=release_checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(g) for g in gaps[:2]], "future_item_gaps_m": [float(g) for g in gaps[2:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_sweep": report["release_sweep"], + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case5_item2_release_sweep.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT05_ITEM02_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..8a1f047679fcc11156837f8c3e0465501a9cd48a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case5_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_05' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_05/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case5_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT05_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..b73c4550633896415531b1eebf95630d2947a5f7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item1_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 6's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_06" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case6 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case6_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT06_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe70d23ad4a33d03e02a1d522f4a798fefeb575 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case6_item2_arrival_release.py @@ -0,0 +1,283 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 6 item 2 frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_06" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"case6 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed items 0 and 1, plus future reservations. + obstacles = [] + for completed_index in range(2): + completed_pose = matrix(checkpoint["target_pose_all"][-1, completed_index]) + completed_local = np.load(job["items"][completed_index]["controls_file"])["target_local"] + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles.append(Polygon(completed_world[ConvexHull(completed_world).vertices])) + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(3, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # The prior physical attempt reached this same item boundary and its + # source-grounded candidate passed the full place/world probe. Recheck + # that exact candidate first against the new live held transform and the + # two newly measured supported objects instead of spending minutes on + # weaker grid candidates. + old_safe_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_06/CASE6_ITEM02_SAFE_PLACE_PROPOSAL.json" + old_safe = json.loads(old_safe_path.read_text())["accepted"] + old_slot = old_safe["slot"] + old_x, old_y, old_yaw = float(old_slot["xy"][0]), float(old_slot["xy"][1]), float(old_slot["yaw_rad"]) + old_rotation = Rotation.from_euler("z", old_yaw).as_matrix() + old_points = vertices @ old_rotation.T + old_local = Polygon(old_points[ConvexHull(old_points[:, :2]).vertices, :2]) + old_footprint = Polygon(np.asarray(old_local.exterior.coords)[:-1] + [old_x, old_y]) + old_gaps = [old_footprint.distance(o) for o in obstacles] + [old_footprint.distance(f) for f in futures] + old_goal = np.asarray(old_safe["placement_report"]["goal"], dtype=float) + old_base = np.asarray(old_safe["place_base"], dtype=float) + old_base[2, 3] = live_base[2, 3] + old_transfer = old_goal @ np.linalg.inv(accepted_goal) + if floor.covers(old_footprint) and (not old_gaps or min(old_gaps) > 0.012): + candidates = [(-1.0, old_x, old_y, old_yaw, old_footprint, old_gaps, + old_goal, old_base, old_transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(3, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + # Explicit post-release screen. The placed target is stationary at the + # solved goal while the gripper opens and the hand follows retreat. + from collision_scene import Body, broad_overlap, contact + import trimesh + landed = Body(item["target"], trimesh.convex.convex_hull(vertices)) + landed.update(np.asarray(report["goal"], dtype=float)) + release_environment = checker.environment + [landed] + release_low = np.asarray([b.lo for b in release_environment]) + release_high = np.asarray([b.hi for b in release_environment]) + release_failure = None + allowed_release_contacts = [] + assigned_gripper_prefix = f"/World/Robot/gripper_{item['hand']}_" + def release_check(qq, phase, sample_index, fraction): + nonlocal release_failure + fk = robot.fk(names, qq, base) + okay, detail = pose_check(fk, robot.root) + if not okay: + release_failure = {"kind": "self_or_wrist", "detail": detail}; return False + for body in checker.self_check.bodies: + body.update(fk[body.name.replace('/World/Robot', robot.root, 1)]) + indices = np.flatnonzero(np.all(release_high >= body.lo, axis=1) & np.all(release_low <= body.hi, axis=1)) + for ii in indices: + other = release_environment[ii] + if contact(body, other, containment=True) is not None: + # The release necessarily starts with the two assigned + # distal fingers touching the stationary placed target. + # Record that contact during opening only; every other + # robot/world pair remains forbidden. Separation and + # the complete retreat are required below. + if other is landed and body.name.startswith(assigned_gripper_prefix): + allowed_release_contacts.append({"phase": phase, "sample": sample_index, "fraction": float(fraction), "link": body.name}) + continue + release_failure = {"kind": "released_world", "robot": body.name, "obstacle": other.name}; return False + return True + landing_q = np.asarray(plan[-1], dtype=float) + opened_q = landing_q.copy() + grip_control = control_names.index(f"idx{4 if item['hand']=='l' else 8}1_gripper_{item['hand']}_outer_joint1") + opened_q[ids[grip_control]] = math.pi / 4 + retreat_open = np.asarray(retreat, dtype=float).copy(); retreat_open[ids[grip_control]] = math.pi / 4 + release_ok = True + release_samples = 0 + phases = (("opening", landing_q, opened_q, 121), ("retreat", opened_q, retreat_open, max(61, int(np.max(np.abs(retreat_open-opened_q))/.01)+1))) + contact_samples = {"opening": set(), "retreat": set()} + for phase, qa, qb, count in phases: + for sample_index, u in enumerate(np.linspace(0., 1., count)): + release_samples += 1 + if release_samples % 10 == 0: print(f"RELEASE_PROGRESS {release_samples} {phase} {sample_index}/{count}", flush=True) + before = len(allowed_release_contacts) + if not release_check(qa*(1-u)+qb*u, phase, sample_index, u): release_ok = False; break + if len(allowed_release_contacts) > before: + contact_samples[phase].add(sample_index) + if not release_ok: break + if release_ok: + # Assigned distal-finger contact is mechanically necessary while + # opening and can persist briefly as retreat begins. Require a + # clean final quarter of retreat and forbid re-contact after the + # first three consecutive clean retreat samples. + retreat_count = phases[1][3] + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late = sorted(i for i in contact_samples["retreat"] if i >= late_start) + if late: + release_ok = False + release_failure = {"kind": "assigned_finger_target_contact_persisted", "samples": late} + else: + clean_start = None + for i in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (i, i + 1, i + 2)): + clean_start = i + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if clean_start is None or recontacts: + release_ok = False + release_failure = {"kind": "assigned_finger_target_recontact", "clean_start": clean_start, "samples": recontacts} + if not release_ok: + attempts.append({"rank": rank, "stage": "open_gripper_release_or_retreat_world", "xy": [x,y], "yaw": yaw, "detail": release_failure}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(g) for g in gaps[:2]], "future_item_gaps_m": [float(g) for g in gaps[2:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "release_open_gripper_world_clearance": True, + "release_retreat_world_clearance": True, + "release_sweep_samples": release_samples, + "release_assigned_contact_samples": {k: sorted(v) for k, v in contact_samples.items()}, + "release_separated_final_retreat_quarter": True, + "release_no_recontact_after_three_clean_samples": True, + "released_target_pose": np.asarray(report["goal"], dtype=float).tolist(), + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case6_item2_arrival_release.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT06_ITEM02_ARRIVAL_RELEASE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..88885c1be4c38a314deff31ee490d1678c14cf7a --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item1_fresh_live_place.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 7's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_07" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case7 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_07/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + futures = [Polygon(job["items"][k]["placement_slot"]["footprint_xy"]) for k in range(2, len(job["items"]))] + reserved = futures + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0])] + [footprint.distance(f) for f in futures] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][k]["placement_slot"]["footprint_xy"] for k in range(2, len(job["items"]))]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item_gaps_m": [float(g) for g in gaps[1:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case7_item1_live_place.v2", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "diagnostics/LAYOUT07_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py new file mode 100644 index 0000000000000000000000000000000000000000..18de7747a0abb899896d81dbde3816ec003f76ee --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Find a screened carried escape route for case 7's frozen blocked turn.""" +import hashlib, json, math, os, sys, time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def base(x,y,yaw,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',yaw).as_matrix();m[:3,3]=[x,y,z];return m +status=json.load(open(C/'STATUS.json'));assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 and status['control_ticks']==33066 +job=json.load(open(C/'job.json'));item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();b0=mat(cp['base_pose'][-1]);z=b0[2,3] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link");ti=cp['target_paths'].tolist().index(item['target']);target=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@target;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])} +stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];ctrl=np.load(item['controls_file']);check=Clearance(stage,robot,names,item['hand'],item['target'],ctrl['target_local'],held,props);check.deadline=time.monotonic()+900 +s=np.array(status['planning_error'].split('"measured_base": [')[1].split(']')[0].split(','),float);goal=np.array([-1.0663820670686106,1.0637987903151804,-1.5746476816413626]);assert check.check(q,b0),check.last +direct=base(goal[0],goal[1],goal[2],z);direct_clear=check.segment(q,q,b0,direct);direct_detail=check.last +candidates=[] +for signed_distance in [-.03,.03,-.05,.05,-.08,.08,-.10,.10,-.15,.15,-.20,.20]: + heading=np.array([math.cos(s[2]),math.sin(s[2])]);xy=s[:2]+signed_distance*heading;theta=math.atan2(goal[1]-xy[1],goal[0]-xy[0]);poses=[b0,base(xy[0],xy[1],s[2],z),base(xy[0],xy[1],theta,z),base(goal[0],goal[1],theta,z),direct] + ok=True;details=[] + for a,b in zip(poses,poses[1:]): + good=check.segment(q,q,a,b);details.append(check.last) + if not good:ok=False;break + candidates.append({'signed_escape_m':signed_distance,'passed':ok,'connector_yaw':theta,'details':details, + 'primitives':[{'kind':'drive','goal':[float(xy[0]),float(xy[1]),float(s[2])],'direction':1 if signed_distance>0 else -1}, + {'kind':'turn','goal':[float(xy[0]),float(xy[1]),float(theta)]}, + {'kind':'drive','goal':[float(goal[0]),float(goal[1]),float(theta)],'direction':1}, + {'kind':'turn','goal':goal.tolist()}]}) + if ok:break +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v1','read_only':True,'status_tick':33066,'failure_sha256':'eda9b457e975bd02f417c18a0024af7559fdc8ac070eb222d9b55686166c5961','direct_turn_geometry_clear':direct_clear,'direct_detail':direct_detail,'held_transform':held.tolist(),'candidates':candidates,'accepted':next((x for x in candidates if x['passed']),None),'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'direct':direct_clear,'accepted':out['accepted']},indent=2)) +if out['accepted'] is None:raise SystemExit(2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..40e55e47d3fd203def74ca3c7587f624954c7e93 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v2.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python3 +"""Screen a within-gate endpoint offset away from the live table contact.""" +import hashlib,json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(x,y,yaw,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',yaw).as_matrix();m[:3,3]=[x,y,z];return m +status=json.load(open(C/'STATUS.json'));assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 and status['control_ticks']==34142 +job=json.load(open(C/'job.json'));item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();start=np.array([-1.0671910047531128,1.0646690130233765,-0.812772274017334]);goal=np.array([-1.0663820670686106,1.0637987903151804,-1.5746476816413626]);z=cp['base_pose'][-1,2] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link");ti=cp['target_paths'].tolist().index(item['target']);target=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@target;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];ctrl=np.load(item['controls_file']);check=Clearance(stage,robot,names,item['hand'],item['target'],ctrl['target_local'],held,props);check.deadline=time.monotonic()+900 +heading=np.array([math.cos(start[2]),math.sin(start[2])]);escape=start[:2]-.03*heading;candidate=goal[:2]+np.array([.00067,.00067]);connector=math.atan2(candidate[1]-escape[1],candidate[0]-escape[0]);states=[start,[*escape,start[2]],[*escape,connector],[*candidate,connector],[*candidate,goal[2]]];primitives=[{'kind':'drive','goal':states[1],'direction':-1},{'kind':'turn','goal':states[2]},{'kind':'drive','goal':states[3],'direction':1},{'kind':'turn','goal':states[4]}];details=[] +for a,b in zip(states,states[1:]): + ok=check.segment(q,q,bm(*a,z),bm(*b,z));details.append(check.last);assert ok,check.last +error=float(np.linalg.norm(candidate-goal[:2]));assert error<.001 +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v2','read_only':True,'status_tick':34142,'failure_sha256':'0abde6995ad418d2f1703eb6061ec1a69adbd63a3f2c0fe19f92f44363a91be8','physical_blocker':{'pair':['/World/Robot/base_link','/World/Assets/CoffeeTable/Mesh'],'latest_min_separation_m':1.6e-6,'impulse_xy_approx':[14.29,14.32],'inferred_away_direction_world_xy':[1,1]},'original_goal':goal.tolist(),'candidate_endpoint':[*candidate,goal[2]],'candidate_xy_error_m':error,'parent_gate_m':.001,'primitives':primitives,'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V2_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'xy_error':error,'primitives':primitives},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..ec30abb613aaa6fd4a72c5e8847b8d75bb2d16b6 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v3.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +"""Screen a carried route that finishes rotation before approaching the table-edge endpoint.""" +import hashlib, json, math, os, sys, time +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_07" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance + + +def mat(p): + m = np.eye(4); m[:3, 3] = p[:3]; m[:3, :3] = Rotation.from_quat(p[3:]).as_matrix(); return m + + +def bm(state, z): + m = np.eye(4); m[:3, :3] = Rotation.from_euler("z", state[2]).as_matrix(); m[:3, 3] = [state[0], state[1], z]; return m + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 2 and status["control_ticks"] == 35270 +job = json.loads((C / "job.json").read_text()); item = job["items"][2] +cp = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = cp["q"][-1].astype(float); names = cp["dof_names"].tolist(); z = float(cp["base_pose"][-1, 2]) +start = np.array([-1.0666694641113281, 1.0653468370437622, -0.8118601441383362]) +goal = np.array([-1.0657120670686107, 1.0644687903151804, -1.5746476816413626]) +hi = cp["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") +ti = cp["target_paths"].tolist().index(item["target"]) +target = mat(cp["target_pose_all"][-1, ti]); held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ target +props = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] +ctrl = np.load(item["controls_file"]) +check = Clearance(stage, robot, names, item["hand"], item["target"], ctrl["target_local"], held, props) +check.deadline = time.monotonic() + 900 + +# First retreat along the current heading. Approach the endpoint only after the +# chassis has reached the final yaw at a point 35 mm away along that yaw. +heading = np.array([math.cos(start[2]), math.sin(start[2])]) +escape = start[:2] - 0.035 * heading +final_heading = np.array([math.cos(goal[2]), math.sin(goal[2])]) +staging = goal[:2] - 0.035 * final_heading +connector = math.atan2(staging[1] - escape[1], staging[0] - escape[0]) +states = [start, [*escape, start[2]], [*escape, connector], [*staging, connector], [*staging, goal[2]], goal] +primitives = [ + {"kind": "drive", "goal": states[1], "direction": -1}, + {"kind": "turn", "goal": states[2]}, + {"kind": "drive", "goal": states[3], "direction": 1}, + {"kind": "turn", "goal": states[4]}, + {"kind": "drive", "goal": states[5], "direction": 1}, +] +details = [] +for a, b in zip(states, states[1:]): + ok = check.segment(q, q, bm(a, z), bm(b, z)); details.append(check.last) + assert ok, check.last + +failure_raw = json.loads((C / "rollout/NAVIGATION_MOTION_BLOCK.item_02.tick_00035270.37a837efc4e9.json").read_text()) +out = { + "schema": "multi_item.case7_item2_carry_recovery_probe.v3", + "read_only": True, + "status_tick": 35270, + "failure_sha256": failure_raw["failure_sha256"], + "physical_strategy": "rotate_to_final_yaw_35mm_before_table_edge_then_straight_approach", + "original_goal": goal.tolist(), + "primitives": primitives, + "segment_details": details, + "all_full_body_held_target_live_props_screened": True, + "native_validation_required": True, +} +p = W / "diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V3_PROPOSAL.json" +p.write_text(json.dumps(out, indent=2, default=lambda value: value.tolist() if hasattr(value, "tolist") else str(value)) + "\n") +print(json.dumps({"out": str(p), "sha256": hashlib.sha256(p.read_bytes()).hexdigest(), "primitives": primitives}, indent=2, default=lambda value: value.tolist() if hasattr(value, "tolist") else str(value))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py new file mode 100644 index 0000000000000000000000000000000000000000..9d21b84a7870f773504da0f78bdac110be5e669d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v4.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python3 +"""Screen a robust overshoot-and-return correction for carried turn drift.""" +import hashlib, json, math, os, sys, time +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent; R = W.parents[1]; C = W / "cases/layout_07" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance + + +def mat(p): + m = np.eye(4); m[:3, 3] = p[:3]; m[:3, :3] = Rotation.from_quat(p[3:]).as_matrix(); return m + + +def bm(state, z): + m = np.eye(4); m[:3, :3] = Rotation.from_euler("z", state[2]).as_matrix(); m[:3, 3] = [state[0], state[1], z]; return m + + +status = json.loads((C / "STATUS.json").read_text()) +assert status["status"] == "WAITING_FOR_PLANNING_REPAIR" and status["item_index"] == 2 and status["control_ticks"] == 36805 +job = json.loads((C / "job.json").read_text()); item = job["items"][2] +cp = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) +q = cp["q"][-1].astype(float); names = cp["dof_names"].tolist(); z = float(cp["base_pose"][-1, 2]) +start = np.array([-1.0898466110229492, 1.089781641960144, 0.3164128363132477]) +goal = np.array([-1.0907547127921056, 1.090741734089923, 0.33365312012539144]) +hi = cp["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") +ti = cp["target_paths"].tolist().index(item["target"]) +target = mat(cp["target_pose_all"][-1, ti]); held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ target +props = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} +stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] +ctrl = np.load(item["controls_file"]) +check = Clearance(stage, robot, names, item["hand"], item["target"], ctrl["target_local"], held, props) +check.deadline = time.monotonic() + 900 + +delta = goal[:2] - start[:2]; direction = delta / np.linalg.norm(delta); approach_yaw = math.atan2(direction[1], direction[0]) +past = goal[:2] + 0.030 * direction +states = [start, [*start[:2], approach_yaw], [*past, approach_yaw], [*past, approach_yaw + math.pi], [*goal[:2], approach_yaw + math.pi], goal] +motion = [ + {"kind": "turn", "goal": states[1]}, + {"kind": "drive", "goal": states[2], "direction": 1}, + {"kind": "turn", "goal": states[3]}, + {"kind": "drive", "goal": states[4], "direction": 1}, + {"kind": "turn", "goal": states[5]}, +] +details = [] +for a, b in zip(states, states[1:]): + ok = check.segment(q, q, bm(a, z), bm(b, z)); details.append(check.last); assert ok, check.last + +block = json.loads((C / "rollout/NAVIGATION_MOTION_BLOCK.item_02.tick_00036805.f410ac1f6e25.json").read_text()) +out = { + "schema": "multi_item.case7_item2_carry_recovery_probe.v4", "read_only": True, + "status_tick": 36805, "failure_sha256": block["failure_sha256"], + "strategy": "30mm_overshoot_and_return_with_high_static_friction_floor", + "start": start.tolist(), "goal": goal.tolist(), "motion": motion, + "controller": {"turn_min_rate_rad_s": 0.06, "turn_cap_rad_s": 0.18, "drive_min_speed_m_s": 0.015, "drive_cap_m_s": 0.08, "xy_gate_m": 0.0008, "yaw_gate_rad": 0.0008}, + "segment_details": details, "all_full_body_held_target_live_props_screened": True, + "native_validation_required": True, +} +p = W / "diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V4_PROPOSAL.json" +p.write_text(json.dumps(out, indent=2, default=lambda x: x.tolist() if hasattr(x, "tolist") else str(x)) + "\n") +print(json.dumps({"out": str(p), "sha256": hashlib.sha256(p.read_bytes()).hexdigest(), "motion": motion}, indent=2, default=lambda x: x.tolist() if hasattr(x, "tolist") else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py new file mode 100644 index 0000000000000000000000000000000000000000..73447b7f40e7c20dc185548a124d687771771507 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v5.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==39547 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112915277481079,-0.9873537215446402]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v5','read_only':True,'status_tick':39547,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'249760697aee4340b1ab2b2218c74e1c2c7ac1d77a777c2c946e9badc11c75f9','strategy':'complete_v4_from_measured_partial_state_at_full_existing_turn_cap','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_command_rate_rad_s':0.25,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V5_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py new file mode 100644 index 0000000000000000000000000000000000000000..23389a5d46709f8982fbb1a42999273c09914b27 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v6.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==41347 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.9051387196416416]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v6','read_only':True,'status_tick':41347,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'08a6363b8f00797c3a1aedeea980a87bda4e635be5a9641dc8a1e9c5769015f1','strategy':'complete_from_measured_partial_state_with_acceleration_aware_braking','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V6_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py new file mode 100644 index 0000000000000000000000000000000000000000..1c589bf02112800d83a3e0a0295227f7e6ef1db8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v7.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43147 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8151900665756764]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v7','read_only':True,'status_tick':43147,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'f77b0ccbf8343be1d3142f4138e5a2b50e64aa7ab7065909bbc152f4e4e610bf','strategy':'coarse_stopping_distance_then_adaptive_recorded_control_tick_pulses','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.19,'fine_pulse_max_rate_rad_s':0.25,'fine_pulse_hold_ticks':6,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V7_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py new file mode 100644 index 0000000000000000000000000000000000000000..02654847343a86770685d11dfcfcabe52cb81c0e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v8.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43568 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8153694789297252]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v8','read_only':True,'status_tick':43568,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'e9f139540c9d02df0bf60a0fa0eb793c9b54ba684f1664f5c0ac49a0cdc7cd5d','strategy':'measured_single_tick_pulse_calibration_then_remaining_route','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.179,'fine_pulse_max_rate_rad_s':0.19,'fine_pulse_hold_ticks':2,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V8_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py new file mode 100644 index 0000000000000000000000000000000000000000..e6d6eb34cb9ee099e6b79508a4c9bc05826b2c6d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_carry_recovery_v9.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""Screen completion of the partially executed v4 overshoot repair.""" +import hashlib,json,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==43752 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.1117451190948486,1.112914800643921,-0.8150531268867094]);goal=np.array([-1.0907547127921056,1.090741734089923,0.33365312012539144]);back_yaw=5.469965114540382 +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900 +states=[start,[*start[:2],back_yaw],[*goal[:2],back_yaw],goal];motion=[{'kind':'turn','goal':states[1]},{'kind':'drive','goal':states[2],'direction':1},{'kind':'turn','goal':states[3]}];details=[] +for a,b in zip(states,states[1:]):ok=check.segment(q,q,bm(a,z),bm(b,z));details.append(check.last);assert ok,check.last +pulse_envelope=[] +for center in (states[1],states[3]): + for delta in (-0.01,0.01): + edge=[center[0],center[1],center[2]+delta];ok=check.segment(q,q,bm(center,z),bm(edge,z));pulse_envelope.append(check.last);assert ok,check.last +out={'schema':'multi_item.case7_item2_carry_recovery_probe.v9','read_only':True,'status_tick':43752,'parent_failure_sha256':'f410ac1f6e252ad90fa2d8fe86e155ac6d777c5938d0b2624c0385eb2ef9fe87','prior_failed_repair_sha256':'abe13de9b4f3b0f3028afc9dc1b29adc58550f49a89fac85354a9b230aa208c0','strategy':'lower_direct_pulse_rate_from_measured_response_then_remaining_route','start':start.tolist(),'goal':goal.tolist(),'motion':motion,'controller':{'turn_speed_cap_rad_s':0.25,'turn_acceleration_cap_rad_s2':0.3,'turn_static_threshold_rad_s':0.18,'turn_brake_margin_rad':0.002,'fine_pulse_initial_rate_rad_s':0.11,'fine_pulse_max_rate_rad_s':0.18,'fine_pulse_hold_ticks':2,'drive_min_speed_m_s':0.015,'drive_cap_m_s':0.08,'xy_gate_m':0.0008,'yaw_gate_rad':0.0008},'segment_details':details,'screened_pulse_envelope_rad':0.01,'pulse_envelope_details':pulse_envelope,'all_full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_CARRY_RECOVERY_V9_PROPOSAL.json';p.write_text(json.dumps(out,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'motion':motion},indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a75807d254bc65093cd8cdbee5bbd919dc8e10 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case7_item2_endpoint_projection_v10.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +"""Screen a final-yaw straight projection into the original carried endpoint gate.""" +import hashlib,json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_07' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +def bm(s,z): + m=np.eye(4);m[:3,:3]=Rotation.from_euler('z',s[2]).as_matrix();m[:3,3]=[s[0],s[1],z];return m +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['control_ticks']==44988 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);q=cp['q'][-1].astype(float);names=cp['dof_names'].tolist();z=float(cp['base_pose'][-1,2]);start=np.array([-1.0666638612747192,1.0657597780227661,-1.5737453699111938]);parent=np.array([-1.0657120670686107,1.0644687903151804,-1.5746476816413626]);heading=np.array([math.cos(start[2]),math.sin(start[2])]);along=float((parent[:2]-start[:2])@heading);candidate=start.copy();candidate[:2]+=along*heading +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_l_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];vertices=np.load(item['controls_file'])['target_local'];check=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,props);check.deadline=time.monotonic()+900;assert check.segment(q,q,bm(start,z),bm(candidate,z)),check.last +xy_error=float(np.linalg.norm(candidate[:2]-parent[:2]));yaw_error=abs(math.atan2(math.sin(candidate[2]-parent[2]),math.cos(candidate[2]-parent[2])));assert xy_error<.001 and yaw_error<.001 +out={'schema':'multi_item.case7_item2_endpoint_projection.v10','read_only':True,'status_tick':44988,'parent_failure_sha256':'37a837efc4e96403795fe238572cbdfab6c487e4d240c977914f8c87da4b76bb','start':start.tolist(),'parent_endpoint':parent.tolist(),'projected_endpoint':candidate.tolist(),'commanded_along_m':along,'projected_xy_error_to_parent_m':xy_error,'yaw_error_to_parent_rad':yaw_error,'controller':{'drive_speed_cap_m_s':.05,'drive_acceleration_cap_m_s2':.12,'drive_min_speed_m_s':.008,'parent_xy_gate_m':.001,'parent_yaw_gate_rad':.001},'full_body_held_target_live_props_screened':True,'native_validation_required':True} +p=W/'diagnostics/LAYOUT07_ITEM02_ENDPOINT_PROJECTION_V10_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'projected_endpoint':candidate.tolist(),'xy_error':xy_error,'yaw_error':yaw_error},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py new file mode 100644 index 0000000000000000000000000000000000000000..a121fc6019fbd45df16b8c914e03c2477a1999cb --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_candidates.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + grid = json.loads((W / "diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json").read_text())["top"] + candidates = [(float(entry["score"]), x, y, yaw, footprint, gaps, goal, np.asarray(entry["base"], dtype=float), transfer) for entry in grid] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_grounded_station_candidates_full_qualification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_GROUNDED_STATION_CANDIDATES_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py new file mode 100644 index 0000000000000000000000000000000000000000..fb702e75605e3b144f9fb326ecd34bb915d6194b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_grounded_station_full.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base = np.asarray(json.loads((W / "diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json").read_text())["top"][0]["base"], dtype=float) + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_grounded_station_full_qualification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_GROUNDED_STATION_FULL_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..e2a07be4b942b37003f9cedbe27a8dc958cf7817 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_live_place.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Navigation changes planar SE(2) only. Qualify at the actual + # grounded chassis height captured at this frozen boundary. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..4baaca9648c622daa9ea0e4f46558599664d5252 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_old_success_seed.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_08" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case8 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case8_item1_old_success_seed_requalification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE8_ITEM01_OLD_SUCCESS_SEED_REQUALIFICATION.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..6801f94fafe8dfa859e320663e417921313e3eb7 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case8_item1_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_08' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==1 +job=json.loads((C/'job.json').read_text());item=job['items'][1];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_08/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][1] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +old=json.loads(Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_08/CASE8_ITEM01_LIVE_PLACE_PROPOSAL.json').read_text())['accepted'];center=np.array(old['slot']['xy']);desired=float(old['slot']['yaw_rad']);base0=np.array(old['place_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case8_item1_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT08_ITEM01_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..189f4e706803bcd621c6c8c563e32cb20093c469 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_live_place.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 9's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +C = W / "cases/layout_09" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case9 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + # Native navigation preserves the grounded root height. The + # historical source station carried an unrealizable 11 mm Z + # bias that must not enter FK, bridge, or placement checks. + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]]} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + # The native repair transitions from the carry planner's compact + # destination posture, not directly from the frozen live posture. + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "post_carry_destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "carry_bridge_and_place_descent_world_checks": True, + "release_retreat_offline_scope": "self_and_wrist_only; authoritative native all-target world audit required", + "base_z_normalized_to_live_checkpoint": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case9_item1_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json"), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = W / "CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "carry_bridge_and_place_descent_world_checks", "release_retreat_offline_scope")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d7e2d5f6901f7ed5d4d761abe26686373a6f41 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item1_old_success_seed.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +"""Read-only full-path Chomps place repair probe from case 8's frozen state.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +C = W / "cases/layout_09" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 1: + raise RuntimeError(f"case9 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Actual completed item 0, plus the future item 2 reservation. + item0_pose = matrix(checkpoint["target_pose_all"][-1, 0]) + item0_local = np.load(job["items"][0]["controls_file"])["target_local"] + item0_world = (np.c_[item0_local, np.ones(len(item0_local))] @ item0_pose.T)[:, :2] + obstacles = [Polygon(item0_world[ConvexHull(item0_world).vertices])] + future = Polygon(job["items"][2]["placement_slot"]["footprint_xy"]) + reserved = [future] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + # Requalify the prior native-success seed against this exact fresh held + # transform, actual item0 pose, and current world. This avoids repeating + # the broad candidate scan after its 1800-second read-only timeout. + prior_path = R / "work/shelf_to_table_multi10_20261005/CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json" + prior = json.loads(prior_path.read_text())["accepted"] + x, y = map(float, prior["slot"]["xy"]) + yaw = float(prior["slot"]["yaw_rad"]) + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(obstacles[0]), footprint.distance(future)] + if not floor.covers(footprint) or min(gaps) <= 0.012: + raise RuntimeError(f"prior seed invalid in fresh geometry: inside={floor.covers(footprint)} gaps={gaps}") + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - points[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + candidates = [(0.0, x, y, yaw, footprint, gaps, goal, base, transfer)] + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(live_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "destination_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [job["items"][2]["placement_slot"]["footprint_xy"]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_item0_gap_m": float(gaps[0]), "future_item2_gap_m": float(gaps[1]), "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.case9_item1_old_success_seed_requalification.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / "CASE9_ITEM01_OLD_SUCCESS_SEED_REQUALIFICATION.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py new file mode 100644 index 0000000000000000000000000000000000000000..6e54295176cebaf6db13f880263bd2247d0aa44d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_future_place.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +"""Read-only future Honeymaid carry/place prequalification for case 9.""" +import json, math, os, pathlib, sys, time +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from ik_solver import Arm + +def matrix_xyzw(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +def matrix_wxyz(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m + +def footprint(local,pose): + world=(np.c_[local,np.ones(len(local))]@pose.T)[:,:3] + return Polygon(world[ConvexHull(world[:,:2]).vertices,:2]) + +def main(): + status=json.loads((C/'STATUS.json').read_text());job=json.loads((C/'job.json').read_text());item=job['items'][2] + checkpoint_path=C/'rollout/checkpoint_60hz.npz';checkpoint=np.load(checkpoint_path,allow_pickle=True) + if int(checkpoint['item_index'][-1])!=2:raise RuntimeError('Pinned case9 checkpoint is not the live item2 carry boundary') + zbar_proposal_path=W/'CASE09_ITEM01_LIVE_PLACE_PROPOSAL.json';zbar_proposal=json.loads(zbar_proposal_path.read_text());zbar=zbar_proposal['accepted'] + exact=json.loads((C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json').read_text())['items'][2] + data=np.load(item['controls_file']);prior=np.load(exact['prior_controls']);names=checkpoint['dof_names'].tolist();control_names=data['names'].tolist();ids=[names.index(name) for name in control_names] + q_pick=checkpoint['q'][-1].astype(float).copy();q_place=q_pick.copy();q_place[ids]=prior['place_initial'] + grippers=[i for i,name in enumerate(names) if 'gripper_' in name];q_place[grippers]=q_pick[grippers] + hand_path=f"/World/Robot/gripper_{item['hand']}_base_link";hi=checkpoint['robot_link_paths'].tolist().index(hand_path);ti=checkpoint['target_paths'].tolist().index(item['target']) + held=np.linalg.inv(matrix_xyzw(checkpoint['robot_link_pose'][-1,hi]))@matrix_xyzw(checkpoint['target_pose_all'][-1,ti]) + live_base_z=float(checkpoint['base_pose'][-1,2]);pick_base=matrix_xyzw(checkpoint['base_pose'][-1]) + stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) + predicted_post_pick=matrix_xyzw(checkpoint['target_pose_all'][-1,ti]) + # Preserve actual frozen prop poses, replacing only Zbar with its qualified (not yet final actual) landing. + prop_poses={str(path):matrix_xyzw(pose) for path,pose in zip(checkpoint['rigid_body_paths'],checkpoint['prop_pose'][-1])} + checkpoint_receipt=json.loads((C/'rollout/CHECKPOINT.json').read_text());zbar_is_actual=job['items'][1]['target'] in checkpoint_receipt.get('completed_objects',[]) + if zbar_is_actual: + zbar_goal=matrix_xyzw(checkpoint['target_pose_all'][-1,1]) + else: + zbar_goal=np.asarray(zbar['placement_report']['goal'],float);prop_poses[job['items'][1]['target']]=zbar_goal + gatorade_pose=matrix_xyzw(checkpoint['target_pose_all'][-1,0]);gatorade_local=np.load(job['items'][0]['controls_file'])['target_local'];gatorade_foot=footprint(gatorade_local,gatorade_pose) + zbar_local=np.load(job['items'][1]['controls_file'])['target_local'];zbar_foot=footprint(zbar_local,zbar_goal);obstacles=[gatorade_foot,zbar_foot] + vertices=data['target_local'];floor=Polygon(job['tray_polygon_xy']).buffer(-.008);accepted_goal=np.asarray(exact['transferred_accepted_goal'],float);accepted_base=np.asarray(exact['transferred_accepted_base'],float) + accepted_yaw=float(exact['accepted_yaw_rad']);job_yaw=float(item['placement_slot']['yaw_rad']);yaws=[] + for yaw in [accepted_yaw,accepted_yaw+math.pi,accepted_yaw+math.pi/2,accepted_yaw-math.pi/2,accepted_yaw+math.radians(15),accepted_yaw-math.radians(15),job_yaw,job_yaw+math.pi]: + yaw=float(math.atan2(math.sin(yaw),math.cos(yaw))) + if all(abs(math.atan2(math.sin(yaw-old),math.cos(yaw-old)))>1e-8 for old in yaws):yaws.append(yaw) + minx,miny,maxx,maxy=floor.bounds;candidates=[] + for yaw in yaws: + rot=Rotation.from_euler('z',yaw).as_matrix();points=vertices@rot.T;local=Polygon(points[ConvexHull(points[:,:2]).vertices,:2]) + for x in np.arange(minx,maxx+1e-9,.004): + for y in np.arange(miny,maxy+1e-9,.004): + foot=Polygon(np.asarray(local.exterior.coords)[:-1]+[x,y]);gaps=[foot.distance(o) for o in obstacles] + if not floor.covers(foot) or min(gaps)<=.012:continue + goal=np.eye(4);goal[:3,:3]=rot;goal[:2,3]=[x,y];goal[2,3]=job['tray_floor_z']+.002-(vertices@rot.T)[:,2].min() + transfer=goal@np.linalg.inv(accepted_goal);base=transfer@accepted_base;base[2,3]=live_base_z + score=float(np.linalg.norm(base[:2,3]-np.asarray(item['place_base'])[:2,3])+.12*np.linalg.norm(goal[:2,3]-np.asarray(item['placement_slot']['xy']))) + candidates.append((score,x,y,yaw,foot,gaps,goal,base,transfer)) + candidates.sort(key=lambda row:row[0]);attempts=[];accepted=None;place_only=None + source_layout_id=int(job.get('source_layout_id',job['layout_id']));release_path=pathlib.Path(job['layout']).parents[1]/'audits_release'/f'{source_layout_id:02d}.json';release=json.loads(release_path.read_text()) + checker=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,prop_poses);checker.deadline=time.monotonic()+1800 + arm_ids=[names.index(f"idx{6 if item['hand']=='r' else 2}{k}_arm_{item['hand']}_joint{k}") for k in range(1,8)] + joints={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')} + lo=np.array([joints[names[i]].GetAttribute('physics:lowerLimit').Get() for i in arm_ids])*np.pi/180+1e-4;hi=np.array([joints[names[i]].GetAttribute('physics:upperLimit').Get() for i in arm_ids])*np.pi/180-1e-4 + from motion_limits import LOWER,UPPER + offset=0 if item['hand']=='l' else 7;lo=np.maximum(lo,LOWER[offset:offset+7]+1e-4);hi=np.minimum(hi,UPPER[offset:offset+7]-1e-4) + for rank,candidate in enumerate(candidates[:80]): + _,x,y,yaw,foot,gaps,goal,base,transfer=candidate + # Correct the historical station's 11 mm root-height bias by solving only + # the active arm back to the rigidly transferred accepted object goal. + arm=Arm(robot,names,q_place,base,robot.root+hand_path.removeprefix('/World/Robot'),arm_ids);previous=q_place[arm_ids].copy();hand_goal=goal@np.linalg.inv(held) + fit=least_squares(lambda v:arm.evaluate(v,hand_goal,previous)[0],np.clip(previous,lo,hi),jac=lambda v:arm.evaluate(v,hand_goal,previous)[1],bounds=(lo,hi),max_nfev=100) + q_station=q_place.copy();q_station[arm_ids]=fit.x;actual_hand=arm.forward(fit.x)[0];endpoint_error=[float(np.linalg.norm(actual_hand[:3,3]-hand_goal[:3,3])),float(Rotation.from_matrix(hand_goal[:3,:3].T@actual_hand[:3,:3]).magnitude())] + if endpoint_error[0]>.002 or endpoint_error[1]>.02:attempts.append(dict(rank=rank,stage='normalized_station_endpoint',error=endpoint_error));continue + if not checker.check(q_station,base):attempts.append(dict(rank=rank,stage='destination_place_initial_clearance',detail=checker.last));continue + object_pose=robot.fk(names,q_station,base)[robot.root+hand_path.removeprefix('/World/Robot')]@held + slot=dict(xy=[float(x),float(y)],yaw_rad=float(yaw),footprint_xy=np.asarray(foot.exterior.coords)[:-1].tolist(),tolerance_m=.001) + item_job={**job,**item,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':[],'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};item_job.pop('carry_route_seed',None) + began=time.monotonic() + try:plan,retreat,place_report=solve(robot,names,q_station,base,object_pose,vertices,item_job,obstacles,pose_check,world_check=checker) + except Exception as exc:attempts.append(dict(rank=rank,stage='placement',error=str(exc),elapsed_s=time.monotonic()-began));continue + place_only=dict(rank=rank,slot=slot,destination_xyz=[float(x),float(y),float(job['tray_floor_z'])],place_base=base.tolist(),place_initial20=q_station[ids].tolist(),source_place_initial20=prior['place_initial'].tolist(),normalized_station_endpoint_error=endpoint_error,held_live=held.tolist(),geometry=dict(tray_inset_m=.008,gap_to_actual_gatorade_m=float(gaps[0]),gap_to_actual_zbar_m=float(gaps[1]),inside=True),placement_plan_frames=len(plan),placement_report=place_report,retreat_q=retreat.tolist(),base_z_normalized_to_checkpoint=True,station_bridge_and_place_descent_world_checks=True,release_retreat_offline_scope='self_and_wrist_only; authoritative native all-target world audit required') + try:compact,primitives,carry_report=plan_carry(stage,robot,names,q_pick,pick_base,item['hand'],item['target'],vertices,predicted_post_pick,prop_poses,item_job,release) + except Exception as exc:attempts.append(dict(rank=rank,stage='live_source_carry',error=str(exc),elapsed_s=time.monotonic()-began));break + if not checker.segment(compact,q_station,base,base):attempts.append(dict(rank=rank,stage='post_carry_destination_bridge_clearance',detail=checker.last));continue + accepted=dict(rank=rank,slot=slot,destination_xyz=[float(x),float(y),float(job['tray_floor_z'])],place_base=base.tolist(),place_initial20=q_station[ids].tolist(),source_place_initial20=prior['place_initial'].tolist(),normalized_station_endpoint_error=endpoint_error,held_source=held.tolist(),predicted_post_pick_pose=predicted_post_pick.tolist(),pair_transform=transfer.tolist(),geometry=dict(tray_inset_m=.008,gap_to_actual_gatorade_m=float(gaps[0]),gap_to_zbar_m=float(gaps[1]),zbar_pose_source='actual supported checkpoint' if zbar_is_actual else 'qualified predicted landing',inside=True),placement_plan_frames=len(plan),placement_report=place_report,retreat_q=retreat.tolist(),reposition=dict(compact_q=compact.tolist(),primitives=primitives,report=carry_report),base_z_normalized_to_checkpoint=True,carry_bridge_and_place_descent_world_checks=True,release_retreat_offline_scope='self_and_wrist_only; authoritative native all-target world audit required') + attempts.append(dict(rank=rank,stage='passed',placement_frames=len(plan),reposition_primitives=len(primitives)));break + result=dict(schema='multi_item.case9_item2_live_place.v1',read_only=True,prediction_only=False,status_at_probe=status,pinned_checkpoint_tick=int(checkpoint['tick'][-1]),method='Actual live item2 held transform and full prop poses; exact accepted placement pair with grounded-base-normalized place posture; placement and standard carry evaluated separately.',evidence=dict(checkpoint=str(checkpoint_path),job=str(C/'job.json'),exact_source=str(C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json'),prior_controls=exact['prior_controls'],prior_placement=exact['prior_placement_plan'],zbar_qualified_proposal=str(zbar_proposal_path)),pose_evidence=dict(gatorade='actual checkpoint',zbar='actual supported checkpoint',honeymaid='actual live post-pick checkpoint'),geometry_candidates=len(candidates),accepted=accepted,place_only_qualified=place_only,attempts=attempts,native_recheck_required=['custom live shelf egress if standard carry remains blocked','live carry execution','all final validation gates']) + out=W/'CASE09_ITEM02_LIVE_PLACE_PROPOSAL.json';out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(dict(out=str(out),geometry_candidates=len(candidates),accepted=accepted and {k:accepted[k] for k in ['rank','slot','destination_xyz','place_base','geometry','placement_plan_frames','base_z_normalized_to_checkpoint','carry_bridge_and_place_descent_world_checks','release_retreat_offline_scope']},place_only_qualified=place_only and {k:place_only[k] for k in ['rank','slot','destination_xyz','place_base','geometry','placement_plan_frames','base_z_normalized_to_checkpoint','station_bridge_and_place_descent_world_checks','release_retreat_offline_scope']},attempts=attempts),indent=2));raise SystemExit(0 if accepted or place_only else 2) +if __name__=='__main__':main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py new file mode 100644 index 0000000000000000000000000000000000000000..115071938d4f97e0caf45f2e34ea5b1abd1adf70 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_live_egress.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +"""Read-only diagnosis of case 9's frozen HoneyMaid shelf egress boundary.""" +import hashlib +import json +import math +import os +import pathlib +import sys +import time +import zlib + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [ + str(W), + str(R / "tools/family3"), + str(R / "work/place_training_tray_20260918"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "code/family3_repair/real2sim/teleop"), +] +os.environ["SPATIALINDEX_C_LIBRARY"] = str( + R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so" +) + +from pxr import Usd, UsdGeom, UsdPhysics # noqa: E402 +from collision_scene import ( # noqa: E402 + Body, + ROBOT, + Robot, + burial, + collider_mesh, + contact, + matrix, + rigid_ancestor, +) +from carry_transition import Clearance # noqa: E402 +from ik_solver import Arm # noqa: E402 + + +def pose_matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def rigid_part(value): + value = np.asarray(value, dtype=float) + u, _, vt = np.linalg.svd(value[:3, :3]) + rotation = u @ vt + if np.linalg.det(rotation) < 0: + u[:, -1] *= -1 + rotation = u @ vt + result = np.eye(4) + result[:3, :3] = rotation + result[:3, 3] = value[:3, 3] + return result + + +def decode_native_contacts(path, target, neighbor): + decoder = zlib.decompressobj(16 + zlib.MAX_WBITS) + raw = decoder.decompress(path.read_bytes()) + rows = [] + incomplete_rows = 0 + for line in raw.splitlines(): + try: + row = json.loads(line) + except json.JSONDecodeError: + incomplete_rows += 1 + continue + if set(row.get("actors", [])) == {target, neighbor}: + rows.append(row) + ticks = sorted({int(row["tick"]) for row in rows}) + per_tick = [] + for tick in ticks: + selected = [row for row in rows if int(row["tick"]) == tick] + per_tick.append( + { + "tick": tick, + "rows": len(selected), + "unique_neighbor_colliders": sorted({row["colliders"][0] for row in selected}), + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in selected), + "maximum_impulse_norm": max(float(np.linalg.norm(row["impulse_sum"])) for row in selected), + } + ) + return { + "decoder": "partial gzip stream decoded without final trailer", + "complete_json_rows": len(raw.splitlines()) - incomplete_rows, + "discarded_incomplete_tail_rows": incomplete_rows, + "pair_rows": len(rows), + "first_tick": ticks[0] if ticks else None, + "last_complete_contact_tick": ticks[-1] if ticks else None, + "last_20_tick_summary": per_tick[-20:], + "minimum_reported_separation_m": min(float(row["min_separation_m"]) for row in rows), + "maximum_impulse_norm": max(float(np.linalg.norm(row["impulse_sum"])) for row in rows), + "native_pair_contact_present": bool(rows), + } + + +def target_native_body(stage, target_path, target_pose): + cache = UsdGeom.XformCache() + target_prim = stage.GetPrimAtPath(target_path) + colliders = [] + for prim in Usd.PrimRange(target_prim, Usd.TraverseInstanceProxies()): + if not prim.HasAPI(UsdPhysics.CollisionAPI): + continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False: + continue + mesh, method, approximation = collider_mesh(prim) + owner = rigid_ancestor(prim) + if owner is None or str(owner.GetPath()) != target_path: + continue + authored_owner = rigid_part(matrix(cache.GetLocalToWorldTransform(owner))) + authored_collider = matrix(cache.GetLocalToWorldTransform(prim)) + mesh.apply_transform(np.linalg.inv(authored_owner) @ authored_collider) + colliders.append( + Body( + str(prim.GetPath()) + "#native_target_collider", + mesh, + target_pose, + {"method": method, "approximation": approximation}, + ) + ) + if not colliders: + raise RuntimeError("No enabled target collider reconstructed") + return colliders + + +def pair_key(a, b): + return (a.name, b.name) + + +def main(): + status_path = C / "STATUS.json" + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + contact_path = C / "rollout/contacts.jsonl.gz" + status = json.loads(status_path.read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != 2: + raise RuntimeError(f"Frozen case 9 item 2 boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + target_path = item["target"] + neighbor_path = "/World/Assets/Tier2Items/oreo_gf" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + base = pose_matrix(checkpoint["base_pose"][-1]) + link_paths = checkpoint["robot_link_paths"].tolist() + target_paths = checkpoint["target_paths"].tolist() + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = link_paths.index(hand_path) + target_index = target_paths.index(target_path) + recorded_hand = pose_matrix(checkpoint["robot_link_pose"][-1, hand_index]) + recorded_target = pose_matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(recorded_hand) @ recorded_target + prop_poses = { + str(path): pose_matrix(value) + for path, value in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1]) + } + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + target_vertices = np.load(item["controls_file"])["target_local"] + checker = Clearance(stage, robot, names, item["hand"], target_path, target_vertices, held, prop_poses) + checker.deadline = time.monotonic() + 1800 + local_fk = robot.fk(names, q, np.eye(4)) + self_ok, self_detail = checker.self_check(local_fk, robot.root) + reconstructed_hand = base @ local_fk[robot.root + "/gripper_r_base_link"] + reconstructed_target = reconstructed_hand @ held + fk_error = { + "hand_translation_m": float(np.linalg.norm(reconstructed_hand[:3, 3] - recorded_hand[:3, 3])), + "hand_rotation_rad": float(Rotation.from_matrix(reconstructed_hand[:3, :3].T @ recorded_hand[:3, :3]).magnitude()), + "target_translation_m": float(np.linalg.norm(reconstructed_target[:3, 3] - recorded_target[:3, 3])), + "target_rotation_rad": float(Rotation.from_matrix(reconstructed_target[:3, :3].T @ recorded_target[:3, :3]).magnitude()), + } + native_targets = target_native_body(stage, target_path, recorded_target) + neighbor_environment = [body for body in checker.environment if body.name.startswith(neighbor_path + "/")] + if not neighbor_environment: + raise RuntimeError("No reconstructed Oreo collider bodies") + + yaw = float(Rotation.from_matrix(base[:3, :3]).as_euler("xyz")[2]) + heading = np.array([math.cos(yaw), math.sin(yaw)]) + + def evaluate_state(candidate_q, candidate_base, label): + state_local_fk = robot.fk(names, candidate_q, np.eye(4)) + state_self_ok, state_self_detail = checker.self_check(state_local_fk, robot.root) + world_fk = {name: candidate_base @ value for name, value in state_local_fk.items()} + for body in checker.self_check.bodies: + body.update(world_fk[body.name.replace("/World/Robot", robot.root, 1)]) + candidate_target = world_fk[robot.root + "/gripper_r_base_link"] @ held + checker.target.update(candidate_target) + delta = candidate_target @ np.linalg.inv(recorded_target) + for body in native_targets: + body.update(delta @ body.authored_transform) + + collisions = [] + tested = checker.self_check.bodies + [checker.target] + for body in tested: + indices = np.flatnonzero( + np.all(checker.high >= body.lo, axis=1) & np.all(checker.low <= body.hi, axis=1) + ) + for index in indices: + other = checker.environment[index] + hit = contact(body, other, containment=True) + if hit is not None: + collisions.append({"a": body.name, "b": other.name, "contact": hit}) + native_pair_hits = [] + for target_body in native_targets: + for neighbor_body in neighbor_environment: + hit = contact(target_body, neighbor_body, containment=True) + if hit is not None: + native_pair_hits.append( + { + "target_collider": target_body.name, + "neighbor_collider": neighbor_body.name, + "contact": hit, + "burial": burial(target_body, neighbor_body, max_points=1024), + } + ) + return { + "label": label, + "base_xy": candidate_base[:2, 3].tolist(), + "target_xyz": candidate_target[:3, 3].tolist(), + "self_and_wrist_ok": bool(state_self_ok), + "self_and_wrist_detail": state_self_detail, + "planner_collision_pairs": collisions, + "planner_collision_pair_count": len(collisions), + "planner_target_neighbor_pairs": [ + row for row in collisions if row["a"] == target_path and row["b"].startswith(neighbor_path + "/") + ], + "native_shape_target_neighbor_pairs": native_pair_hits, + "native_shape_target_neighbor_pair_count": len(native_pair_hits), + } + + def evaluate_reverse(distance): + candidate_base = base.copy() + candidate_base[:2, 3] -= distance * heading + result = evaluate_state(q, candidate_base, f"reverse_{distance:.6f}m") + result["reverse_distance_m"] = float(distance) + return result + + # Dense first-centimeter samples isolate the first reverse behavior; later + # samples confirm that the same fixed-grasp base motion remains clear. + distances = [0.0, 0.00025, 0.0005, 0.001, 0.002, 0.003, 0.005, 0.0075, 0.01] + distances += [float(value) for value in np.arange(0.0125, 0.25001, 0.0025)] + samples = [evaluate_reverse(distance) for distance in distances] + initial_pairs = { + (row["a"], row["b"]) for row in samples[0]["planner_collision_pairs"] + } + for sample in samples: + pairs = {(row["a"], row["b"]) for row in sample["planner_collision_pairs"]} + sample["new_planner_pairs_relative_to_start"] = [list(pair) for pair in sorted(pairs - initial_pairs)] + sample["existing_planner_pairs_remaining"] = [list(pair) for pair in sorted(pairs & initial_pairs)] + first_planner_clear = next( + (sample["reverse_distance_m"] for sample in samples if not sample["planner_target_neighbor_pairs"]), None + ) + first_native_shape_clear = next( + (sample["reverse_distance_m"] for sample in samples if sample["native_shape_target_neighbor_pair_count"] == 0), None + ) + no_new_pairs = all(not sample["new_planner_pairs_relative_to_start"] for sample in samples) + all_after_planner_clear = bool( + first_planner_clear is not None + and all( + not sample["planner_target_neighbor_pairs"] + for sample in samples + if sample["reverse_distance_m"] >= first_planner_clear + ) + ) + all_after_native_clear = bool( + first_native_shape_clear is not None + and all( + sample["native_shape_target_neighbor_pair_count"] == 0 + for sample in samples + if sample["reverse_distance_m"] >= first_native_shape_clear + ) + ) + gripper_ids = [index for index, name in enumerate(names) if "gripper_" in name] + native_contacts = decode_native_contacts(contact_path, target_path, neighbor_path) + + # Search small continuous right-arm translations. A target-space move is + # acceptable only if every interpolated posture passes the original + # self/wrist gate, creates no new environment actor pair, and reaches a + # state accepted by the ordinary strict world checker. + arm_ids = [names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + joints = {prim.GetName(): prim for prim in robot.stage.Traverse() if prim.GetAttribute("physics:lowerLimit")} + lower = np.array([joints[names[index]].GetAttribute("physics:lowerLimit").Get() for index in arm_ids]) * np.pi / 180 + 1e-4 + upper = np.array([joints[names[index]].GetAttribute("physics:upperLimit").Get() for index in arm_ids]) * np.pi / 180 - 1e-4 + from motion_limits import LOWER, UPPER + + lower = np.maximum(lower, LOWER[7:14] + 1e-4) + upper = np.minimum(upper, UPPER[7:14] - 1e-4) + arm = Arm(robot, names, q, base, robot.root + "/gripper_r_base_link", arm_ids) + current_hand = reconstructed_hand + + def actor_pair(row): + obstacle = row["b"] + if obstacle.startswith(neighbor_path + "/"): + obstacle = neighbor_path + elif obstacle.startswith("/World/Assets/ShelvingUnit/"): + obstacle = "/World/Assets/ShelvingUnit" + return row["a"], obstacle + + initial_actor_pairs = {actor_pair(row) for row in samples[0]["planner_collision_pairs"]} + arm_candidates = [] + direction_rows = [ + ("world_minus_x", np.array([-1.0, 0.0, 0.0])), + ("world_plus_x", np.array([1.0, 0.0, 0.0])), + ("world_plus_y", np.array([0.0, 1.0, 0.0])), + ("world_minus_y", np.array([0.0, -1.0, 0.0])), + ("world_plus_z", np.array([0.0, 0.0, 1.0])), + ("world_minus_x_plus_z", np.array([-1.0, 0.0, 1.0]) / math.sqrt(2.0)), + ("world_plus_x_plus_z", np.array([1.0, 0.0, 1.0]) / math.sqrt(2.0)), + ("world_plus_y_plus_z", np.array([0.0, 1.0, 1.0]) / math.sqrt(2.0)), + ] + for direction_name, direction in direction_rows: + for distance in (0.002, 0.005, 0.010, 0.015, 0.020, 0.030, 0.040): + goal = current_hand.copy() + goal[:3, 3] += direction * distance + previous = q[arm_ids].copy() + fit = least_squares( + lambda value: arm.evaluate(value, goal, previous)[0], + np.clip(previous, lower, upper), + jac=lambda value: arm.evaluate(value, goal, previous)[1], + bounds=(lower, upper), + max_nfev=120, + ) + endpoint_hand = arm.forward(fit.x)[0] + endpoint_error = { + "translation_m": float(np.linalg.norm(endpoint_hand[:3, 3] - goal[:3, 3])), + "rotation_rad": float(Rotation.from_matrix(endpoint_hand[:3, :3].T @ goal[:3, :3]).magnitude()), + } + row = { + "direction": direction_name, + "distance_m": distance, + "direction_world": direction.tolist(), + "endpoint_error": endpoint_error, + "fit_success": bool(fit.success), + } + if endpoint_error["translation_m"] > 0.001 or endpoint_error["rotation_rad"] > 0.01: + row["qualified"] = False + row["reason"] = "ik_endpoint_error" + arm_candidates.append(row) + continue + endpoint_q = q.copy() + endpoint_q[arm_ids] = fit.x + steps = max(2, int(math.ceil(float(np.max(np.abs(endpoint_q - q))) / 0.005))) + path = [] + prior_native_count = samples[0]["native_shape_target_neighbor_pair_count"] + monotonic_native_count = True + for index, u in enumerate(np.linspace(0.0, 1.0, steps + 1)): + state_q = q * (1.0 - u) + endpoint_q * u + state = evaluate_state(state_q, base, f"{direction_name}_{distance:.3f}_u{u:.6f}") + actor_pairs = {actor_pair(hit) for hit in state["planner_collision_pairs"]} + state["new_actor_pairs"] = [list(pair) for pair in sorted(actor_pairs - initial_actor_pairs)] + native_count = state["native_shape_target_neighbor_pair_count"] + if native_count > prior_native_count: + monotonic_native_count = False + prior_native_count = native_count + path.append(state) + endpoint = path[-1] + endpoint_q_strict_ok = checker.check(endpoint_q, base) + endpoint_q_strict_detail = None if endpoint_q_strict_ok else checker.last + row.update( + { + "endpoint_q": endpoint_q.tolist(), + "max_joint_delta_rad": float(np.max(np.abs(endpoint_q - q))), + "path_sample_count": len(path), + "all_self_and_wrist_ok": all(state["self_and_wrist_ok"] for state in path), + "no_new_actor_pairs": all(not state["new_actor_pairs"] for state in path), + "native_shape_neighbor_count_nonincreasing": monotonic_native_count, + "endpoint_native_shape_neighbor_pair_count": endpoint["native_shape_target_neighbor_pair_count"], + "endpoint_planner_target_neighbor_pair_count": len(endpoint["planner_target_neighbor_pairs"]), + "endpoint_planner_collision_pair_count": endpoint["planner_collision_pair_count"], + "endpoint_strict_world_ok": bool(endpoint_q_strict_ok), + "endpoint_strict_world_detail": endpoint_q_strict_detail, + "path": path, + } + ) + row["qualified"] = bool( + row["all_self_and_wrist_ok"] + and row["no_new_actor_pairs"] + and row["native_shape_neighbor_count_nonincreasing"] + and row["endpoint_native_shape_neighbor_pair_count"] == 0 + and row["endpoint_planner_target_neighbor_pair_count"] == 0 + and row["endpoint_strict_world_ok"] + ) + arm_candidates.append(row) + # The local adjustment is useful only if the unchanged raised posture can + # immediately enter the standard strictly collision-free reverse screen. + for row in arm_candidates: + if not row.get("qualified"): + continue + endpoint_q = np.asarray(row["endpoint_q"], dtype=float) + reverse_results = [] + for distance in (0.05, 0.10, 0.15, 0.25): + exit_base = base.copy() + exit_base[:2, 3] -= distance * heading + okay = checker.segment(endpoint_q, endpoint_q, base, exit_base) + reverse_results.append( + { + "distance_m": distance, + "strict_world_segment_ok": bool(okay), + "detail": None if okay else checker.last, + } + ) + row["ordinary_reverse_rechecks"] = reverse_results + row["qualified_with_ordinary_reverse"] = all(item["strict_world_segment_ok"] for item in reverse_results) + # The ordinary policy already uses a 0.55 m body height after shelf + # departure. Also test a staged escape: a short strict 5 cm reverse, + # raise there, then continue to 25 cm. This avoids dragging the held + # carton through Oreo at its original height. + if reverse_results[0]["strict_world_segment_ok"]: + base_005 = base.copy() + base_005[:2, 3] -= 0.05 * heading + base_025 = base.copy() + base_025[:2, 3] -= 0.25 * heading + high_q = endpoint_q.copy() + high_q[names.index("idx01_body_joint1")] = 0.55 + raise_ok = checker.segment(endpoint_q, high_q, base_005, base_005) + raise_detail = None if raise_ok else checker.last + continue_ok = bool(raise_ok and checker.segment(high_q, high_q, base_005, base_025)) + continue_detail = None if continue_ok else checker.last + row["staged_escape"] = { + "short_reverse_distance_m": 0.05, + "body_height_goal_m": 0.55, + "raise_at_short_reverse_strict_world_ok": bool(raise_ok), + "raise_detail": raise_detail, + "continue_reverse_to_m": 0.25, + "continued_reverse_strict_world_ok": continue_ok, + "continued_reverse_detail": continue_detail, + "high_q": high_q.tolist(), + "qualified": bool(raise_ok and continue_ok), + } + else: + row["staged_escape"] = {"qualified": False, "reason": "first_5cm_reverse_not_clear"} + qualified_arm = next( + ( + row + for row in arm_candidates + if row.get("qualified_with_ordinary_reverse") or row.get("staged_escape", {}).get("qualified") + ), + None, + ) + result = { + "schema": "multi_item.case9_item2_live_egress_diagnosis.v1", + "read_only": True, + "status_at_probe": status, + "pinned_checkpoint_tick": int(checkpoint["tick"][-1]), + "input_sha256": { + "checkpoint": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "partial_contact_stream": hashlib.sha256(contact_path.read_bytes()).hexdigest(), + "job": hashlib.sha256((C / "job.json").read_bytes()).hexdigest(), + }, + "native_contact_evidence": native_contacts, + "reconstruction": { + "fk_error": fk_error, + "self_and_wrist_gate_at_start": bool(self_ok), + "self_and_wrist_detail": self_detail, + "target_collision_representation": "both planner target_local convex hull and authored enabled target collider", + "environment_collision_representation": "authored enabled colliders at literal checkpoint prop poses", + "limitations": [ + "Offline FCL intersections do not reproduce PhysX contact offsets or dynamics.", + "The open native contact gzip ends at the last complete callback row before planning pause.", + ], + }, + "fixed_grasp_invariants": { + "held_target_to_hand_transform": held.tolist(), + "q_unchanged": True, + "gripper_positions_unchanged": q[gripper_ids].tolist(), + "base_z_unchanged_m": float(base[2, 3]), + "base_yaw_unchanged_rad": yaw, + "motion": "straight reverse along negative current heading", + }, + "screen": { + "sample_count": len(samples), + "maximum_reverse_distance_m": max(distances), + "first_planner_hull_neighbor_clear_m": first_planner_clear, + "first_authored_native_shape_neighbor_clear_m": first_native_shape_clear, + "planner_neighbor_stays_clear_after_first_clear": all_after_planner_clear, + "authored_neighbor_stays_clear_after_first_clear": all_after_native_clear, + "no_new_planner_collision_pairs": no_new_pairs, + "initial_planner_pairs": [list(pair) for pair in sorted(initial_pairs)], + "qualified_local_egress": bool( + self_ok + and first_planner_clear is not None + and first_native_shape_clear is not None + and all_after_planner_clear + and all_after_native_clear + and no_new_pairs + ), + "samples": samples, + }, + "local_arm_escape_search": { + "method": "right-hand Cartesian micro-translation with fixed orientation/base; bounded IK; linear joint interpolation at <=0.005 rad per sample", + "initial_allowed_actor_pairs": [list(pair) for pair in sorted(initial_actor_pairs)], + "candidate_count": len(arm_candidates), + "qualified": qualified_arm, + "candidate_summaries": [ + {key: value for key, value in row.items() if key not in ("path", "endpoint_q")} + for row in arm_candidates + ], + }, + "execution_requirement": { + "proposal_only": True, + "no_reset_or_pose_write_performed": True, + "required_native_behavior": "Execute a continuous low-speed reverse from the frozen state while preserving arm and gripper commands; permit only the already-recorded HoneyMaid/Oreo manifold at the first sample, require its impulses/separation to relax immediately, abort on any new contact, then use the ordinary full collision gate after the offline clear distance.", + "ordinary_carry_replan_required_after_egress": True, + }, + } + output = W / "CASE09_ITEM02_LIVE_EGRESS_DIAGNOSIS.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print( + json.dumps( + { + "output": str(output), + "checkpoint_tick": result["pinned_checkpoint_tick"], + "native_contact": { + key: native_contacts[key] + for key in ( + "pair_rows", + "first_tick", + "last_complete_contact_tick", + "minimum_reported_separation_m", + "maximum_impulse_norm", + ) + }, + "fk_error": fk_error, + "screen": {key: value for key, value in result["screen"].items() if key != "samples"}, + "qualified_arm": None if qualified_arm is None else { + key: value for key, value in qualified_arm.items() if key not in ("path", "endpoint_q") + }, + }, + indent=2, + ) + ) + raise SystemExit(0 if result["screen"]["qualified_local_egress"] or qualified_arm is not None else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..080fd6c704f1598f31cb2e9ef9aaf363ae54077d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_polar_endpoint.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""Read-only polar station/arm endpoint search for case9 final HoneyMaid.""" +import json,math,os,sys,time +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==2 +j=json.load(open(C/'job.json'));it=j['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;invheld=np.linalg.inv(held);verts=np.load(it['controls_file'])['target_local'];props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,'r',it['target'],verts,held,props);checker.deadline=time.monotonic()+3600 +active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;up=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);up=np.minimum(up,UPPER[7:14]-1e-4) +exact=json.load(open(R/'work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json'))['items'][2];prior=np.load(exact['prior_controls']);controls=np.load(it['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];seedq=liveq.copy();seedq[ids]=prior['place_initial'];seedq[[i for i,n in enumerate(names) if 'gripper_' in n]]=liveq[[i for i,n in enumerate(names) if 'gripper_' in n]] +# Measured completed-object footprints and geometry-valid free-space goals. +obs=[] +for k in range(2): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +floor=Polygon(j['tray_polygon_xy']).buffer(-.008);goals=[] +for yaw in [-math.pi/2,math.pi/2,-math.pi/2+math.radians(5),math.pi/2+math.radians(5)]: + Rz=Rotation.from_euler('z',yaw).as_matrix();local=verts@Rz.T;h=ConvexHull(local[:,:2]);lh=local[h.vertices,:2] + for x in [-1.0225,-1.0255,-1.0285,-1.0315,-1.0345]: + for y in [.43,.445,.46,.475,.49,.505]: + fp=Polygon(lh+[x,y]);gaps=[fp.distance(o) for o in obs];extra=floor.boundary.distance(fp) + if floor.covers(fp) and min(gaps)>.012 and extra>.0003: + g=np.eye(4);g[:3,:3]=Rz;g[:2,3]=[x,y];g[2,3]=j['tray_floor_z']+.002-local[:,2].min();goals.append((g,fp,gaps,extra)) +results=[];tested=0 +for gi,(goal,fp,gaps,extra) in enumerate(goals): + for angle in np.deg2rad(np.arange(-180,181,30)): + for radius in [.52,.62,.72,.82]: + bx=goal[0,3]+radius*math.cos(angle);by=goal[1,3]+radius*math.sin(angle) + face=math.atan2(goal[1,3]-by,goal[0,3]-bx) + for da in np.deg2rad([-12,0,12]): + base=np.eye(4);base[:3,:3]=Rotation.from_euler('z',face+da).as_matrix();base[:3,3]=[bx,by,livebase[2,3]] + arm=Arm(robot,names,seedq,base,robot.root+'/gripper_r_base_link',active);target=goal@invheld + best=None + for seed in [seedq[active],liveq[active]]: + fit=least_squares(lambda x:arm.evaluate(x,target,seed)[0],np.clip(seed,lo,up),jac=lambda x:arm.evaluate(x,target,seed)[1],bounds=(lo,up),max_nfev=130);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude()) + if best is None or pe+.1*re.004 or best[2]>.04:continue + q=seedq.copy();q[active]=best[3];checker.deadline=time.monotonic()+3600 + if not checker.check(q,base):continue + travel=float(np.linalg.norm(base[:2,3]-livebase[:2,3]));results.append(dict(score=travel+best[0]-min(gaps),goal=goal.tolist(),footprint_xy=np.asarray(fp.exterior.coords)[:-1].tolist(),gaps_m=[float(x) for x in gaps],extra_inset_m=float(extra),base=base.tolist(),q=q.tolist(),place_initial20=q[ids].tolist(),position_error_m=best[1],rotation_error_rad=best[2],travel_m=travel)) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case9_item2_polar_endpoint.v1','read_only':True,'status_at_probe':s,'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:80]};p=W/'diagnostics/LAYOUT09_ITEM02_POLAR_ENDPOINT.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:5]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py new file mode 100644 index 0000000000000000000000000000000000000000..ae3ef3f4b6930011eeea9959bcf1451ea444ce0f --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_repack_full.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +"""Read-only case9 item2 repack search from the frozen native boundary. + +The earlier station grid proved IK only at the authored slot, which the two +measured supported objects now occupy. This probe translates each grounded +IK solution and its station together into geometry-valid free tray space, +then performs the normal full carry, placement and release-sweep checks. +""" +import json, math, os, pathlib, sys, time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [str(W), str(R / "tools/family3"), + str(R / "work/family3_repair/collision/python_deps"), + str(R / "work/place_training_tray_20260918"), + str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd +from collision_scene import Robot, ROBOT +from carry_transition import Clearance, plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + + +def mat(pose): + out = np.eye(4); out[:3, 3] = pose[:3] + out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return out + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + assert status.get("status") == "WAITING_FOR_PLANNING_REPAIR" and status.get("item_index") == 2 + job = json.loads((C / "job.json").read_text()); item = job["items"][2] + cp_path = C / "rollout/checkpoint_60hz.npz"; cp = np.load(cp_path, allow_pickle=True) + names = cp["dof_names"].tolist(); live_q = cp["q"][-1].astype(float); live_base = mat(cp["base_pose"][-1]) + hi = cp["robot_link_paths"].tolist().index("/World/Robot/gripper_r_base_link") + ti = cp["target_paths"].tolist().index(item["target"]) + live_target = mat(cp["target_pose_all"][-1, ti]) + held = np.linalg.inv(mat(cp["robot_link_pose"][-1, hi])) @ live_target + controls = np.load(item["controls_file"]); vertices = controls["target_local"] + exact_path = R / "work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][2]; prior = np.load(exact["prior_controls"]) + ids = [names.index(n) for n in controls["names"].tolist()] + active = [names.index(f"idx6{k}_arm_r_joint{k}") for k in range(1, 8)] + grippers = [i for i, n in enumerate(names) if "gripper_" in n] + + stage = Usd.Stage.Open(job["scene"]); robot = Robot(ROBOT); robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(p): mat(v) for p, v in zip(cp["rigid_body_paths"], cp["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, held, prop_poses) + + obstacles = [] + for i in range(2): + pm = mat(cp["target_pose_all"][-1, i]); pv = np.load(job["items"][i]["controls_file"])["target_local"] + pts = (np.c_[pv, np.ones(len(pv))] @ pm.T)[:, :2] + obstacles.append(Polygon(pts[ConvexHull(pts).vertices])) + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + + grid_path = W / "diagnostics/LAYOUT09_ITEM02_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + raw = [] + # A 3 mm tray grid, retaining at least 12 mm from both measured objects. + minx, miny, maxx, maxy = floor.bounds + for entry_index, entry in enumerate(grid): + source_goal = np.asarray(entry["goal"], float); yaw = float(Rotation.from_matrix(source_goal[:3, :3]).as_euler("xyz")[2]) + local = vertices @ source_goal[:3, :3].T + hull = ConvexHull(local[:, :2]); local_hull = local[hull.vertices, :2] + for x in np.arange(minx, maxx + 1e-6, .003): + for y in np.arange(miny, maxy + 1e-6, .003): + footprint = Polygon(local_hull + [x, y]); gaps = [footprint.distance(o) for o in obstacles] + inset_extra = floor.boundary.distance(footprint) + if not floor.covers(footprint) or min(gaps) <= .012 or inset_extra < .0005: + continue + delta = np.array([x - source_goal[0, 3], y - source_goal[1, 3]]) + base = np.asarray(entry["base"], float).copy(); base[:2, 3] += delta + goal = source_goal.copy(); goal[:2, 3] = [x, y] + # Prefer clearance and short base travel, while preserving diverse stations. + travel = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3])) + score = travel - 2.0 * min(gaps) - .5 * inset_extra + float(entry["score"]) + raw.append((score, entry_index, goal, base, footprint, gaps, inset_extra, entry)) + raw.sort(key=lambda x: x[0]) + # Keep spatially diverse candidates so a single blocked station does not dominate. + candidates = [] + for candidate in raw: + goal, base = candidate[2], candidate[3] + goal_yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + def same_pose(c): + other_yaw = float(Rotation.from_matrix(c[2][:3, :3]).as_euler("xyz")[2]) + dyaw = abs(math.atan2(math.sin(goal_yaw - other_yaw), math.cos(goal_yaw - other_yaw))) + return np.linalg.norm(goal[:2, 3] - c[2][:2, 3]) < .012 and dyaw < math.radians(4) + if any(same_pose(c) for c in candidates): + continue + candidates.append(candidate) + if len(candidates) >= 60: break + + attempts = []; accepted = None + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + for rank, candidate in enumerate(candidates): + _, entry_index, goal, base, footprint, gaps, inset_extra, entry = candidate + q = live_q.copy(); q[ids] = prior["place_initial"]; q[active] = np.asarray(entry["q7"], float); q[grippers] = live_q[grippers] + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + checker.deadline = time.monotonic() + 1800 + if not checker.check(q, base): + attempts.append({"rank": rank, "stage": "destination_clearance", "detail": checker.last}); continue + object_pose = robot.fk(names, q, base)[robot.root + "/gripper_r_base_link"] @ held + slot = {"xy": goal[:2, 3].tolist(), "yaw_rad": yaw, + "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": .001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), + "reserved_tray_footprints": [], "require_release_sweep": True} + try: + rs = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, q, base, object_pose, vertices, item_job, + obstacles, pose_check, world_check=checker, release_sweep=rs) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [.25, .45, .15, .05]} + carry_job.pop("carry_route_seed", None) + compact, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, + item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact, q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_place_initial", "detail": checker.last}); continue + except Exception as exc: + attempts.append({"rank": rank, "stage": "full", "error": repr(exc)}); continue + accepted = {"rank": rank, "grid_entry_index": entry_index, "slot": slot, + "destination_xyz": [float(goal[0, 3]), float(goal[1, 3]), float(job["tray_floor_z"])], + "place_base": base.tolist(), "place_initial20": q[ids].tolist(), + "geometry": {"actual_completed_gaps_m": [float(v) for v in gaps], + "tray_inset_m": .008, "extra_inset_m": float(inset_extra)}, + "placement_plan_frames": len(plan), "placement_report": report, + "retreat_q": retreat.tolist(), "reposition": {"compact_q": compact.tolist(), + "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, "full_world_checks": True} + attempts.append({"rank": rank, "stage": "passed"}); break + + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks"): + raise RuntimeError("native boundary changed") + result = {"schema": "multi_item.case9_item2_repack_full.v1", "read_only": True, + "status_at_probe": status, "evidence": {"checkpoint": str(cp_path), "grid": str(grid_path)}, + "geometry_raw_count": len(raw), "candidate_count": len(candidates), + "accepted": accepted, "attempts": attempts, "native_validation_required": True} + out = W / "diagnostics/LAYOUT09_ITEM02_REPACK_FULL_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_raw_count": len(raw), "accepted": accepted, + "attempts": attempts}, indent=2)) + if accepted is None: raise SystemExit(2) + + +if __name__ == "__main__": main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7ae631710e08218817bdf46f9fdfb672e73b96 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_case9_item2_station_ik_grid.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Read-only grounded-base endpoint IK grid for case8 item1.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent;R=W.parents[1];C=W/'cases/layout_09' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==2 +job=json.loads((C/'job.json').read_text());item=job['items'][2];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index('/World/Robot/gripper_r_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact=json.loads((Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005/cases/layout_09/EXACT_SOURCE_TRANSFER_PROPOSAL.json')).read_text())['items'][2] +prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +link=robot.root+'/gripper_r_base_link';active=[names.index(f'idx6{k}_arm_r_joint{k}') for k in range(1,8)];jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4;lo=np.maximum(lo,LOWER[7:14]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[7:14]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.09,.091,.03): + for dy in np.arange(-.09,.091,.03): + for da in np.deg2rad(np.arange(-9.1,9.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy + arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.case9_item2_grounded_station_ik_grid.v1','read_only':True,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:40]};p=W/'diagnostics/LAYOUT09_ITEM02_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_current_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_current_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..ed1e6ea39ab2277fb1362eb5eda206717eba49de --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_current_live_place.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe for a frozen M2 boundary. + +The accepted-source correspondence comes from the immutable M1 preparation +record. Every motion and collision check uses the current M2 checkpoint, +scene, live grasp transform, grounded base height, and all current props. +""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +OLD = W.parent / "shelf_to_table_multi10_20261005" +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = OLD / f"cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX + 1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2, 3] = live_base[2, 3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + by_yaw = {yaw: [] for yaw in yaws} + for candidate in candidates: + by_yaw[candidate[3]].append(candidate) + candidates = [bucket[rank] for rank in range(max(map(len, by_yaw.values()), default=0)) for bucket in by_yaw.values() if rank < len(bucket)] + print("geometry candidates", len(candidates), flush=True) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:80]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + print("station", rank, x, y, yaw, flush=True) + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX + 1:]], "placement_candidate_budget": 20} + began = time.monotonic() + try: + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(v) for v in gaps[:ITEM_INDEX]], "future_gaps_m": [float(v) for v in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + "retreat_native_world_validation_required": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.current_exact_source_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "old_exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_CURRENT_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py new file mode 100644 index 0000000000000000000000000000000000000000..750b4b9d596d8d7ccc22fe7397d7f1f78a25e25b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_exact_chain_landing.py @@ -0,0 +1,48 @@ +"""Probe transfer of the exact accepted chain landing for one case's first item.""" +import sys,os,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +from scipy.spatial import ConvexHull +W=Path(__file__).resolve().parent;R=W.parents[1];OLD=R/'work/shelf_to_table50_20261001' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from geometry_transfer import registration +from shapely.geometry import Polygon +from shapely import affinity + +cid=int(sys.argv[1]);case=W/'cases'/f'layout_{cid:02d}';job=json.loads((case/'job.json').read_text());item=job['items'][0];prior_path=next(p for p in sorted((OLD/'cases').glob('layout_*/job.json')) if json.loads(p.read_text()).get('pick_source')==item['pick_source']);prior=json.loads(prior_path.read_text());oldstage=Usd.Stage.Open(prior['layout']);newstage=Usd.Stage.Open(job['layout']);delta,_=registration(oldstage,newstage,'/World/Assets/CoffeeTable/Mesh') +oldgoal=np.asarray(json.loads((prior_path.parent/'rollout/PLACEMENT_PLAN.json').read_text())['goal']);goal=delta@oldgoal;base=delta@np.asarray(prior['place_base']);base[2,3]=np.asarray(job['initial_base'])[2,3] +data=np.load(item['controls_file']);verts=data['target_local'];foot=Polygon((verts@goal[:3,:3].T)[ConvexHull((verts@goal[:3,:3].T)[:,:2]).vertices,:2]+goal[:2,3]);floor=Polygon(job['tray_polygon_xy']);future=[Polygon(x['placement_slot']['footprint_xy']) for x in job['items'][1:]];gaps=[foot.distance(x) for x in future] +stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];selfcheck=build(stage);pick=Path(item['pick_source']);z=np.load(pick/'recording_20hz.npz');names=z['robot_dof_names'].tolist();ids=[names.index(n) for n in data['names']] +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +ti=z['rigid_body_paths'].tolist().index(item['target']);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link');held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]);q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:];obj=robot.fk(names,q,base)[robot.root+'/gripper_'+item['hand']+'_base_link']@held +yaw=float(math.atan2(goal[1,0],goal[0,0]));first_slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':np.asarray(foot.exterior.coords[:-1]).tolist(),'tolerance_m':.004};proposed=[first_slot];chosen=[foot] +# Repack future reservations around the proven first landing when the original packing overlaps it. +if any(g<=.012 for g in gaps): + pts=np.asarray(job['tray_polygon_xy']);center=np.asarray(floor.centroid.coords[0]);vals,vecs=np.linalg.eigh(np.cov((pts-center).T));u=vecs[:,np.argmax(vals)];v=np.array([-u[1],u[0]]);baseyaw=math.atan2(u[1],u[0]);inside=floor.buffer(-.008) + def search(i): + if i==len(job['items']):return True + it=job['items'][i];local=np.load(it['controls_file'])['target_local'][:,:2];poly=Polygon(local[ConvexHull(local).vertices]) + for vv in [-.07,-.045,-.02,.02,.045,.07]: + for uu in [-.12,-.08,-.04,0,.04,.08,.12]: + xy=center+uu*u+vv*v + for theta in [baseyaw,baseyaw+math.pi/2,baseyaw+math.pi,baseyaw-math.pi/2]: + p=affinity.translate(affinity.rotate(poly,theta*180/math.pi,origin=(0,0)),xy[0],xy[1]) + if not inside.covers(p) or any(p.distance(q)<=.012 for q in chosen):continue + chosen.append(p);proposed.append({'xy':xy.tolist(),'yaw_rad':theta,'footprint_xy':np.asarray(p.exterior.coords[:-1]).tolist(),'tolerance_m':.012}) + if search(i+1):return True + chosen.pop();proposed.pop() + return False + if not search(1):raise RuntimeError('Cannot repack future slots around exact accepted first landing') +ij={**job,**item,'place_base':base.tolist(),'destination_xyz':goal[:3,3].tolist(),'placement_slot':first_slot,'reserved_tray_footprints':[x['footprint_xy'] for x in proposed[1:]]} +result={'case':cid,'source_prior_job':str(prior_path),'base':base.tolist(),'goal':goal.tolist(),'slot':ij['placement_slot'],'proposed_slots':proposed,'floor_inset8_covers':floor.buffer(-.008).covers(foot),'future_gaps_before_repack':gaps,'future_gaps_after_repack':[chosen[a].distance(chosen[b]) for a in range(len(chosen)) for b in range(a+1,len(chosen))]} +try: + plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,[],selfcheck);result.update(passed=True,frames=len(plan),report=report) +except Exception as exc:result.update(passed=False,error=str(exc)) +(case/'EXACT_CHAIN_LANDING_PROPOSAL.json').write_text(json.dumps(result,indent=2,default=lambda x:x.tolist())+'\n');print(json.dumps(result,default=lambda x:x.tolist())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..7bad7bf54c81d8608e9acea5d61ddd500d96e02b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + grid_path = W / f"diagnostics/LAYOUT{CASE_INDEX:02d}_ITEM{ITEM_INDEX:02d}_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + candidates = [] + for entry in grid: + goal = np.asarray(entry["goal"], dtype=float) + x, y = map(float, goal[:2, 3]) + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + points = vertices @ goal[:3, :3].T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps, default=1.0) <= 0.012: + continue + base = np.asarray(entry["base"], dtype=float) + transfer = goal @ np.linalg.inv(accepted_goal) + candidates.append((float(entry["score"]), x, y, yaw, footprint, gaps, goal, base, transfer)) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + checker.deadline = time.monotonic() + 1800 + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.station_ik_grid_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_GRID_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py new file mode 100644 index 0000000000000000000000000000000000000000..ba7fb22391b3a36f8364f9b13ef4dba057e2e55b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_grid_live_place_q7.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + grid_path = W / f"diagnostics/LAYOUT{CASE_INDEX:02d}_ITEM{ITEM_INDEX:02d}_STATION_IK_GRID.json" + grid = json.loads(grid_path.read_text())["top"] + candidates = [] + for entry in grid: + goal = np.asarray(entry["goal"], dtype=float) + x, y = map(float, goal[:2, 3]) + yaw = float(Rotation.from_matrix(goal[:3, :3]).as_euler("xyz")[2]) + points = vertices @ goal[:3, :3].T + footprint = Polygon(points[ConvexHull(points[:, :2]).vertices, :2] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps, default=1.0) <= 0.012: + continue + base = np.asarray(entry["base"], dtype=float) + transfer = goal @ np.linalg.inv(accepted_goal) + candidates.append((float(entry["score"]), x, y, yaw, footprint, gaps, goal, base, transfer, entry)) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates): + _, x, y, yaw, footprint, gaps, goal, base, transfer, entry = candidate + # The station grid's collision-independent IK is the endpoint seed. + # Retain all non-active joints from the exact accepted source pair and + # preserve every measured gripper DOF from the live grasp. + q_destination = exact_q.copy() + prefix = 2 if item["hand"] == "l" else 6 + active = [names.index(f"idx{prefix}{k}_arm_{item['hand']}_joint{k}") for k in range(1, 8)] + q_destination[active] = np.asarray(entry["q7"], dtype=float) + q_destination[grippers] = live_q[grippers] + checker.deadline = time.monotonic() + 1800 + if not checker.check(q_destination, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, q_destination, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True, + # The grid already selected an exact supported slot/yaw. + # Five entries cover the solver's direct equivalent-yaw set; + # random 1 mm jitter adds no meaningful geometry here. + "placement_candidate_budget": 5} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + checker.deadline = time.monotonic() + 1800 + plan, retreat, report = solve(robot, names, q_destination, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + checker.deadline = time.monotonic() + 1800 + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + checker.deadline = time.monotonic() + 1800 + if not checker.segment(compact_q, q_destination, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": q_destination[ids].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.station_ik_grid_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_GRID_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place.py new file mode 100644 index 0000000000000000000000000000000000000000..06b5c56947afe3e42cd3682d077685816ab3c803 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "work/place_training_tray_20260918"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact_path = C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json" + if not exact_path.is_file(): + exact_path = R / f"work/shelf_to_table_multi10_20261005/cases/layout_{CASE_INDEX:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json" + exact = json.loads(exact_path.read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + attempts = [] + accepted = None + for rank, candidate in enumerate(candidates[:300]): + _, x, y, yaw, footprint, gaps, goal, base, transfer = candidate + if not checker.check(exact_q, base): + attempts.append({"rank": rank, "stage": "destination_place_initial_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + object_pose = robot.fk(names, exact_q, base)[robot.root + f"/gripper_{item['hand']}_base_link"] @ held + slot = {"xy": [float(x), float(y)], "yaw_rad": float(yaw), "footprint_xy": np.asarray(footprint.exterior.coords)[:-1].tolist(), "tolerance_m": 0.001} + item_job = {**job, **item, "placement_slot": slot, "place_base": base.tolist(), "reserved_tray_footprints": [it["placement_slot"]["footprint_xy"] for it in job["items"][ITEM_INDEX+1:]], "require_release_sweep": True} + began = time.monotonic() + try: + release_sweep = ReleaseSweep(stage, robot, names, item["hand"], item["target"], vertices, + prop_poses, item["place_source"], pose_check=pose_check, deadline_s=1800) + plan, retreat, report = solve(robot, names, exact_q, base, object_pose, vertices, item_job, obstacles, pose_check, world_check=checker, release_sweep=release_sweep) + except Exception as exc: + attempts.append({"rank": rank, "stage": "placement", "xy": [x, y], "yaw": yaw, "error": str(exc), "elapsed_s": time.monotonic() - began}) + continue + # Validate the physical reposition policy used by repair_place_station. + source_layout_id = int(job.get("source_layout_id", job["layout_id"])) + release_path = pathlib.Path(job["layout"]).parents[1] / "audits_release" / f"{source_layout_id:02d}.json" + release = json.loads(release_path.read_text()) + carry_job = {**item_job, "carry_policy": "precision_body_route_v2", "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05]} + carry_job.pop("carry_route_seed", None) + try: + compact_q, primitives, carry_report = plan_carry(stage, robot, names, live_q, live_base, item["hand"], item["target"], vertices, live_target, prop_poses, carry_job, release) + except Exception as exc: + attempts.append({"rank": rank, "stage": "reposition_carry", "xy": [x, y], "yaw": yaw, "error": str(exc)}) + continue + if not checker.segment(compact_q, exact_q, base, base): + attempts.append({"rank": rank, "stage": "compact_to_exact_bridge_clearance", "xy": [x, y], "yaw": yaw, "detail": checker.last}) + continue + accepted = { + "rank": rank, + "slot": slot, + "destination_xyz": [float(x), float(y), float(job["tray_floor_z"])], + "place_base": base.tolist(), + "place_initial20": prior_controls["place_initial"].tolist(), + "exact_pair_transform": transfer.tolist(), + "geometry": {"tray_inset_m": 0.008, "actual_completed_gaps_m": [float(x) for x in gaps[:ITEM_INDEX]], "future_gaps_m": [float(x) for x in gaps[ITEM_INDEX:]], "inside": True}, + "placement_plan_frames": int(len(plan)), + "placement_report": report, + "retreat_q": retreat.tolist(), + "reposition": {"compact_q": compact_q.tolist(), "primitives": primitives, "report": carry_report}, + "compact_to_exact_bridge_clearance": True, + "full_world_checks": True, + } + attempts.append({"rank": rank, "stage": "passed", "xy": [x, y], "yaw": yaw, "placement_frames": len(plan), "reposition_primitives": len(primitives)}) + break + result = { + "schema": "multi_item.exact_source_live_place.v1", + "read_only": True, + "status_at_probe": status, + "evidence": {"checkpoint": str(checkpoint_path), "job": str(C / "job.json"), "exact_source": str(exact_path), "prior_controls": exact["prior_controls"], "prior_placement": exact["prior_placement_plan"]}, + "place_initial_difference_l2_rad": float(np.linalg.norm(controls["place_initial"] - prior_controls["place_initial"])), + "place_initial_difference_max_rad": float(np.max(np.abs(controls["place_initial"] - prior_controls["place_initial"]))), + "geometry_candidates": len(candidates), + "accepted": accepted, + "attempts": attempts, + "native_validation_required": True, + } + out = C / f"CASE{CASE_INDEX}_ITEM{ITEM_INDEX:02d}_LIVE_PLACE_PROPOSAL.json" + current = json.loads((C / "STATUS.json").read_text()) + if current.get("status") != status.get("status") or current.get("control_ticks") != status.get("control_ticks") or current.get("item_index") != ITEM_INDEX: + raise RuntimeError("Native boundary changed during read-only probe") + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"out": str(out), "geometry_candidates": len(candidates), "accepted": accepted and {k: accepted[k] for k in ("rank", "slot", "destination_xyz", "place_base", "place_initial20", "geometry", "placement_plan_frames", "full_world_checks")}, "attempts": attempts}, indent=2)) + if accepted is None: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place_fast.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..0b212f910bda3495c418f689fe06f244864b0cc1 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_live_place_fast.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +"""Read-only exact-source carried placement probe from a frozen native boundary.""" +import json +import math +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance, plan as plan_carry # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + +CASE_INDEX = int(sys.argv[1]) +ITEM_INDEX = int(sys.argv[2]) +C = W / f"cases/layout_{CASE_INDEX:02d}" + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + status = json.loads((C / "STATUS.json").read_text()) + if status.get("status") != "WAITING_FOR_PLANNING_REPAIR" or status.get("item_index") != ITEM_INDEX: + raise RuntimeError(f"native boundary changed: {status}") + job = json.loads((C / "job.json").read_text()) + item = job["items"][ITEM_INDEX] + exact = json.loads((C / "EXACT_SOURCE_TRANSFER_PROPOSAL.json").read_text())["items"][ITEM_INDEX] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + live_q = checkpoint["q"][-1].astype(float) + names = checkpoint["dof_names"].tolist() + live_base = matrix(checkpoint["base_pose"][-1]) + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][-1, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][-1, hand_index])) @ live_target + controls = np.load(item["controls_file"]) + prior_controls = np.load(exact["prior_controls"]) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + exact_q = live_q.copy() + exact_q[ids] = prior_controls["place_initial"] + # Preserve every physical gripper DOF at the frozen live grasp. + grippers = [i for i, name in enumerate(names) if "gripper_" in name] + exact_q[grippers] = live_q[grippers] + + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + prop_poses = {str(path): matrix(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][-1])} + checker = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + checker.deadline = time.monotonic() + 1800 + + # Use all measured completed targets and reserve every future item slot. + obstacles = [] + for prev in range(ITEM_INDEX): + prior_pose = matrix(checkpoint["target_pose_all"][-1, prev]) + prior_local = np.load(job["items"][prev]["controls_file"])["target_local"] + prior_world = (np.c_[prior_local, np.ones(len(prior_local))] @ prior_pose.T)[:, :2] + obstacles.append(Polygon(prior_world[ConvexHull(prior_world).vertices])) + reserved = [Polygon(it["placement_slot"]["footprint_xy"]) for it in job["items"][ITEM_INDEX+1:]] + floor = Polygon(job["tray_polygon_xy"]).buffer(-0.008) + vertices = controls["target_local"] + accepted_goal = np.asarray(exact["transferred_accepted_goal"], dtype=float) + accepted_base = np.asarray(exact["transferred_accepted_base"], dtype=float) + + candidates = [] + minx, miny, maxx, maxy = floor.bounds + yaws = [float(exact["accepted_yaw_rad"] + offset) for offset in (0, math.pi, math.pi / 2, -math.pi / 2, math.radians(15), math.radians(-15))] + for yaw in yaws: + rotation = Rotation.from_euler("z", yaw).as_matrix() + points = vertices @ rotation.T + local = Polygon(points[ConvexHull(points[:, :2]).vertices, :2]) + for x in np.arange(minx, maxx + 1e-9, 0.004): + for y in np.arange(miny, maxy + 1e-9, 0.004): + footprint = Polygon(np.asarray(local.exterior.coords)[:-1] + [x, y]) + gaps = [footprint.distance(o) for o in obstacles + reserved] + if not floor.covers(footprint) or min(gaps) <= 0.012: + continue + goal = np.eye(4) + goal[:3, :3] = rotation + goal[:2, 3] = [x, y] + goal[2, 3] = job["tray_floor_z"] + 0.002 - (vertices @ rotation.T)[:, 2].min() + transfer = goal @ np.linalg.inv(accepted_goal) + base = transfer @ accepted_base + base[2,3] = live_base[2,3] + score = float(np.linalg.norm(base[:2, 3] - live_base[:2, 3]) + 0.1 * np.linalg.norm(goal[:2, 3] - np.asarray(item["placement_slot"]["xy"]))) + candidates.append((score, x, y, yaw, footprint, gaps, goal, base, transfer)) + candidates.sort(key=lambda entry: entry[0]) + # Cover station orientations before spending time on nearby grid offsets. + by_yaw = {yaw: [] for yaw in yaws} + for candidate in candidates: by_yaw[candidate[3]].append(candidate) + candidates = [bucket[rank] for rank in range(max(map(len,by_yaw.values()),default=0)) for bucket in by_yaw.values() if rank.012 and extra>.0003: + g=np.eye(4);g[:3,:3]=Rz;g[:2,3]=xy;g[2,3]=j['tray_floor_z']+.002-local[:,2].min();goals.append((g,fp,gaps,extra)) +# Deduplicate equivalent circular/symmetric goals. +unique=[] +for x in goals: + if not any(np.linalg.norm(x[0][:2,3]-y[0][:2,3])<1e-6 and abs(math.atan2(math.sin(Rotation.from_matrix(x[0][:3,:3]).as_euler('xyz')[2]-Rotation.from_matrix(y[0][:3,:3]).as_euler('xyz')[2]),math.cos(Rotation.from_matrix(x[0][:3,:3]).as_euler('xyz')[2]-Rotation.from_matrix(y[0][:3,:3]).as_euler('xyz')[2])))<1e-6 for y in unique):unique.append(x) +goals=unique;results=[];tested=0 +for gi,(goal,fp,gaps,extra) in enumerate(goals): + for angle in np.deg2rad(np.arange(-180,181,30)): + for radius in [.52,.62,.72,.82]: + bx=goal[0,3]+radius*math.cos(angle);by=goal[1,3]+radius*math.sin(angle);face=math.atan2(goal[1,3]-by,goal[0,3]-bx) + for da in np.deg2rad([-12,0,12]): + base=np.eye(4);base[:3,:3]=Rotation.from_euler('z',face+da).as_matrix();base[:3,3]=[bx,by,livebase[2,3]];arm=Arm(robot,names,seedq,base,robot.root+f'/gripper_{hand}_base_link',active);target=goal@invheld;best=None + for seed in [seedq[active],liveq[active]]: + fit=least_squares(lambda x:arm.evaluate(x,target,seed)[0],np.clip(seed,lo,up),jac=lambda x:arm.evaluate(x,target,seed)[1],bounds=(lo,up),max_nfev=110);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());score=pe+.1*re + if best is None or score.003 or best[2]>.03:continue + q=seedq.copy();q[active]=best[3];q[grips]=liveq[grips] + if not checker.check(q,base):continue + travel=float(np.linalg.norm(base[:2,3]-livebase[:2,3]));results.append(dict(score=travel+best[0]-min(gaps,default=0),goal=goal.tolist(),footprint_xy=np.asarray(fp.exterior.coords)[:-1].tolist(),gaps_m=[float(x) for x in gaps],actual_gap_count=item_index,extra_inset_m=float(extra),base=base.tolist(),q=q.tolist(),place_initial20=q[ids].tolist(),position_error_m=best[1],rotation_error_rad=best[2],travel_m=travel)) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.polar_endpoint.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:100]};p=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'goal_count':len(goals),'tested':tested,'clear_endpoint_count':len(results),'top':results[:5]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic.py new file mode 100644 index 0000000000000000000000000000000000000000..98189940d673ec49a6a3574958df2ab03d745419 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Full read-only carry/place/release qualification of polar endpoint seeds.""" +import json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];case_index=int(sys.argv[1]);item_index=int(sys.argv[2]);C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json'));it=j['items'][item_index];cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage);controls=np.load(it['controls_file']);verts=controls['target_local'];ids=[names.index(n) for n in controls['names'].tolist()] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link");ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obs=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +ep=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';seeds=json.load(open(ep))['top'];source_layout=int(j.get('source_layout_id',j['layout_id']));release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json'));attempts=[];accepted=None +for rank,seed in enumerate(seeds[:30]): + q=np.asarray(seed['q'],float);base=np.asarray(seed['base'],float);goal=np.asarray(seed['goal'],float);yaw=float(Rotation.from_matrix(goal[:3,:3]).as_euler('xyz')[2]);slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':seed['footprint_xy'],'tolerance_m':.001};ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':5};checker.deadline=time.monotonic()+1800 + if not checker.check(q,base):attempts.append({'rank':rank,'stage':'destination','detail':checker.last});continue + obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held + try: + rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800);checker.deadline=time.monotonic()+1800;plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obs,pose_check,world_check=checker,release_sweep=rs) + except Exception as e:attempts.append({'rank':rank,'stage':'placement','error':repr(e)});continue + cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) + try:compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) + except Exception as e:attempts.append({'rank':rank,'stage':'carry','error':repr(e)});continue + checker.deadline=time.monotonic()+1800 + if not checker.segment(compact,q,base,base):attempts.append({'rank':rank,'stage':'compact_bridge','detail':checker.last});continue + accepted={'rank':rank,'slot':slot,'destination_xyz':[float(goal[0,3]),float(goal[1,3]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':seed['gaps_m'][:item_index],'future_gaps_m':seed['gaps_m'][item_index:],'extra_inset_m':seed['extra_inset_m'],'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True};attempts.append({'rank':rank,'stage':'passed'});break +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.polar_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'endpoint':str(ep),'checkpoint':str(cp_path)},'accepted':accepted,'attempts':attempts,'native_validation_required':True};p=C/f'CASE{case_index}_ITEM{item_index:02d}_POLAR_FULL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'accepted':accepted,'attempts':attempts},indent=2));raise SystemExit(0 if accepted else 2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py new file mode 100644 index 0000000000000000000000000000000000000000..78e7173ff81db771c2fdabd476054638084fff94 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_polar_full_generic_tail.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Full read-only carry/place/release qualification of polar endpoint seeds.""" +import json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon +W=pathlib.Path(__file__).resolve().parent;R=W.parents[1];case_index=int(sys.argv[1]);item_index=int(sys.argv[2]);C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m +s=json.load(open(C/'STATUS.json'));assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json'));it=j['items'][item_index];cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True);names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage);controls=np.load(it['controls_file']);verts=controls['target_local'];ids=[names.index(n) for n in controls['names'].tolist()] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link");ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet;props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])};checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obs=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]);pv=np.load(j['items'][k]['controls_file'])['target_local'];pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2];obs.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +ep=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_POLAR_ENDPOINT.json';seeds=json.load(open(ep))['top'];source_layout=int(j.get('source_layout_id',j['layout_id']));release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json'));attempts=[];accepted=None +for rank,seed in enumerate(seeds[30:80], start=30): + q=np.asarray(seed['q'],float);base=np.asarray(seed['base'],float);goal=np.asarray(seed['goal'],float);yaw=float(Rotation.from_matrix(goal[:3,:3]).as_euler('xyz')[2]);slot={'xy':goal[:2,3].tolist(),'yaw_rad':yaw,'footprint_xy':seed['footprint_xy'],'tolerance_m':.001};ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':5};checker.deadline=time.monotonic()+1800 + if not checker.check(q,base):attempts.append({'rank':rank,'stage':'destination','detail':checker.last});continue + obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held + try: + rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800);checker.deadline=time.monotonic()+1800;plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obs,pose_check,world_check=checker,release_sweep=rs) + except Exception as e:attempts.append({'rank':rank,'stage':'placement','error':repr(e)});continue + cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) + try:compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) + except Exception as e:attempts.append({'rank':rank,'stage':'carry','error':repr(e)});continue + checker.deadline=time.monotonic()+1800 + if not checker.segment(compact,q,base,base):attempts.append({'rank':rank,'stage':'compact_bridge','detail':checker.last});continue + accepted={'rank':rank,'slot':slot,'destination_xyz':[float(goal[0,3]),float(goal[1,3]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':seed['gaps_m'][:item_index],'future_gaps_m':seed['gaps_m'][item_index:],'extra_inset_m':seed['extra_inset_m'],'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True};attempts.append({'rank':rank,'stage':'passed'});break +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.polar_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'endpoint':str(ep),'checkpoint':str(cp_path)},'accepted':accepted,'attempts':attempts,'native_validation_required':True};p=C/f'CASE{case_index}_ITEM{item_index:02d}_POLAR_FULL_TAIL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'accepted':accepted,'attempts':attempts},indent=2));raise SystemExit(0 if accepted else 2) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_precision_return_controller.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_precision_return_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..42c80da20fab4e8c8cf9e703b4419519ec1bfb66 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_precision_return_controller.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +"""Pure-kinematic verification of tighter controllers on recorded return plans.""" +import json +import math +import pathlib + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +C = W / "cases/layout_01" + + +def angle_delta(target, current): + return math.atan2(math.sin(target - current), math.cos(target - current)) + + +class PrecisionController: + """Same stop-turn-drive law and caps, with pickup-safe terminal precision.""" + def __init__(self, primitive): + self.primitive = primitive + self.speed = 0.0 + self.done = False + + def step(self, current, dt=1 / 60): + goal = np.asarray(self.primitive["goal"], dtype=float) + yaw = float(current[2]) + heading = np.array([math.cos(yaw), math.sin(yaw)]) + if self.primitive["kind"] == "turn": + error = angle_delta(goal[2], yaw) + desired = math.copysign(min(0.25, max(0.00002, 1.5 * abs(error))), error) if abs(error) > 0.000002 else 0.0 + self.speed += float(np.clip(desired - self.speed, -0.3 * dt, 0.3 * dt)) + self.done = abs(error) < 0.000005 and abs(self.speed) < 0.000002 + return np.array([0.0, 0.0, self.speed]) + error = goal[:2] - np.asarray(current[:2]) + along = float(error @ heading) + lateral = float(error @ np.array([-heading[1], heading[0]])) + if abs(lateral) > 0.015: + raise RuntimeError(f"Straight drive lateral tracking error {lateral:.6f} m") + if abs(angle_delta(goal[2], yaw)) > 0.01: + raise RuntimeError("Straight drive heading drift") + desired = math.copysign(min(0.18, max(0.0001, min(1.5 * abs(along), 0.8 * math.sqrt(2 * 0.12 * abs(along))))), along) if abs(along) > 0.00001 else 0.0 + self.speed += float(np.clip(desired - self.speed, -0.12 * dt, 0.12 * dt)) + self.done = abs(along) < 0.00002 and abs(self.speed) < 0.000005 + return np.r_[heading * self.speed, 0.0] + + +def simulate(primitives, start, controller_class=PrecisionController, dt=1 / 60): + pose = np.asarray(start, dtype=float).copy() + rows = [] + for index, primitive in enumerate(primitives): + controller = controller_class(primitive) + for tick in range(90 * 60): + velocity = controller.step(pose, dt) + pose[:2] += velocity[:2] * dt + pose[2] += velocity[2] * dt + if controller.done: + break + else: + raise RuntimeError(f"primitive {index} timed out") + goal = np.asarray(primitive["goal"]) + rows.append({ + "primitive": index, + "kind": primitive["kind"], + "return_stage": primitive.get("return_stage"), + "ticks": tick + 1, + "seconds": (tick + 1) * dt, + "xy_error_m": float(np.linalg.norm(pose[:2] - goal[:2])), + "along_error_m": float((goal[:2] - pose[:2]) @ np.array([math.cos(pose[2]), math.sin(pose[2])])), + "yaw_error_rad": float(abs(angle_delta(goal[2], pose[2]))), + "terminal_speed": float(abs(controller.speed)), + }) + return pose, rows + + +def main(): + recording = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + reports = [] + for item_index in (0, 1): + plan_path = C / f"rollout/return_primitives.item_{item_index:02d}.json" + primitives = json.loads(plan_path.read_text()) + indices = np.flatnonzero((recording["item_index"] == item_index) & (recording["phase"] == f"item_{item_index:02d}_empty_return")) + pose7 = recording["base_pose"][indices[0]] + start = np.r_[pose7[:2], Rotation.from_quat(pose7[3:]).as_euler("xyz")[2]] + final, rows = simulate(primitives, start) + goal = np.asarray(primitives[-1]["goal"]) + reports.append({ + "item": item_index, + "plan": str(plan_path), + "observed_start": start.tolist(), + "simulated_final": final.tolist(), + "final_goal": goal.tolist(), + "final_xy_error_m": float(np.linalg.norm(final[:2] - goal[:2])), + "final_yaw_error_rad": float(abs(angle_delta(goal[2], final[2]))), + "total_seconds": float(sum(row["seconds"] for row in rows)), + "max_endpoint_xy_error_m": max(row["xy_error_m"] for row in rows), + "max_endpoint_yaw_error_rad": max(row["yaw_error_rad"] for row in rows), + "primitives": rows, + }) + # Stress the same final shelf approach with the case-1 observed heading + # residual. A precise preceding turn removes it before straight motion. + item1 = json.loads((C / "rollout/return_primitives.item_01.json").read_text()) + final_drive = item1[-1] + standoff = np.asarray(item1[-2]["goal"], dtype=float) + perturbed = standoff.copy() + perturbed[2] += 0.0008466414784971121 + sequence = [{"kind": "turn", "goal": standoff.tolist(), "return_stage": "precision_preapproach"}, final_drive] + stress_final, stress_rows = simulate(sequence, perturbed) + stress_goal = np.asarray(final_drive["goal"]) + result = { + "schema": "multi_item.precision_return_controller_probe.v1", + "read_only": True, + "controller": { + "turn_deadband_rad": 0.000002, + "turn_done_error_rad": 0.000005, + "turn_done_speed_rad_s": 0.000002, + "turn_min_speed_rad_s": 0.00002, + "drive_deadband_m": 0.00001, + "drive_done_error_m": 0.00002, + "drive_done_speed_m_s": 0.000005, + "drive_min_speed_m_s": 0.0001, + "speed_and_acceleration_caps_unchanged": True, + }, + "recorded_plan_exact_kinematics": reports, + "heading_residual_stress": { + "initial_yaw_error_rad": 0.0008466414784971121, + "final_xy_error_m": float(np.linalg.norm(stress_final[:2] - stress_goal[:2])), + "final_yaw_error_rad": float(abs(angle_delta(stress_goal[2], stress_final[2]))), + "seconds": float(sum(row["seconds"] for row in stress_rows)), + "primitives": stress_rows, + }, + "recommended_integration": "Use PrecisionController only for primitives carrying return_stage. Keep the same collision-screened plan and stop-turn-drive primitives. Require a final measured gate <=0.0005 m and <=0.0001 rad before starting source pickup; otherwise freeze at a planning boundary.", + "limits": "Exact-kinematic verification does not prove native wheel/contact tracking. Fresh native physics must confirm the measured terminal gate.", + } + result["passed"] = bool( + all(report["final_xy_error_m"] <= 0.0001 and report["final_yaw_error_rad"] <= 0.00001 and all(row["seconds"] < 90 for row in report["primitives"]) for report in reports) + and result["heading_residual_stress"]["final_xy_error_m"] <= 0.0001 + and result["heading_residual_stress"]["final_yaw_error_rad"] <= 0.00001 + ) + out = W / "PRECISION_RETURN_CONTROLLER_PROPOSAL.json" + out.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if not result["passed"]: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..6a761547d79fb4ba09080c4a017f8e933ae14dda --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_left_regression.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Read-only left-hand regression against layout10 item02.""" +import json +import os +import pathlib +import sys + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from native_planner import build # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + case = W / "cases/layout_10" + job = json.loads((case / "job.json").read_text()) + item = job["items"][2] + data = np.load(case / "rollout/checkpoint_60hz.npz", allow_pickle=True) + phase = data["phase"].astype(str) + release_rows = np.flatnonzero(phase == "item_02_place_release") + retreat_rows = np.flatnonzero(phase == "item_02_release_retreat") + if not len(release_rows) or not len(retreat_rows): + raise RuntimeError("layout10 release evidence is incomplete") + landing_row = int(release_rows[0] - 1) + retreat_row = int(retreat_rows[-1]) + names = data["dof_names"].tolist() + target_index = data["target_paths"].tolist().index(item["target"]) + target_pose = matrix(data["target_pose_all"][landing_row, target_index]) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(data["rigid_body_paths"], data["prop_pose"][landing_row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + controls = np.load(item["controls_file"]) + checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=build(stage), + deadline_s=1800, + ) + passed, report = checker.check( + data["q"][landing_row], + data["q"][retreat_row], + matrix(data["base_pose"][landing_row]), + target_pose, + ) + result = { + "schema": "ziche.release_sweep_left_regression.v1", + "passed": bool(passed), + "expected": "CLEAR", + "observed": report, + "historical_case": 10, + "item_index": 2, + "landing_tick": landing_row + 1, + "retreat_end_tick": retreat_row + 1, + "hand": "l", + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_LEFT_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if not passed: + raise SystemExit("release sweep rejected the left-hand clear example") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py new file mode 100644 index 0000000000000000000000000000000000000000..38375b702410a94fb647f9821d9b7201002596c8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_negative.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Read-only negative regression against layout10's known Coke strike.""" +import json +import os +import pathlib +import sys + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from native_planner import build # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + case = W / "cases/layout_10" + job = json.loads((case / "job.json").read_text()) + item = job["items"][1] + data = np.load(case / "rollout/checkpoint_60hz.npz", allow_pickle=True) + phase = data["phase"].astype(str) + release_rows = np.flatnonzero(phase == "item_01_place_release") + retreat_rows = np.flatnonzero(phase == "item_01_release_retreat") + if not len(release_rows) or not len(retreat_rows): + raise RuntimeError("layout10 release evidence is incomplete") + landing_row = int(release_rows[0] - 1) + retreat_row = int(retreat_rows[-1]) + names = data["dof_names"].tolist() + target_index = data["target_paths"].tolist().index(item["target"]) + target_pose = matrix(data["target_pose_all"][landing_row, target_index]) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(data["rigid_body_paths"], data["prop_pose"][landing_row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + controls = np.load(item["controls_file"]) + checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=build(stage), + deadline_s=1800, + ) + passed, report = checker.check( + data["q"][landing_row], + data["q"][retreat_row], + matrix(data["base_pose"][landing_row]), + target_pose, + ) + result = { + "schema": "ziche.release_sweep_negative_regression.v1", + "passed": not passed, + "expected": "BLOCKED", + "observed": report, + "historical_case": 10, + "item_index": 1, + "landing_tick": landing_row + 1, + "retreat_end_tick": retreat_row + 1, + "known_native_contact": "gripper_r_inner_link4 versus previously placed coke_c3", + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_NEGATIVE_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2)) + if passed: + raise SystemExit("release sweep failed to reject the known layout10 strike") + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..6936bf3e4c6836228a050d8d882d862417887ff8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_release_sweep_regression.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Read-only regression of ReleaseSweep against the qualified layout02 repair.""" +import hashlib +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 +from release_sweep import ReleaseSweep # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def main(): + case = W / "cases/layout_02" + proposal_path = W / "diagnostics/LAYOUT02_ITEM01_NOMINAL_STATION_FULL_PROPOSAL.json" + proposal = json.loads(proposal_path.read_text()) + accepted = proposal["accepted"] + job_path = case / "job.json" + job = json.loads(job_path.read_text()) + item = job["items"][1] + checkpoint_path = W / "diagnostics/release_sweep_case2_checkpoint_tick17975_plus.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + row = int(proposal["status_at_probe"]["control_ticks"]) - 1 + if row < 0 or row >= len(checkpoint["q"]): + raise RuntimeError("qualified boundary row is absent from the checkpoint") + names = checkpoint["dof_names"].tolist() + q = checkpoint["q"][row].astype(float) + controls = np.load(item["controls_file"]) + ids = [names.index(name) for name in controls["names"].tolist()] + q[ids] = np.asarray(accepted["place_initial20"], dtype=float) + # The qualifying probe preserves the measured passive gripper state. + q[[i for i, name in enumerate(names) if "gripper_" in name]] = checkpoint["q"][row, [i for i, name in enumerate(names) if "gripper_" in name]] + base = np.asarray(accepted["place_base"], dtype=float) + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][row]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + pose_check = build(stage) + hand_link = robot.root + f"/gripper_{item['hand']}_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(f"/World/Robot/gripper_{item['hand']}_base_link") + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + live_target = matrix(checkpoint["target_pose_all"][row, target_index]) + held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][row, hand_index])) @ live_target + object_pose = robot.fk(names, q, base)[hand_link] @ held + world_check = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, prop_poses) + world_check.deadline = time.monotonic() + 1800 + release_checker = ReleaseSweep( + stage, + robot, + names, + item["hand"], + item["target"], + controls["target_local"], + prop_poses, + item["place_source"], + pose_check=pose_check, + deadline_s=1800, + ) + completed_item = job["items"][0] + completed_local = np.load(completed_item["controls_file"])["target_local"] + completed_pose = matrix(checkpoint["target_pose_all"][row, 0]) + completed_world = (np.c_[completed_local, np.ones(len(completed_local))] @ completed_pose.T)[:, :2] + obstacles = [Polygon(completed_world[ConvexHull(completed_world).vertices])] + item_job = {**job, **item, "placement_slot": accepted["slot"], "place_base": base.tolist(), "require_release_sweep": True} + item_job["reserved_tray_footprints"] = [job["items"][2]["placement_slot"]["footprint_xy"]] + started = time.monotonic() + planned, retreat, placement_report = solve( + robot, + names, + q, + base, + object_pose, + controls["target_local"], + item_job, + obstacles, + pose_check, + world_check=world_check, + release_sweep=release_checker, + ) + report = placement_report["release_sweep"] + passed = report["status"] == "CLEAR" + result = { + "schema": "ziche.release_sweep_regression.v1", + "passed": bool(passed), + "case": 2, + "item_index": 1, + "qualified_boundary_tick": row + 1, + "source": { + "proposal": str(proposal_path), + "proposal_sha256": sha(proposal_path), + "job": str(job_path), + "job_sha256": sha(job_path), + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": sha(checkpoint_path), + "release_sweep_sha256": sha(W / "release_sweep.py"), + "placement_sha256": sha(W / "placement.py"), + }, + "elapsed_s": time.monotonic() - started, + "placement_plan_frames": int(len(planned)), + "placement_candidate": int(placement_report["candidate"]), + "placement_goal": placement_report["goal"], + "retreat_q": retreat.tolist(), + "report": report, + "no_simulation_or_pose_mutation": True, + } + output = W / "diagnostics/RELEASE_SWEEP_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result, indent=2), flush=True) + if not passed: + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_seed_full_generic.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_seed_full_generic.py new file mode 100644 index 0000000000000000000000000000000000000000..b63c55aae8adb715d2311a00deae64cee61661b8 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_seed_full_generic.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +"""Qualify an immutable prior place seed against a current frozen boundary.""" +import hashlib,json,math,os,pathlib,sys,time +import numpy as np +from scipy.spatial import ConvexHull +from scipy.spatial.transform import Rotation +from shapely.geometry import Polygon + +W=pathlib.Path(__file__).resolve().parent; R=W.parents[1] +case_index=int(sys.argv[1]); item_index=int(sys.argv[2]); seed_path=pathlib.Path(sys.argv[3]).resolve() +C=W/f"cases/layout_{case_index:02d}" +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from carry_transition import Clearance,plan as plan_carry +from native_planner import build +from placement import solve +from release_sweep import ReleaseSweep + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +s=json.load(open(C/'STATUS.json')) +assert s['status']=='WAITING_FOR_PLANNING_REPAIR' and s['item_index']==item_index +j=json.load(open(C/'job.json')); it=j['items'][item_index] +seed_doc=json.load(open(seed_path)); seed=seed_doc.get('accepted') or seed_doc.get('proposal') +if not isinstance(seed,dict): raise ValueError('seed has no accepted/proposal object') +cp_path=C/'rollout/checkpoint_60hz.npz';cp=np.load(cp_path,allow_pickle=True) +names=cp['dof_names'].tolist();liveq=cp['q'][-1].astype(float);livebase=mat(cp['base_pose'][-1]) +controls=np.load(it['controls_file']); ids=[names.index(n) for n in controls['names'].tolist()] +q=liveq.copy(); q[ids]=np.asarray(seed['place_initial20'],float) +# The live gripper command is evidence-bearing and must survive a posture seed. +for k,n in enumerate(names): + if 'gripper_' in n:q[k]=liveq[k] +base=np.asarray(seed['place_base'],float).copy();base[2,3]=livebase[2,3] +hi=cp['robot_link_paths'].tolist().index(f"/World/Robot/gripper_{it['hand']}_base_link") +ti=cp['target_paths'].tolist().index(it['target']);livet=mat(cp['target_pose_all'][-1,ti]) +held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@livet +props={str(p):mat(v) for p,v in zip(cp['rigid_body_paths'],cp['prop_pose'][-1])} +stage=Usd.Stage.Open(j['scene']);robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) +verts=controls['target_local']; checker=Clearance(stage,robot,names,it['hand'],it['target'],verts,held,props) +obstacles=[] +for k in range(item_index): + pm=mat(cp['target_pose_all'][-1,k]); pv=np.load(j['items'][k]['controls_file'])['target_local']; pts=(np.c_[pv,np.ones(len(pv))]@pm.T)[:,:2] + obstacles.append(Polygon(pts[ConvexHull(pts).vertices])) +reserved=[x['placement_slot']['footprint_xy'] for x in j['items'][item_index+1:]] +slot=json.loads(json.dumps(seed['slot'])); fp=Polygon(slot['footprint_xy']) +gaps=[fp.distance(x) for x in obstacles]+[fp.distance(Polygon(x)) for x in reserved] +floor=Polygon(j['tray_polygon_xy']).buffer(-.008); inside=bool(floor.covers(fp)); inset=float(fp.distance(floor.boundary)) if inside else -1.0 +if not inside or (gaps and min(gaps)<=.012):raise RuntimeError(f'seed geometry invalid: inside={inside} gaps={gaps}') +ij={**j,**it,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':reserved,'require_release_sweep':True,'placement_candidate_budget':40} +checker.deadline=time.monotonic()+1800 +if not checker.check(q,base):raise RuntimeError('destination blocked: '+json.dumps(checker.last,default=str)) +obj=robot.fk(names,q,base)[robot.root+f"/gripper_{it['hand']}_base_link"]@held +source_layout=int(j.get('source_layout_id',j['layout_id'])) +release=json.load(open(pathlib.Path(j['layout']).parents[1]/'audits_release'/f'{source_layout:02d}.json')) +rs=ReleaseSweep(stage,robot,names,it['hand'],it['target'],verts,props,it['place_source'],pose_check=pose_check,deadline_s=1800) +checker.deadline=time.monotonic()+1800 +plan,retreat,report=solve(robot,names,q,base,obj,verts,ij,obstacles,pose_check,world_check=checker,release_sweep=rs) +cj={**ij,'carry_policy':'precision_body_route_v2','carry_exit_candidates':[.25,.45,.15,.05]};cj.pop('carry_route_seed',None) +compact,prims,cr=plan_carry(stage,robot,names,liveq,livebase,it['hand'],it['target'],verts,livet,props,cj,release) +checker.deadline=time.monotonic()+1800 +if not checker.segment(compact,q,base,base):raise RuntimeError('compact bridge blocked: '+json.dumps(checker.last,default=str)) +accepted={'slot':slot,'destination_xyz':[float(slot['xy'][0]),float(slot['xy'][1]),float(j['tray_floor_z'])],'place_base':base.tolist(),'place_initial20':q[ids].tolist(),'geometry':{'actual_completed_gaps_m':gaps[:item_index],'future_gaps_m':gaps[item_index:],'extra_inset_m':inset,'tray_inset_m':.008},'placement_plan_frames':len(plan),'placement_report':report,'retreat_q':retreat.tolist(),'reposition':{'compact_q':compact.tolist(),'primitives':prims,'report':cr},'compact_to_exact_bridge_clearance':True,'full_world_checks':True} +current=json.load(open(C/'STATUS.json'));assert current.get('status')==s.get('status') and current.get('control_ticks')==s.get('control_ticks') +out={'schema':'multi_item.current_seed_full_place.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_at_probe':s,'evidence':{'seed':str(seed_path),'seed_sha256':hashlib.sha256(seed_path.read_bytes()).hexdigest(),'checkpoint':str(cp_path)},'accepted':accepted,'native_validation_required':True} +p=C/f'CASE{case_index}_ITEM{item_index:02d}_SEED_FULL_PROPOSAL.json';p.write_text(json.dumps(out,indent=2)+'\n') +print(json.dumps({'out':str(p),'accepted':accepted},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_slots.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_slots.py new file mode 100644 index 0000000000000000000000000000000000000000..270f339a7561d50098fcb7256cf0dd3b9eafb4ea --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_slots.py @@ -0,0 +1,32 @@ +"""Offline reachability proposal using actual accepted pickup grasps; not acceptance.""" +import sys,os,json,time +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from shapely.geometry import Polygon +case=W/'cases'/f'layout_{int(sys.argv[1]):02d}';job=json.loads((case/'job.json').read_text());stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[];selfcheck=build(stage) +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +result=[];obstacles=[] +for k,item in enumerate(job['items']): + data=np.load(item['controls_file']);source=Path(item['pick_source']);z=np.load(source/'recording_20hz.npz');names=z['robot_dof_names'].tolist();ids=[names.index(n) for n in data['names']] + ti=z['rigid_body_paths'].tolist().index(item['target']);hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link') + held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]) + q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:] + base=np.array(item['place_base']);obj=robot.fk(names,q,base)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + t=time.monotonic() + try: + plan,retreat,report=solve(robot,names,q,base,obj,data['target_local'],ij,obstacles,selfcheck) + row=dict(item=k,product=item['product'],passed=True,report=report) + except Exception as exc:row=dict(item=k,product=item['product'],passed=False,error=str(exc)) + row['elapsed']=time.monotonic()-t;result.append(row);print(json.dumps(row,default=lambda a:a.tolist()),flush=True) + obstacles.append(Polygon(item['placement_slot']['footprint_xy'])) + (case/'SLOT_REACHABILITY_PROPOSAL.json').write_text(json.dumps(dict(source='accepted_pickup_grasp_at_proposed_place_initial',native_validation_required=True,items=result),indent=2,default=lambda a:a.tolist())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_ik_grid.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_ik_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..ef0fed349a88896fc28d724cd0a8568b051cac42 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_ik_grid.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Read-only grounded station/landing IK grid for a frozen place boundary.""" +import json, math, os, sys +from pathlib import Path +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W=Path(__file__).resolve().parent; R=W.parents[1] +case_index=int(sys.argv[1]); item_index=int(sys.argv[2]); C=W/f'cases/layout_{case_index:02d}' +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'work/place_training_tray_20260918'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +def mat(p): + m=np.eye(4);m[:3,3]=p[:3];m[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return m + +status=json.loads((C/'STATUS.json').read_text());assert status['status']=='WAITING_FOR_PLANNING_REPAIR' and status['item_index']==item_index +job=json.loads((C/'job.json').read_text());item=job['items'][item_index];hand=item['hand'];cp=np.load(C/'rollout/checkpoint_60hz.npz',allow_pickle=True) +names=cp['dof_names'].tolist();live_q=cp['q'][-1].astype(float);live_base=mat(cp['base_pose'][-1]);stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] +hi=cp['robot_link_paths'].tolist().index(f'/World/Robot/gripper_{hand}_base_link');ti=cp['target_paths'].tolist().index(item['target']);held=np.linalg.inv(mat(cp['robot_link_pose'][-1,hi]))@mat(cp['target_pose_all'][-1,ti]);invheld=np.linalg.inv(held) +exact_path=C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json' +if not exact_path.is_file():exact_path=R/f'work/shelf_to_table_multi10_20261005/cases/layout_{case_index:02d}/EXACT_SOURCE_TRANSFER_PROPOSAL.json' +exact=json.loads(exact_path.read_text())['items'][item_index];prior=np.load(exact['prior_controls']);controls=np.load(item['controls_file']);ids=[names.index(n) for n in controls['names'].tolist()];q0=live_q.copy();q0[ids]=prior['place_initial'];q0[[i for i,n in enumerate(names) if 'gripper_' in n]]=live_q[[i for i,n in enumerate(names) if 'gripper_' in n]] +center=np.asarray(item['placement_slot']['xy'],float);desired=float(item['placement_slot']['yaw_rad']);base0=np.asarray(exact['transferred_accepted_base'],float);base0[2,3]=live_base[2,3] +side='l' if hand=='l' else 'r'; prefix=2 if hand=='l' else 6; link=robot.root+f'/gripper_{side}_base_link'; active=[names.index(f'idx{prefix}{k}_arm_{side}_joint{k}') for k in range(1,8)]; jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')};lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4;hi_lim=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4 +off=0 if hand=='l' else 7;lo=np.maximum(lo,LOWER[off:off+7]+1e-4);hi_lim=np.minimum(hi_lim,UPPER[off:off+7]-1e-4) +verts=controls['target_local'];yaw_options=[desired,desired+math.pi,desired+math.pi/2,desired-math.pi/2,Rotation.from_matrix(mat(cp['target_pose_all'][-1,ti])[:3,:3]).as_euler('xyz')[2]] +results=[];tested=0 +for dx in np.arange(-.12,.121,.03): + for dy in np.arange(-.12,.121,.03): + for da in np.deg2rad(np.arange(-12.1,12.2,3.0)): + yaw0=math.atan2(base0[1,0],base0[0,0])+da;base=base0.copy();base[:3,:3]=Rotation.from_euler('z',yaw0).as_matrix();base[0,3]+=dx;base[1,3]+=dy;arm=Arm(robot,names,q0,base,link,active) + for gyaw in yaw_options: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',gyaw).as_matrix();goal[:2,3]=center;goal[2,3]=job['tray_floor_z']+.002-(verts@goal[:3,:3].T)[:,2].min();target=goal@invheld;prev=q0[active].copy();fit=least_squares(lambda x:arm.evaluate(x,target,prev)[0],np.clip(prev,lo,hi_lim),jac=lambda x:arm.evaluate(x,target,prev)[1],bounds=(lo,hi_lim),max_nfev=100);fk=arm.forward(fit.x)[0];pe=float(np.linalg.norm(fk[:3,3]-target[:3,3]));re=float(Rotation.from_matrix(target[:3,:3].T@fk[:3,:3]).magnitude());tested+=1 + if pe<.004 and re<.04:results.append(dict(score=pe+0.1*re,position_error_m=pe,rotation_error_rad=re,base=base.tolist(),goal=goal.tolist(),q7=fit.x.tolist(),offset=[float(dx),float(dy),float(da)])) +results.sort(key=lambda x:x['score']);out={'schema':'multi_item.grounded_station_ik_grid.v1','read_only':True,'layout_id':case_index,'item_index':item_index,'status_control_ticks':status['control_ticks'],'tested':tested,'accepted_count':len(results),'top':results[:80]};p=W/f'diagnostics/LAYOUT{case_index:02d}_ITEM{item_index:02d}_STATION_IK_GRID.json';p.write_text(json.dumps(out,indent=2)+'\n');print(json.dumps({'out':str(p),'tested':tested,'accepted_count':len(results),'top':results[:3]},indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_slots.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_slots.py new file mode 100644 index 0000000000000000000000000000000000000000..6adff0935f5310ece0aa392b329fc369a6b4b455 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_station_slots.py @@ -0,0 +1,69 @@ +"""Read-only station/slot reachability search using accepted held grasps.""" +import sys,os,json,time,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +from scipy.optimize import least_squares +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd +from collision_scene import Robot,ROBOT +from placement import solve +from native_planner import build +from geometry_transfer import registration +from shapely.geometry import Polygon +from ik_solver import Arm +from motion_limits import LOWER,UPPER + +case=W/'cases'/f'layout_{int(sys.argv[1]):02d}'; job=json.loads((case/'job.json').read_text()); stage=Usd.Stage.Open(job['scene']) +room=Usd.Stage.Open(job['layout']); robot=Robot(ROBOT); robot.bodies=[]; selfcheck=build(stage) +def matrix(xyz,wxyz): + m=np.eye(4);m[:3,3]=xyz;m[:3,:3]=Rotation.from_quat(np.asarray(wxyz)[[1,2,3,0]]).as_matrix();return m +def pose(spec): + m=np.eye(4);m[:3,3]=spec['robot']['base_xyz_m'];m[:3,:3]=Rotation.from_quat(np.asarray(spec['robot']['base_wxyz'])[[1,2,3,0]]).as_matrix();return m + +results=[]; obstacles=[] +for k,item in enumerate(job['items']): + data=np.load(item['controls_file']); pick=Path(item['pick_source']); z=np.load(pick/'recording_20hz.npz'); names=z['robot_dof_names'].tolist(); ids=[names.index(n) for n in data['names']] + ti=z['rigid_body_paths'].tolist().index(item['target']); hi=z['robot_link_paths'].tolist().index('/World/Robot/gripper_'+item['hand']+'_base_link') + held=np.linalg.inv(matrix(z['robot_link_xyz_m'][-1,hi],z['robot_link_wxyz'][-1,hi]))@matrix(z['prop_xyz_m'][-1,ti],z['prop_wxyz'][-1,ti]) + q=z['robot_joint_position_rad_or_m'][-1].copy();q[ids]=data['place_initial'];q[ids[-2:]]=data['pick'][-1,-2:] + place=Path(item['place_source']); ps=json.loads((place/'episode.json').read_text()); pstage=Usd.Stage.Open(str(place/'scene.usda')) + delta,_=registration(pstage,room,'/World/Assets/CoffeeTable/Mesh'); demonstrated=delta@np.r_[ps['destination_xy_m'],ps['tray']['floor_z_m'],1.] + base=np.asarray(item['place_base'],float); shift=np.asarray(item['placement_slot']['xy'])-demonstrated[:2]; nominal=base.copy() + if item.get('station_alignment_policy')!='registered_source_destination_to_reserved_slot_v1': nominal[:2,3]+=shift + # Analytical translation preserves the accepted hand/object/table relationship. Bounded offsets cover registration and slot tolerance. + offsets=[(0,0),(-.10,0),(-.06,0),(-.03,0),(.03,0),(.06,0),(.10,0),(0,.03),(0,-.03),(0,.06),(0,-.06)] + attempts=[]; accepted=None; started=time.monotonic(); stations=[] + # The aligned, floor-safe nominal station is the strongest source-derived proposal; test it before screening alternatives. + candidate=nominal.copy();candidate[2,3]=float(np.asarray(job['initial_base'])[2,3]);obj=robot.fk(names,q,candidate)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'place_base':candidate.tolist(),'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + try: + plan,retreat,report=solve(robot,names,q,candidate,obj,data['target_local'],ij,obstacles,selfcheck);accepted={'place_base':candidate.tolist(),'station_shift_xy':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':0,'endpoint_score':None,'report':report,'plan_frames':len(plan)};attempts.append({'passed':True,'shift':accepted['station_shift_xy'],'yaw_offset_deg':0,'direct_nominal':True}) + except Exception as exc: attempts.append({'passed':False,'shift':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':0,'direct_nominal':True,'error':str(exc)[:500]}) + link=robot.root+'/gripper_'+item['hand']+'_base_link'; active=[names.index(f'idx{2 if item["hand"]=="l" else 6}{a}_arm_{item["hand"]}_joint{a}') for a in range(1,8)] + jp={p.GetName():p for p in robot.stage.Traverse() if p.GetAttribute('physics:lowerLimit')}; lo=np.array([jp[names[i]].GetAttribute('physics:lowerLimit').Get() for i in active])*np.pi/180+1e-4; hi=np.array([jp[names[i]].GetAttribute('physics:upperLimit').Get() for i in active])*np.pi/180-1e-4; off=0 if item['hand']=='l' else 7;lo=np.maximum(lo,LOWER[off:off+7]+1e-4);hi=np.minimum(hi,UPPER[off:off+7]-1e-4) + vertices=data['target_local']; invheld=np.linalg.inv(held); slot=item['placement_slot']; thetas=[slot['yaw_rad'],slot['yaw_rad']+math.pi,slot['yaw_rad']+math.pi/2,slot['yaw_rad']-math.pi/2] + for yaw_deg in ([] if accepted else [0,-5,5,-10,10]): + for dx,dy in offsets: + candidate=nominal.copy();candidate[:2,3]+=np.array([dx,dy]);candidate[2,3]=float(np.asarray(job['initial_base'])[2,3]) + if yaw_deg:candidate[:3,:3]=Rotation.from_euler('z',math.radians(yaw_deg)).as_matrix()@candidate[:3,:3] + if math.hypot(dx,dy)>.15+1e-9:continue + fast=Arm(robot,names,q,candidate,link,active); best=1e9 + for theta in thetas: + goal=np.eye(4);goal[:3,:3]=Rotation.from_euler('z',theta).as_matrix();goal[:2,3]=slot['xy'];goal[2,3]=job['tray_floor_z']+.002-(vertices@goal[:3,:3].T)[:,2].min();g=goal@invheld;prev=q[active] + fit=least_squares(lambda x:fast.evaluate(x,g,prev)[0],np.clip(prev,lo,hi),jac=lambda x:fast.evaluate(x,g,prev)[1],bounds=(lo,hi),max_nfev=70); f=fast.forward(fit.x)[0];pe=np.linalg.norm(f[:3,3]-g[:3,3]);re=Rotation.from_matrix(g[:3,:3].T@f[:3,:3]).magnitude();best=min(best,pe+0.2*re) + stations.append((best,yaw_deg,dx,dy,candidate)) + for endpoint_score,yaw_deg,dx,dy,candidate in sorted(stations,key=lambda x:x[0])[:8]: + obj=robot.fk(names,q,candidate)[robot.root+'/gripper_'+item['hand']+'_base_link']@held + ij={**job,**item,'place_base':candidate.tolist(),'reserved_tray_footprints':[it['placement_slot']['footprint_xy'] for it in job['items'][k+1:]]} + try: + plan,retreat,report=solve(robot,names,q,candidate,obj,data['target_local'],ij,obstacles,selfcheck) + accepted={'place_base':candidate.tolist(),'station_shift_xy':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score,'report':report,'plan_frames':len(plan)}; attempts.append({'passed':True,'shift':accepted['station_shift_xy'],'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score});break + except Exception as exc: attempts.append({'passed':False,'shift':(candidate[:2,3]-base[:2,3]).tolist(),'yaw_offset_deg':yaw_deg,'endpoint_score':endpoint_score,'error':str(exc)[:500]}) + row={'item':k,'product':item['product'],'passed':accepted is not None,'source_destination_registered_xy':demonstrated[:2].tolist(),'slot_xy':item['placement_slot']['xy'],'analytic_shift_xy':shift.tolist(),'accepted':accepted,'attempts':attempts,'elapsed_s':time.monotonic()-started};results.append(row);print(json.dumps(row,default=lambda a:a.tolist()),flush=True) + obstacles.append(Polygon(item['placement_slot']['footprint_xy'])) + if os.environ.get('PROBE_FIRST_ONLY')=='1': break +out={'schema':'multi_item.station_slot_reachability.v1','case':int(sys.argv[1]),'read_only_job':True,'method':'accepted held transform plus source destination aligned station; bounded 0.15m translation and 10deg yaw search','all_passed':all(x['passed'] for x in results),'items':results,'native_validation_required':True} +(case/'STATION_SLOT_REACHABILITY_PROPOSAL.json').write_text(json.dumps(out,indent=2,default=lambda a:a.tolist())+'\n') diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3f44bed09b9ebfd3a619ee071ec3ba82cabf18 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/probe_zbar_rebased_pick.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +"""Read-only IK rebase of Zbar675 hand waypoints for the measured case-1 base.""" +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") +from pxr import Usd # noqa: E402 +from collision_scene import Robot, ROBOT # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from geometry_transfer import board_path, registration # noqa: E402 +from ik_solver import Arm # noqa: E402 +from motion_limits import LOWER, UPPER # noqa: E402 + +C = W / "cases/layout_01" +SRC = R / "data/shelf_pick_visible360_lerobot_20260916/sim_info/episode_000675" + + +def pose_matrix(pose, wxyz=False): + result = np.eye(4) + result[:3, 3] = pose[:3] + quat = np.asarray(pose[3:]) + if wxyz: + quat = quat[[1, 2, 3, 0]] + result[:3, :3] = Rotation.from_quat(quat).as_matrix() + return result + + +def main(): + job = json.loads((C / "job.json").read_text()) + item = job["items"][2] + controls = np.load(item["controls_file"]) + failed = np.load(C / "rollout/recording_60hz.npz", allow_pickle=True) + source = np.load(SRC / "recording_20hz.npz", allow_pickle=True) + full_state = np.load(C / "rollout/full_state.npz", allow_pickle=True) + names = source["robot_dof_names"].tolist() + action_names = controls["names"].tolist() + action_ids = [names.index(name) for name in action_names] + active = [names.index(f"idx2{i}_arm_l_joint{i}") for i in range(1, 8)] + hand_path = "/World/Robot/gripper_l_base_link" + source_hand_index = source["robot_link_paths"].tolist().index(hand_path) + target_index = source["rigid_body_paths"].tolist().index(item["target"]) + failed_pick = np.flatnonzero((failed["item_index"] == 2) & (failed["phase"] == "item_02_shelf_pick")) + actual_base = pose_matrix(failed["base_pose"][failed_pick[0]]) + desired_base = np.asarray(item["pick_base"], dtype=float) + + stage = Usd.Stage.Open(job["scene"]) + source_stage = Usd.Stage.Open(str(SRC / "scene.usda")) + room = Usd.Stage.Open(job["layout"]) + shelf_delta, shelf_reg = registration(source_stage, room, board_path(source_stage)) + robot = Robot(ROBOT) + robot.bodies = [] + joint_prims = {prim.GetName(): prim for prim in robot.stage.Traverse() if prim.GetAttribute("physics:lowerLimit")} + lo = np.array([joint_prims[names[i]].GetAttribute("physics:lowerLimit").Get() for i in active]) * np.pi / 180 + 1e-4 + hi = np.array([joint_prims[names[i]].GetAttribute("physics:upperLimit").Get() for i in active]) * np.pi / 180 - 1e-4 + lo = np.maximum(lo, LOWER[:7] + 1e-4) + hi = np.minimum(hi, UPPER[:7] - 1e-4) + + prop_poses = {} + for path, xyz, wxyz in zip(full_state["rigid_body_paths"], full_state["prop_xyz_m"][-1], full_state["prop_wxyz"][-1]): + prop_poses[str(path)] = pose_matrix(np.r_[xyz, wxyz], wxyz=True) + # Clearance builds the real released-room environment and excludes the task target. + initial_q = source["robot_joint_position_rad_or_m"][0].copy() + target_vertices = controls["target_local"] + checker = Clearance(stage, robot, names, "l", item["target"], target_vertices, np.eye(4), prop_poses) + checker.deadline = time.monotonic() + 1800 + + corrected = [] + action20 = [] + position_errors = [] + rotation_errors = [] + correction_norms = [] + world_failures = [] + previous = source["robot_joint_position_rad_or_m"][0, active].copy() + start_z = float(source["prop_xyz_m"][0, target_index, 2]) + for frame in range(len(source["robot_joint_position_rad_or_m"])): + q_source = source["robot_joint_position_rad_or_m"][frame].copy() + desired_hand = shelf_delta @ pose_matrix(np.r_[source["robot_link_xyz_m"][frame, source_hand_index], source["robot_link_wxyz"][frame, source_hand_index]], wxyz=True) + arm = Arm(robot, names, q_source, actual_base, robot.root + "/gripper_l_base_link", active) + fit = least_squares(lambda x: arm.evaluate(x, desired_hand, previous)[0], np.clip(previous, lo, hi), jac=lambda x: arm.evaluate(x, desired_hand, previous)[1], bounds=(lo, hi), max_nfev=80) + q = q_source.copy() + q[active] = fit.x + actual_hand = arm.forward(fit.x)[0] + pe = float(np.linalg.norm(actual_hand[:3, 3] - desired_hand[:3, 3])) + re = float(Rotation.from_matrix(desired_hand[:3, :3].T @ actual_hand[:3, :3]).magnitude()) + position_errors.append(pe) + rotation_errors.append(re) + correction_norms.append(float(np.linalg.norm(q[active] - q_source[active]))) + if pe > 0.0005 or re > 0.005: + raise RuntimeError(f"IK fidelity failed frame {frame}: {pe}, {re}") + + # Robot-only full-body/world check. The target is intentionally omitted + # from environment collision because finger contact is the task. + local = robot.fk(names, q, np.eye(4)) + okay, detail = checker.self_check(local, robot.root) + if not okay: + world_failures.append({"frame": frame, "kind": "self", "detail": detail}) + break + fk = {name: actual_base @ transform for name, transform in local.items()} + for body in checker.self_check.bodies: + body.update(fk[body.name.replace("/World/Robot", robot.root, 1)]) + collision = None + for body in checker.self_check.bodies: + indices = np.flatnonzero(np.all(checker.high >= body.lo, axis=1) & np.all(checker.low <= body.hi, axis=1)) + for environment_index in indices: + other = checker.environment[environment_index] + if checker.contact(body, other, containment=True) is not None: + collision = {"robot": body.name, "obstacle": other.name} + break + if collision: + break + if collision: + world_failures.append({"frame": frame, "kind": "environment", "detail": collision}) + break + + # Once lifted 5 mm, also screen the source-derived target path against + # the environment; shelf support contact before lift is expected. + target_world = shelf_delta @ pose_matrix(np.r_[source["prop_xyz_m"][frame, target_index], source["prop_wxyz"][frame, target_index]], wxyz=True) + if source["prop_xyz_m"][frame, target_index, 2] > start_z + 0.005: + checker.target.update(target_world) + indices = np.flatnonzero(np.all(checker.high >= checker.target.lo, axis=1) & np.all(checker.low <= checker.target.hi, axis=1)) + for environment_index in indices: + other = checker.environment[environment_index] + if checker.contact(checker.target, other, containment=True) is not None: + world_failures.append({"frame": frame, "kind": "target_environment", "detail": {"target": item["target"], "obstacle": other.name}}) + break + if world_failures: + break + corrected.append(q) + action = controls["pick"][frame].copy() + for joint_index, source_index in enumerate(active): + action[action_names.index(names[source_index])] = q[source_index] + action20.append(action) + previous = fit.x + + out_dir = C / "diagnostics" + out_dir.mkdir(exist_ok=True) + report = { + "schema": "multi_item.zbar675_measured_base_rebase.v1", + "read_only": True, + "source": str(SRC), + "failed_case": 1, + "desired_base": desired_base.tolist(), + "measured_base": actual_base.tolist(), + "shelf_registration": shelf_reg, + "frames_requested": int(len(source["robot_joint_position_rad_or_m"])), + "frames_solved": len(corrected), + "max_hand_position_error_m": max(position_errors), + "max_hand_rotation_error_rad": max(rotation_errors), + "max_arm_correction_l2_rad": max(correction_norms), + "world_failures": world_failures, + "full_sequence_passed": len(corrected) == len(source["robot_joint_position_rad_or_m"]) and not world_failures, + "output_controls": str(out_dir / "ZBAR_REBASED_PICK_PROPOSAL.npz"), + "method": "At the measured stationary base, solve the left arm to reproduce each registered source gripper world pose. Preserve waist/head/right-arm/gripper commands. Screen every frame for self and released-room collisions, excluding intended robot-target contact; screen the target against the room after 5 mm lift.", + "native_validation_required": True, + } + if report["full_sequence_passed"]: + np.savez_compressed(out_dir / "ZBAR_REBASED_PICK_PROPOSAL.npz", names=controls["names"], pick=np.asarray(action20), source_pick=controls["pick"], full_q=np.asarray(corrected), desired_base=desired_base, measured_base=actual_base) + (out_dir / "ZBAR_REBASED_PICK_PROPOSAL.json").write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) + if not report["full_sequence_passed"]: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py new file mode 100644 index 0000000000000000000000000000000000000000..4c9e6a671e6ebc2b6643e454b9ecc9a20dd0bd88 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_fresh_placements.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +"""Read-only full placement qualification for fresh case-3 inputs.""" +import json, math, os, pathlib, sys, time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +A = W / "attempts/layout_03/attempt_001" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial import ConvexHull # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from shapely.geometry import Polygon # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from native_planner import build # noqa: E402 +from placement import solve # noqa: E402 + + +def m_xyzw(p): + out=np.eye(4);out[:3,3]=p[:3];out[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return out + + +def m_wxyz(xyz,q): + out=np.eye(4);out[:3,3]=xyz;out[:3,:3]=Rotation.from_quat(np.r_[q[1:],q[0]]).as_matrix();return out + + +def footprint(vertices, pose): + pts=vertices@pose[:3,:3].T+pose[:3,3] + return Polygon(pts[ConvexHull(pts[:,:2]).vertices,:2]) + + +def main(): + status=json.load(open(C/'STATUS.json')); assert status['status']=='PREPARING' + job=json.load(open(C/'job.json')); assert job['attempt_index']==job.get('native_attempt_index')==2 + exact=json.load(open(C/'EXACT_SOURCE_TRANSFER_PROPOSAL.json')) + prior_events=json.load(open(A/'rollout/ITEM_EVENTS.json')) + live_prop={e['item_index']:m_xyzw(e['metrics']['pose']) for e in prior_events['events'] if e['event']=='item_supported'} + live_place=json.load(open(A/'CASE3_ITEM01_LIVE_PLACE_PROPOSAL.json'))['accepted'] + names=job['source_dof_names']; floor=Polygon(job['tray_polygon_xy']).buffer(-.008) + + # Slots executed successfully in attempt 1 remain the first two nominal slots. + slots=[dict(item['placement_slot']) for item in job['items']] + slots[1]=dict(live_place['slot']) + station_search=json.load(open(C/'CASE3_ITEM23_STATION_SEARCH.json')) + for entry in station_search['items']: + slots[entry['i']]={'xy':entry['xy'],'yaw_rad':entry['yaw'],'footprint_xy':entry['footprint'],'tolerance_m':.001} + planned_polygons=[] + obstacle_poses=[] + for i,item in enumerate(job['items']): + vertices=np.load(item['controls_file'])['target_local'] + if i<2: + pose=live_prop[i]; poly=footprint(vertices,pose); obstacle_poses.append(pose) + else: + poly=Polygon(slots[i]['footprint_xy']) + pose=np.eye(4);pose[:3,:3]=Rotation.from_euler('z',slots[i]['yaw_rad']).as_matrix();pose[:2,3]=slots[i]['xy'];pose[2,3]=job['tray_floor_z']+.002-(vertices@pose[:3,:3].T)[:,2].min();obstacle_poses.append(pose) + planned_polygons.append(poly) + + geometry={"tray_inset_m":.008,"minimum_pair_gap_m":.012,"items":[],"pair_gaps_m":[]} + for i,poly in enumerate(planned_polygons): + geometry['items'].append({'item_index':i,'inside_inset_floor':bool(floor.covers(poly)),'inset_floor_boundary_gap_m':float(poly.distance(floor.boundary)),'footprint_xy':np.asarray(poly.exterior.coords)[:-1].tolist(),'footprint_source':'attempt1_settled_pose' if i<2 else 'planned_slot'}) + for i in range(4): + for k in range(i+1,4):geometry['pair_gaps_m'].append({'items':[i,k],'gap_m':float(planned_polygons[i].distance(planned_polygons[k]))}) + geometry['passed']=all(x['inside_inset_floor'] for x in geometry['items']) and all(x['gap_m']>.012 for x in geometry['pair_gaps_m']) + assert geometry['passed'] + + stage=Usd.Stage.Open(job['scene']); robot=Robot(ROBOT);robot.bodies=[];pose_check=build(stage) + results=[]; proposals=[] + for i,item in enumerate(job['items']): + began=time.monotonic(); controls=np.load(item['controls_file'],allow_pickle=True); source=np.load(pathlib.Path(item['pick_source'])/'recording_20hz.npz',allow_pickle=True) + ex=exact['items'][i]; prior=np.load(ex['prior_controls'],allow_pickle=True) + prior_names=prior['names'].tolist(); q=source['robot_joint_position_rad_or_m'][-1].astype(float).copy() + for jn,value in zip(prior_names,prior['place_initial']): + if 'gripper_' not in jn:q[names.index(jn)]=value + # Preserve every demonstrated physical gripper DOF from the pickup endpoint. + source_names=source['robot_dof_names'].tolist();q=np.asarray([q[source_names.index(n)] for n in names]) + target_i=source['rigid_body_paths'].tolist().index(item['target']);hand_path=f"/World/Robot/gripper_{item['hand']}_base_link";hand_i=source['robot_link_paths'].tolist().index(hand_path) + held=np.linalg.inv(m_wxyz(source['robot_link_xyz_m'][-1,hand_i],source['robot_link_wxyz'][-1,hand_i]))@m_wxyz(source['prop_xyz_m'][-1,target_i],source['prop_wxyz'][-1,target_i]) + vertices=controls['target_local'] + + if i==0: + base=np.asarray(item['place_base'],float); slot=dict(item['placement_slot']); place_kind='attempt1_native_supported_original' + elif i==1: + base=np.asarray(live_place['place_base'],float);slot=dict(live_place['slot']);place_kind='attempt1_native_supported_exact_pair_repair' + # Reproduce the exact posture used by the successful live repair. + for jn,value in zip(prior_names,live_place['place_initial20']): + if 'gripper_' not in jn:q[names.index(jn)]=value + else: + slot=dict(slots[i]); desired=np.eye(4);desired[:3,:3]=Rotation.from_euler('z',slot['yaw_rad']).as_matrix();desired[:2,3]=slot['xy'];desired[2,3]=job['tray_floor_z']+.002-(vertices@desired[:3,:3].T)[:,2].min() + base=desired@np.linalg.inv(np.asarray(ex['transferred_accepted_goal']))@np.asarray(ex['transferred_accepted_base']);base[2,3]=job['initial_base'][2][3] + place_kind='exact_accepted_chain_station_aligned_to_reserved_slot' + object_pose=robot.fk(names,q,base)[robot.root+f"/gripper_{item['hand']}_base_link"]@held + + prop_poses={} + for k in range(i):prop_poses[job['items'][k]['target']]=obstacle_poses[k] + checker=Clearance(stage,robot,names,item['hand'],item['target'],vertices,held,prop_poses);checker.deadline=time.monotonic()+1800 + prior_obstacles=planned_polygons[:i];future=[np.asarray(planned_polygons[k].exterior.coords)[:-1].tolist() for k in range(i+1,4)] + item_job={**job,**item,'placement_slot':slot,'place_base':base.tolist(),'reserved_tray_footprints':future,'placement_candidate_budget':800} + start_clear=checker.check(q,base);start_detail=checker.last + if not start_clear: + results.append({'item_index':i,'passed':False,'stage':'place_initial_world','detail':start_detail});continue + try: + plan,retreat,report=solve(robot,names,q,base,object_pose,vertices,item_job,prior_obstacles,pose_check,world_check=checker) + passed=True;failure=None + except Exception as exc: + plan=[];retreat=None;report=None;passed=False;failure=repr(exc) + proposal={'item_index':i,'target':item['target'],'place_kind':place_kind,'prior_controls':ex['prior_controls'],'place_initial20':prior['place_initial'].tolist(),'place_base':base.tolist(),'placement_slot':slot,'destination_xyz':[float(slot['xy'][0]),float(slot['xy'][1]),float(job['tray_floor_z'])],'source_held_transform':held.tolist(),'preserve_pick_endpoint_gripper_dofs':True} + if passed:proposal.update({'placement_frames':int(len(plan)),'retreat_q':retreat.tolist(),'placement_report':report}) + proposals.append(proposal) + results.append({'item_index':i,'target':item['target'],'passed':passed,'place_initial_world_clear':start_clear,'placement_frames':int(len(plan)),'failure':failure,'elapsed_s':time.monotonic()-began}) + print(json.dumps(results[-1]),flush=True) + + output={'schema':'multi_item.case3_fresh_placement_qualification.v1','read_only':True,'status_at_probe':status,'scene':job['scene'],'attempt_index':2,'geometry':geometry,'items':results,'proposals':proposals,'all_four_passed':bool(len(results)==4 and all(x['passed'] for x in results)),'native_240hz_validation_required':True,'motion_executed':False} + out=C/'CASE3_FRESH_PLACEMENT_QUALIFICATION.json';out.write_text(json.dumps(output,indent=2)+'\n');print(out) + if not output['all_four_passed']:raise SystemExit(2) + +if __name__=='__main__':main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py new file mode 100644 index 0000000000000000000000000000000000000000..8c296d56bc8157a18925a2d78f6a6033a458a01d --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_minimal_distractors.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""Read-only case-3 source-pick qualification after minimal blocker removal.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +REMOVED = [ + "/World/Assets/ShelfItems/cayman_jacked", + "/World/Assets/Tier2Items/oreo_gf", + "/World/Assets/Tier2Items/skit_littles", +] + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + [initial_props.pop(path, None) for path in REMOVED] + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + [stage.GetPrimAtPath(path).SetActive(False) for path in REMOVED] + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.025) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_25mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case3_minimal_distractor_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractors": REMOVED, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 25 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_four_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = C / "CASE3_MINIMAL_DISTRACTOR_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py new file mode 100644 index 0000000000000000000000000000000000000000..07a204294e871a69f90daf02bdbc6491edecbd06 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case3_without_cayman.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""Read-only in-memory source-pick qualification for fresh case 3 without Cayman.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_03" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +CAYMAN = "/World/Assets/ShelfItems/cayman_jacked" + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + initial_props.pop(CAYMAN, None) + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + stage.GetPrimAtPath(CAYMAN).SetActive(False) + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.005) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_5mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case3_without_cayman_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractor": CAYMAN, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 5 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_four_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = C / "CASE3_WITHOUT_CAYMAN_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py new file mode 100644 index 0000000000000000000000000000000000000000..22c3c8bcbf9721e5c392a27dbd204714c34a6c86 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case9_without_oreo.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""Read-only in-memory source-pick qualification for fresh case 9 without Oreo.""" +import json +import os +import pathlib +import sys +import time + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_09" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +import numpy as np # noqa: E402 +from scipy.spatial.transform import Rotation # noqa: E402 +from pxr import Usd # noqa: E402 +from collision_scene import ROBOT, Robot # noqa: E402 +from carry_transition import Clearance # noqa: E402 +from multi_motion import EmptyClearance # noqa: E402 + +OREO = "/World/Assets/Tier2Items/oreo_gf" + + +def matrix_xyzw(pose): + out = np.eye(4); out[:3, 3] = pose[:3]; out[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix(); return out + + +def matrix_wxyz(xyz, quat): + out = np.eye(4); out[:3, 3] = xyz; out[:3, :3] = Rotation.from_quat(np.r_[quat[1:], quat[0]]).as_matrix(); return out + + +def main(): + job = json.loads((C / "job.json").read_text()) + checkpoint = np.load(C / "rollout/checkpoint_60hz.npz", allow_pickle=True) + names = checkpoint["dof_names"].tolist() + initial_props = {str(path): matrix_xyzw(pose) for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][0])} + initial_props.pop(OREO, None) + results = [] + all_passed = True + for index, item in enumerate(job["items"]): + began = time.monotonic() + controls = np.load(item["controls_file"], allow_pickle=True) + source = np.load(pathlib.Path(item["pick_source"]) / "recording_20hz.npz", allow_pickle=True) + control_names = controls["names"].tolist() + ids = [names.index(name) for name in control_names] + q_path = [] + for row in controls["pick"]: + q = np.asarray(job["source_initial_q"], dtype=float).copy(); q[ids] = row; q_path.append(q) + q_path = np.asarray(q_path) + base = np.asarray(item["pick_base"], dtype=float) + + # In-memory only: remove the one nonrequested blocker and omit the active + # target from robot/world checking so intended finger contact is allowed. + stage = Usd.Stage.Open(job["scene"]) + stage.GetPrimAtPath(OREO).SetActive(False) + stage.GetPrimAtPath(item["target"]).SetActive(False) + props = dict(initial_props); props.pop(item["target"], None) + robot = Robot(ROBOT); robot.bodies = [] + empty = EmptyClearance(stage, robot, names, props, deadline_s=1800) + robot_failure = None + for frame, q in enumerate(q_path): + if frame == 0: + okay = empty.check(q, base) + else: + okay = empty.segment(q_path[frame - 1], q, base, base) + if not okay: + robot_failure = {"frame": frame, "detail": empty.last}; break + + # From measured source lift onward, additionally screen the demonstrated + # held-object transform against every retained collider. The source itself + # establishes intended bilateral finger contact and pickup physics. + target_i = source["rigid_body_paths"].tolist().index(item["target"]) + hand_path = f"/World/Robot/gripper_{item['hand']}_base_link" + hand_i = source["robot_link_paths"].tolist().index(hand_path) + source_target_xyz = source["prop_xyz_m"][:, target_i] + lift = np.flatnonzero(source_target_xyz[:, 2] > source_target_xyz[0, 2] + 0.005) + lift_frame = int(lift[0]) if len(lift) else len(q_path) + source_hand_final = matrix_wxyz(source["robot_link_xyz_m"][-1, hand_i], source["robot_link_wxyz"][-1, hand_i]) + source_target_final = matrix_wxyz(source["prop_xyz_m"][-1, target_i], source["prop_wxyz"][-1, target_i]) + held = np.linalg.inv(source_hand_final) @ source_target_final + carried = Clearance(stage, robot, names, item["hand"], item["target"], controls["target_local"], held, props) + carried.deadline = time.monotonic() + 1800 + carry_failure = None + if robot_failure is None: + for frame in range(lift_frame, len(q_path)): + if frame == lift_frame: + okay = carried.check(q_path[frame], base) + else: + okay = carried.segment(q_path[frame - 1], q_path[frame], base, base) + if not okay: + carry_failure = {"frame": frame, "detail": carried.last}; break + + passed = robot_failure is None and carry_failure is None + all_passed &= passed + results.append({ + "item_index": index, + "target": item["target"], + "source": item["pick_source"], + "frames": len(q_path), + "lift_screen_start_frame": lift_frame, + "robot_swept_path_against_all_other_props": robot_failure is None, + "held_target_swept_path_after_5mm_lift": carry_failure is None, + "robot_failure": robot_failure, + "held_target_failure": carry_failure, + "passed": passed, + "elapsed_s": time.monotonic() - began, + }) + print(json.dumps(results[-1]), flush=True) + + report = { + "schema": "multi_item.case9_without_oreo_pick_qualification.v1", + "read_only": True, + "scene_edit_mode": "in_memory_only_not_saved", + "removed_nonrequested_distractor": OREO, + "requested_targets": [item["target"] for item in job["items"]], + "method": "Every source control edge is screened with full robot self/world geometry against all retained static props except its intended target. From the source's measured 5 mm lift onward, the demonstrated final held transform and target hull are also screened against every retained collider. Intended gripper/target contact is excluded. Existing source episodes remain the physical grasp evidence.", + "items": results, + "all_three_passed": bool(all_passed), + "limitations": "This is conservative offline geometry qualification at source control resolution with interpolated edge checks. A fresh 240 Hz physical rollout and native validation remain mandatory.", + "scene_or_job_written": False, + "motion_executed": False, + } + out = W / "CASE9_WITHOUT_OREO_PICK_QUALIFICATION.json" + out.write_text(json.dumps(report, indent=2) + "\n") + print(out) + if not all_passed: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case_inputs.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case_inputs.py new file mode 100644 index 0000000000000000000000000000000000000000..d99693077f89ce55d7d311aae48caa8ccfebd8e9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_case_inputs.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""Static fresh-input gate; it does not launch or modify a simulation scene.""" +from __future__ import annotations +import argparse, hashlib, json, re +from pathlib import Path +import numpy as np + +ROOT=Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_stable_20261006') +OLD=Path('/mnt/agent-bench-data/lingjun/ziche/work/shelf_to_table_multi10_20261005') + +def digest(p): return hashlib.sha256(p.read_bytes()).hexdigest() + +def active(scene, name): + m=re.search(r'def Xform "'+re.escape(name)+r'" \((.*?)\n\s*\)',scene,re.S) + return bool(m) and 'active = false' not in m.group(1) + +def point_in_poly(p, poly): + x,y=p; inside=False + for a,b in zip(poly,poly[1:]+poly[:1]): + if (a[1]>y)!=(b[1]>y) and x < (b[0]-a[0])*(y-a[1])/(b[1]-a[1])+a[0]: inside=not inside + return inside + +def segdist(a,b,c,d): + def ptseg(p,u,v): + w=np.asarray(v)-u; t=np.clip(np.dot(np.asarray(p)-u,w)/max(np.dot(w,w),1e-15),0,1) + return float(np.linalg.norm(np.asarray(p)-(np.asarray(u)+t*w))) + return min(ptseg(a,c,d),ptseg(b,c,d),ptseg(c,a,b),ptseg(d,a,b)) + +def polydist(a,b): + if any(point_in_poly(p,b) for p in a) or any(point_in_poly(p,a) for p in b): return 0.0 + return min(segdist(x,y,u,v) for x,y in zip(a,a[1:]+a[:1]) for u,v in zip(b,b[1:]+b[:1])) + +def qualify(cid): + c=ROOT/'cases'/f'layout_{cid:02d}'; old=OLD/'cases'/f'layout_{cid:02d}' + j=json.loads((c/'job.json').read_text()); scene=(c/'scene.usda').read_text(); checks={} + checks['fresh_no_rollout']=not (c/'rollout').exists() + checks['empty_initial_tray']=j.get('initial_tray_items') in (0, []) + checks['item_count_3_or_4']=len(j['items']) in (3,4) + checks['unique_targets']=len({x['target'] for x in j['items']})==len(j['items']) + checks['all_targets_active']=all(active(scene,x['target'].rsplit('/',1)[-1]) for x in j['items']) + checks['all_paths_new_root']=str(OLD) not in (c/'job.json').read_text()+(c/'scene_physical.usda').read_text() + required={'names','initial','pick','place_initial','target_local'} + controls=[] + for x in j['items']: + p=Path(x['controls_file']); a=np.load(p,allow_pickle=True) + controls.append({'path':str(p),'sha256':digest(p),'fields':list(a.files), + 'valid':p.parent==c and required.issubset(a.files) and a['pick'].ndim==2}) + checks['controls_valid']=all(x['valid'] for x in controls) + tray=j['tray_polygon_xy']; slots=[x['placement_slot']['footprint_xy'] for x in j['items']] + # These authored slots were generated against floor.buffer(-8 mm). Recheck + # containment and retain the original exact footprints; pair clearance is + # computed against polygon edges rather than bounding boxes. + checks['slot_floor_containment']=all(all(point_in_poly(p,tray) for p in x) for x in slots) + checks['slot_pair_gap_12mm']=all(polydist(slots[a],slots[b])>.012 for a in range(len(slots)) for b in range(a+1,len(slots))) + special={} + startup_cleanup={ + 1:['pring_bbq','grapefruit_can'], + 2:['gatorade_t','pring_bbq'], + 4:['oreo_gf'], + 5:['gatorade_t','good2grow'], + 6:['cayman_jacked','good2grow'], + 7:['pring_bbq'], + } + if cid in startup_cleanup: + checks['unstable_nonrequested_prims_inactive']=all(not active(scene,x) for x in startup_cleanup[cid]) + special['startup_cleanup_inactive']=startup_cleanup[cid] + if cid==3: + removed=['cayman_jacked','oreo_gf','skit_littles','oreo_gf_copy_01','coke_c3_copy_01','pring_bbq']; special['inactive']=removed + checks['qualified_distractors_inactive']=all(not active(scene,x) for x in removed) + special['source_pick_qualification']=str(OLD/'attempts/layout_03/attempt_001/CASE3_MINIMAL_DISTRACTOR_PICK_QUALIFICATION.json') + if cid==9: + removed=['oreo_gf','cayman_jacked','starbucks_copy_01'] + checks['qualified_distractors_inactive']=all(not active(scene,x) for x in removed) + special['inactive']=removed; special['source_pick_qualification']=str(OLD/'CASE9_WITHOUT_OREO_PICK_QUALIFICATION.json') + if cid==8: + removed=['zbar_t','coke_c3'] + checks['unstable_nonrequested_prims_inactive']=all(not active(scene,x) for x in removed) + special['inactive']=removed + if cid==4: + checks['three_item_scope']=len(j['items'])==3 and [x['product'] for x in j['items']]==['good2grow','honeymaid','sprite_c1'] + ev=json.loads((old/'rollout/ITEM_EVENTS.json').read_text()) + supported={x['target'] for x in ev['events'] if x['event']=='item_supported'} + checks['all_retained_items_previously_supported']=all(x['target'] in supported for x in j['items']) + special['excluded_failed_pick']='pring_bbq' + passed=all(checks.values()) + receipt={'schema':'shelf_to_table_multi10.fresh_input_qualification.v1','case_id':cid, + 'passed':passed,'native_physics_still_required':True,'checks':checks,'controls':controls, + 'special_evidence':special,'job_sha256':digest(c/'job.json'),'scene_sha256':digest(c/'scene.usda'), + 'scene_physical_sha256':digest(c/'scene_physical.usda'),'collision_profile_sha256':digest(c/'collision_profile.usda')} + (c/'INPUT_QUALIFICATION.json').write_text(json.dumps(receipt,indent=2)+'\n') + status={'status':'PREPARED' if passed else 'PREPARING','phase':'fresh_input_qualified' if passed else 'input_qualification_failed', + 'item_index':None,'completed_objects':[],'control_ticks':0,'qualification':str(c/'INPUT_QUALIFICATION.json')} + (c/'STATUS.json').write_text(json.dumps(status,indent=2)+'\n') + return receipt + +if __name__=='__main__': + ap=argparse.ArgumentParser(); ap.add_argument('cases',nargs='+',type=int); a=ap.parse_args() + out=[qualify(x) for x in a.cases]; print(json.dumps([{'case_id':x['case_id'],'passed':x['passed'],'checks':x['checks']} for x in out],indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_return.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_return.py new file mode 100644 index 0000000000000000000000000000000000000000..7951d30609da95d0597f503a29abe713e478832e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_return.py @@ -0,0 +1,23 @@ +"""Exercise the actual future controller on two immutable recorded return plans.""" +import ast,hashlib,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;source=W/'run_episode.py';archive=W/'attempts/layout_01/attempt_001';namespace={'np':np,'math':math} +tree=ast.parse(source.read_text());node=next(n for n in tree.body if isinstance(n,ast.ClassDef) and n.name=='PrecisionReturnController');exec(compile(ast.Module(body=[node],type_ignores=[]),str(source),'exec'),namespace);Controller=namespace['PrecisionReturnController'];z=np.load(archive/'rollout/recording_60hz.npz');reports=[] +for item in (0,1): + primitives=json.loads((archive/f'rollout/return_primitives.item_{item:02d}.json').read_text());indices=np.flatnonzero((z['item_index']==item)&(z['phase']==f'item_{item:02d}_empty_return'));p=z['base_pose'][indices[0]];pose=np.r_[p[:2],Rotation.from_quat(p[3:]).as_euler('xyz')[2]];ticks=0;max_stall=0 + for primitive in primitives: + controller=Controller(primitive);last=pose.copy();stall=0;previous=0 + for tick in range(5400): + velocity=controller.step(pose);speed=controller.speed;turn=primitive['kind']=='turn';assert abs(speed)<=(.25 if turn else .18)+1e-12;assert abs(speed-previous)<=(.3 if turn else .12)/60+1e-12;previous=speed + pose+=velocity/60;ticks+=1 + if controller.done:break + moved=abs(math.atan2(math.sin(pose[2]-last[2]),math.cos(pose[2]-last[2]))) if turn else np.linalg.norm(pose[:2]-last[:2]) + if moved>.003:last=pose.copy();stall=0 + else:stall+=1 + max_stall=max(max_stall,stall);assert stall<360,'Existing stall detector would trip' + else:raise AssertionError('Primitive timeout') + goal=np.array(primitives[-1]['goal']);xy=float(np.linalg.norm(pose[:2]-goal[:2]));yaw=abs(math.atan2(math.sin(pose[2]-goal[2]),math.cos(pose[2]-goal[2])));assert xy<.0001 and yaw<.00001 + reports.append({'item':item,'xy_error_m':xy,'yaw_error_rad':yaw,'duration_s':ticks/60,'max_no_progress_s':max_stall/60,'speed_acceleration_caps_pass':True}) +result={'passed':True,'actual_runner_sha256':hashlib.sha256(source.read_bytes()).hexdigest(),'source_attempt':str(archive),'reports':reports,'limitation':'Exact kinematics only; actual native contact tracking and measured arrival gate remain required.'};(W/'PRECISION_RETURN_IMPLEMENTATION_QUALIFICATION.json').write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(result)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py new file mode 100644 index 0000000000000000000000000000000000000000..a1fa3012c67175d3c8b0bab56453ed729d589eeb --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor.py @@ -0,0 +1,37 @@ +import ast,hashlib,json,math +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent; src=W/'run_episode.py'; tree=ast.parse(src.read_text()); ns={'np':np,'math':math} +for n in tree.body: + if isinstance(n,ast.ClassDef) and n.name=='PrecisionReturnController':exec(compile(ast.Module(body=[n],type_ignores=[]),str(src),'exec'),ns) + if isinstance(n,ast.Assign) and any(isinstance(t,ast.Name) and t.id=='PRECISION_RETURN_POLICY' for t in n.targets):exec(compile(ast.Module(body=[n],type_ignores=[]),str(src),'exec'),ns) +C=ns['PrecisionReturnController']; policy=ns['PRECISION_RETURN_POLICY'] +def ad(a,b):return math.atan2(math.sin(a-b),math.cos(a-b)) +plans=[] +for lp in sorted((W/'cases').glob('layout_*')): + rec=lp/'rollout/recording_60hz.npz' + if not rec.exists():continue + z=np.load(rec,allow_pickle=False) + for pp in sorted((lp/'rollout').glob('return_primitives.item_*.json')): + item=int(pp.stem.split('_')[-1]); inds=np.flatnonzero((z['item_index']==item)&(z['phase']==f'item_{item:02d}_empty_return')) + if not len(inds):continue + p7=z['base_pose'][inds[0]]; pose=np.r_[p7[:2],Rotation.from_quat(p7[3:]).as_euler('xyz')[2]]; rows=[] + for k,prim in enumerate(json.loads(pp.read_text())): + c=C(prim) + for tick in range(5400): + v=c.step(pose); pose=pose+v/60 + if c.done:break + else:raise AssertionError((pp,k,'timeout')) + goal=np.asarray(prim['goal'],float); xy=float(np.linalg.norm(pose[:2]-goal[:2])); yaw=abs(ad(pose[2],goal[2])); rows.append(dict(index=k,kind=prim['kind'],ticks=tick+1,xy_error_m=xy,yaw_error_rad=yaw)) + assert xy<=policy['arrival_translation_gate_m'] and yaw<=policy['arrival_yaw_gate_rad'],(pp,k,xy,yaw) + plans.append(dict(layout=lp.name,item_index=item,source_plan=str(pp.relative_to(W)),primitive_count=len(rows),final_xy_error_m=rows[-1]['xy_error_m'],final_yaw_error_rad=rows[-1]['yaw_error_rad'],max_primitive_ticks=max(x['ticks'] for x in rows))) +repairs=[] +for p in sorted((W/'cases').glob('layout_*/rollout/RETURN_MICROTURN_REPAIR*.json')): + d=json.loads(p.read_text()); pulses=d['pulse_history']; rates={abs(float(x['commanded_yaw_rate_rad_s'])) for x in pulses} + assert rates=={.001}; assert d['xy_error_m']<=.0005 and d['yaw_error_rad']<=.0001 + assert d['empty_hand_full_body_segment_clear'] and d['all_live_props_checked'] and not d['pose_writes_or_resets'] + repairs.append(dict(path=str(p.relative_to(W)),sha256=hashlib.sha256(p.read_bytes()).hexdigest(),pulses=len(pulses),initial_yaw_error_rad=pulses[0]['yaw_error_rad'],final_yaw_error_rad=d['yaw_error_rad'],xy_error_m=d['xy_error_m'])) +assert repairs +result=dict(schema='multi_item.precision_return_turn_floor_qualification.v1',passed=True,run_episode_sha256=hashlib.sha256(src.read_bytes()).hexdigest(),change=dict(turn_min_speed_rad_s=.001,prior_turn_min_speed_rad_s=.00002,turn_deadband_rad=policy['turn_deadband_rad'],turn_done_error_rad=policy['turn_done_error_rad'],arrival_translation_gate_m=policy['arrival_translation_gate_m'],arrival_yaw_gate_rad=policy['arrival_yaw_gate_rad'],normal_turn_speed_cap_rad_s=.25,scope=policy['scope']),native_evidence=dict(repair_count=len(repairs),layout_count=len({x['path'].split('/')[1] for x in repairs}),all_commands_rad_s=.001,all_screened_full_body_and_live_props=True,all_measured_endpoints_within_gate=True,repairs=repairs),exact_kinematic_regression=dict(plan_count=len(plans),layouts=sorted({x['layout'] for x in plans}),all_primitive_endpoints_within_unchanged_gate=True,plans=plans),limits='Exact-kinematic replay verifies controller math; recorded native microturn repairs establish that the 0.001 rad/s floor overcomes native static friction. Fresh uninterrupted native returns remain required for final acceptance.') +out=W/'diagnostics/PRECISION_RETURN_TURN_FLOOR_QUALIFICATION.json';out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps({k:v for k,v in result.items() if k not in ('native_evidence','exact_kinematic_regression')},indent=2));print(out,hashlib.sha256(out.read_bytes()).hexdigest(),len(repairs),len(plans)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..5af71f515633e3500912fc485350243d011f6a43 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_precision_turn_floor_v2.py @@ -0,0 +1,96 @@ +"""Qualify the 0.003 rad/s precision-turn floor from native recoveries and plan replay.""" +import ast +import hashlib +import json +import math +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +W = Path(__file__).resolve().parent +source = W / "run_episode.py" +tree = ast.parse(source.read_text()) +namespace = {"np": np, "math": math} +for node in tree.body: + if isinstance(node, ast.ClassDef) and node.name == "PrecisionReturnController": + exec(compile(ast.Module(body=[node], type_ignores=[]), str(source), "exec"), namespace) + if isinstance(node, ast.Assign) and any(isinstance(target, ast.Name) and target.id == "PRECISION_RETURN_POLICY" for target in node.targets): + exec(compile(ast.Module(body=[node], type_ignores=[]), str(source), "exec"), namespace) +Controller = namespace["PrecisionReturnController"] +policy = namespace["PRECISION_RETURN_POLICY"] + + +def angle_delta(a, b): + return math.atan2(math.sin(a - b), math.cos(a - b)) + + +plans = [] +for layout in sorted((W / "cases").glob("layout_*")): + recording = layout / "rollout/recording_60hz.npz" + if not recording.exists(): + recording = layout / "rollout/checkpoint_60hz.npz" + if not recording.exists(): + continue + data = np.load(recording, allow_pickle=False) + for path in sorted((layout / "rollout").glob("return_primitives.item_*.json")): + item = int(path.stem.split("_")[-1]) + indices = np.flatnonzero((data["item_index"] == item) & (data["phase"] == f"item_{item:02d}_empty_return")) + if not len(indices): + continue + pose7 = data["base_pose"][indices[0]] + pose = np.r_[pose7[:2], Rotation.from_quat(pose7[3:]).as_euler("xyz")[2]] + rows = [] + for index, primitive in enumerate(json.loads(path.read_text())): + controller = Controller(primitive) + for tick in range(5400): + velocity = controller.step(pose) + pose = pose + velocity / 60.0 + if controller.done: + break + else: + raise AssertionError((path, index, "timeout")) + goal = np.asarray(primitive["goal"], dtype=float) + xy_error = float(np.linalg.norm(pose[:2] - goal[:2])) + yaw_error = abs(angle_delta(pose[2], goal[2])) + assert xy_error <= policy["arrival_translation_gate_m"] + assert yaw_error <= policy["arrival_yaw_gate_rad"] + rows.append({"index": index, "kind": primitive["kind"], "ticks": tick + 1, + "xy_error_m": xy_error, "yaw_error_rad": yaw_error}) + plans.append({"layout": layout.name, "item_index": item, + "source_plan": str(path.relative_to(W)), "primitive_count": len(rows), + "max_primitive_ticks": max(row["ticks"] for row in rows)}) + +native_paths = [ + W / "cases/layout_07/rollout/NAVIGATION_MOTION_RECOVERY_EXECUTION.item_00.e4721a9f8bd7.json", + W / "cases/layout_10/rollout/NAVIGATION_MOTION_RECOVERY_EXECUTION.item_00.206c44a5234c.json", +] +native = [] +for path in native_paths: + data = json.loads(path.read_text()) + assert data["minimum_commanded_turn_rate_rad_s"] == 0.003 + assert data["full_body_all_live_props_screened"] and data["empty_hand"] + assert not data["no_pose_writes"] is False and not data["stale_primitive_replayed"] + assert data["xy_error_m"] <= policy["arrival_translation_gate_m"] + assert data["yaw_error_rad"] <= policy["arrival_yaw_gate_rad"] + native.append({"path": str(path.relative_to(W)), "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "start_tick": data["start_tick"], "end_tick": data["end_tick"], + "xy_error_m": data["xy_error_m"], "yaw_error_rad": data["yaw_error_rad"]}) + +result = { + "schema": "multi_item.precision_return_turn_floor_qualification.v2", + "passed": bool(plans) and len(native) == 2, + "run_episode_sha256": hashlib.sha256(source.read_bytes()).hexdigest(), + "change": {"turn_min_speed_rad_s": 0.003, "supersedes_turn_min_speed_rad_s": 0.001, + "strict_arrival_translation_gate_m": policy["arrival_translation_gate_m"], + "strict_arrival_yaw_gate_rad": policy["arrival_yaw_gate_rad"], + "speed_cap_rad_s": 0.25, "scope": policy["scope"]}, + "native_recoveries": native, + "exact_kinematic_regression": {"plan_count": len(plans), "plans": plans, + "all_endpoints_within_unchanged_gate": True}, + "reason": "Two fresh native returns stalled for six seconds near 0.001 rad/s. The screened 0.003 rad/s recoveries reached the unchanged strict endpoint gates without replay or pose writes.", +} +output = W / "diagnostics/PRECISION_RETURN_TURN_FLOOR_V2_QUALIFICATION.json" +output.write_text(json.dumps(result, indent=2) + "\n") +print(json.dumps({"passed": result["passed"], "plans": len(plans), "native": len(native), + "output": str(output), "sha256": hashlib.sha256(output.read_bytes()).hexdigest()})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head.py new file mode 100644 index 0000000000000000000000000000000000000000..c27b9ffb5f59e9eaeed049648fcd694bdbeeeb2e --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head.py @@ -0,0 +1,36 @@ +"""Check neutral head optical direction and sampled real return geometry without physics.""" +import os,sys,json,hashlib +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd,UsdGeom +from collision_scene import Robot,ROBOT +from multi_motion import EmptyClearance +from return_head_policy import level_head,HEAD_JOINTS,BODY_PITCH_JOINT + +def matrix(p): + r=np.eye(4);r[:3,3]=p[:3];r[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return r + +records=[] +for layout in [4,10]: + c=W/'cases'/f'layout_{layout:02d}';job=json.loads((c/'job.json').read_text());out=c/'rollout' + rec=np.load(out/'recording_60hz.npz');full=np.load(out/'full_state.npz');names=rec['dof_names'].tolist();stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] + paths=full['rigid_body_paths'].tolist();xyz=full['prop_xyz_m'];quat=full['prop_wxyz'][:,:,[1,2,3,0]] + cache=UsdGeom.XformCache();camera=stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera');head=stage.GetPrimAtPath('/World/Robot/head_link2') + mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(head)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + for item in range(len(job['items'])-1): + indices=np.flatnonzero((rec['item_index']==item)&(rec['phase']==f'item_{item:02d}_empty_return')) + first=int(indices[0]);last=int(indices[-1]);selected=sorted(set([first,last]+[int(indices[int(t)]) for t in np.linspace(0,len(indices)-1,7)])) + for row in selected: + q=rec['q'][row].astype(float);b=matrix(rec['base_pose'][row]);lev=level_head(q,names) + props={path:matrix(np.r_[xyz[row,k],quat[row,k]]) for k,path in enumerate(paths)} + check=EmptyClearance(stage,robot,names,props,deadline_s=900) + passed=check.segment(q,lev,b,b) if row in [first,last] else check.check(lev,b) + fk=robot.fk(names,lev,b);cm=fk[robot.root+'/head_link2']@mount;axis=-cm[:3,2];axis/=np.linalg.norm(axis) + pitch=float(np.rad2deg(np.arcsin(np.clip(axis[2],-1,1)))) + records.append(dict(layout_id=layout,item_index=item,row_index=row,tick=int(rec['tick'][row]),original_head_rad=q[[names.index(j) for j in HEAD_JOINTS]].tolist(),new_head_rad=lev[[names.index(j) for j in HEAD_JOINTS]].tolist(),expected_optical_pitch_deg=-float(np.rad2deg(lev[names.index(BODY_PITCH_JOINT)]+lev[names.index(HEAD_JOINTS[1])])),optical_axis_world=axis.tolist(),optical_pitch_deg=pitch,geometry_passed=bool(passed),collision_detail=check.last)) +report=dict(schema='level_return_head_offline_qualification_v1',passed=all(r['geometry_passed'] and abs(r['optical_pitch_deg']-r['expected_optical_pitch_deg'])<1 for r in records),scope='Real recorded empty-return start/end transitions and seven progress samples per return on complete layouts04/10. Read-only; not a native rollout acceptance.',records=records,implementation_sha256=hashlib.sha256((W/'return_head_policy.py').read_bytes()).hexdigest()) +p=W/'RETURN_HEAD_OFFLINE_QUALIFICATION.json';p.write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({'passed':report['passed'],'samples':len(records),'max_optical_pitch_deg':max(abs(r['optical_pitch_deg']) for r in records),'geometry_failures':[r for r in records if not r['geometry_passed']]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py new file mode 100644 index 0000000000000000000000000000000000000000..206c45d61a3c9f8530d2f6648560184f26b880fc --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/qualify_return_head_normalized.py @@ -0,0 +1,36 @@ +"""Check neutral head optical direction and sampled real return geometry without physics.""" +import os,sys,json,hashlib +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +W=Path(__file__).resolve().parent;R=W.parents[1] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'work/family3_repair/collision/python_deps'),str(R/'code/family3_repair/real2sim/teleop')] +os.environ['SPATIALINDEX_C_LIBRARY']=str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so') +from pxr import Usd,UsdGeom +from collision_scene import Robot,ROBOT +from multi_motion import EmptyClearance +from return_head_policy import level_head,HEAD_JOINTS,BODY_PITCH_JOINT + +def matrix(p): + r=np.eye(4);r[:3,3]=p[:3];r[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();return r + +records=[] +for layout in [4,10]: + c={4:W/'attempts/layout_04/attempt_002',10:W/'attempts/layout_10/attempt_001'}[layout];job=json.loads((c/'job.json').read_text());out=c/'rollout' + rec=np.load(out/'recording_60hz.npz');full=np.load(out/'full_state.npz');names=rec['dof_names'].tolist();stage=Usd.Stage.Open(job['scene']);robot=Robot(ROBOT);robot.bodies=[] + paths=full['rigid_body_paths'].tolist();xyz=full['prop_xyz_m'];quat=full['prop_wxyz'][:,:,[1,2,3,0]] + cache=UsdGeom.XformCache();camera=stage.GetPrimAtPath('/World/Robot/head_link2/Head_Camera');head=stage.GetPrimAtPath('/World/Robot/head_link2') + mount=np.linalg.inv(np.asarray(cache.GetLocalToWorldTransform(head)).T)@np.asarray(cache.GetLocalToWorldTransform(camera)).T + for item in range(len(job['items'])-1): + indices=np.flatnonzero((rec['item_index']==item)&(rec['phase']==f'item_{item:02d}_empty_return')) + first=int(indices[0]);last=int(indices[-1]);selected=sorted(set([first,last]+[int(indices[int(t)]) for t in np.linspace(0,len(indices)-1,7)])) + for row in selected: + q=rec['q'][row].astype(float);b=matrix(rec['base_pose'][row]);lev=level_head(q,names) + props={path:matrix(np.r_[xyz[row,k],quat[row,k]]) for k,path in enumerate(paths)} + check=EmptyClearance(stage,robot,names,props,deadline_s=900) + passed=check.segment(q,lev,b,b) if row in [first,last] else check.check(lev,b) + fk=robot.fk(names,lev,b);cm=fk[robot.root+'/head_link2']@mount;axis=-cm[:3,2];axis/=np.linalg.norm(axis) + pitch=float(np.rad2deg(np.arcsin(np.clip(axis[2],-1,1)))) + records.append(dict(layout_id=layout,item_index=item,row_index=row,tick=int(rec['tick'][row]),original_head_rad=q[[names.index(j) for j in HEAD_JOINTS]].tolist(),new_head_rad=lev[[names.index(j) for j in HEAD_JOINTS]].tolist(),expected_optical_pitch_deg=-float(np.rad2deg(lev[names.index(BODY_PITCH_JOINT)]+lev[names.index(HEAD_JOINTS[1])])),optical_axis_world=axis.tolist(),optical_pitch_deg=pitch,geometry_passed=bool(passed),collision_detail=check.last)) +report=dict(schema='level_return_head_offline_qualification_v1',passed=all(r['geometry_passed'] and abs(r['optical_pitch_deg']-r['expected_optical_pitch_deg'])<1 for r in records),scope='Real recorded empty-return start/end transitions and seven progress samples per return on complete layouts04/10. Read-only; not a native rollout acceptance.',records=records,implementation_sha256=hashlib.sha256((W/'return_head_policy.py').read_bytes()).hexdigest()) +p=W/'RETURN_HEAD_INPUT_NORMALIZATION_OFFLINE_QUALIFICATION.json';p.write_text(json.dumps(report,indent=2)+'\n');print(json.dumps({'passed':report['passed'],'samples':len(records),'max_optical_pitch_deg':max(abs(r['optical_pitch_deg']) for r in records),'geometry_failures':[r for r in records if not r['geometry_passed']]})) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/record_manual_review.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/record_manual_review.py new file mode 100644 index 0000000000000000000000000000000000000000..9cb15a7612f24ccf65f169d4c11ca70f35d479ec --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/record_manual_review.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python3 +"""Record an explicit human panel review; never infer approval.""" +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path + +W = Path(__file__).resolve().parent + +parser = argparse.ArgumentParser() +parser.add_argument("--layout", type=int, required=True) +parser.add_argument("--reviewer", required=True) +parser.add_argument("--observation", required=True) +parser.add_argument("--approve", action="store_true", required=True) +args = parser.parse_args() +folder = W / "qa" / f"layout_{args.layout:02d}" +panels = json.loads((folder / "visual_evidence/PANELS.json").read_text()) +digests = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} +report = {"schema": "ziche.multi10.root_manual_visual_review.v1", "layout_id": args.layout, + "passed": True, "reviewer": args.reviewer, "reviewed_utc": datetime.now(timezone.utc).isoformat(), + "reviewed_all_three_views": True, "reviewed_first_retained_0p5s": True, + "reviewed_every_phase_midpoint": True, "panels": digests, + "observation": args.observation, + "scope": "Human review of first retained 0.5 seconds and every ordered phase midpoint in all three cameras."} +(folder / "ROOT_MANUAL_REVIEW.json").write_text(json.dumps(report, indent=2) + "\n") +print(folder / "ROOT_MANUAL_REVIEW.json") diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/release_sweep.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/release_sweep.py new file mode 100644 index 0000000000000000000000000000000000000000..77a4c351e120b9c10bbb5662c06bb6229195f8be --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/release_sweep.py @@ -0,0 +1,292 @@ +"""Conservative open-gripper and retreat collision screen for tray placement. + +The carried-object checker is valid only until release. This checker keeps the +released target fixed at the proposed landing pose, opens both fingers to their +articulation limits, and then sweeps the open hand through the retreat posture. +It never changes simulation state; native physics and contact audits remain +authoritative. +""" +import math +import time +import hashlib +import json +from pathlib import Path + +import numpy as np + + +class ReleaseSweep: + """Screen the complete release motion against every live world object.""" + + def __init__( + self, + stage, + robot, + names, + hand, + target, + target_vertices, + prop_poses, + place_source, + pose_check=None, + deadline_s=900, + joint_step_rad=0.005, + ): + from pxr import Usd, UsdGeom, UsdPhysics + from collision_scene import Body, broad_overlap, collider_mesh, contact, matrix, rigid_ancestor + from native_planner import build + import trimesh + + if hand not in {"l", "r"}: + raise ValueError("release hand must be 'l' or 'r'") + self.deadline = time.monotonic() + float(deadline_s) + self.robot = robot + self.names = list(names) + self.hand = hand + self.target_path = str(target) + self.target_vertices = np.asarray(target_vertices, dtype=float) + self.pose_check = pose_check or build(stage) + self.contact = contact + self.broad_overlap = broad_overlap + self.cache = UsdGeom.XformCache() + self.environment = [] + self.last = None + self.joint_step_rad = float(joint_step_rad) + if self.target_vertices.ndim != 2 or self.target_vertices.shape[1] != 3: + raise ValueError("target_vertices must be an Nx3 array") + if not 0 < self.joint_step_rad <= 0.02: + raise ValueError("joint_step_rad must be in (0, 0.02]") + + # A single conservative convex target is used at the proposed landing + # pose. The target's authored colliders are excluded below so it is + # represented exactly once during the release sweep. + self.target = Body(self.target_path, trimesh.convex.convex_hull(self.target_vertices)) + for prim in Usd.PrimRange(stage.GetPseudoRoot(), Usd.TraverseInstanceProxies()): + path = str(prim.GetPath()) + if path.startswith("/World/Robot/") or path == self.target_path or path.startswith(self.target_path + "/"): + continue + if not prim.HasAPI(UsdPhysics.CollisionAPI): + continue + if UsdPhysics.CollisionAPI(prim).GetCollisionEnabledAttr().Get() is False: + continue + mesh, _, _ = collider_mesh(prim) + world = matrix(self.cache.GetLocalToWorldTransform(prim)) + owner = rigid_ancestor(prim) + if owner and str(owner.GetPath()) in prop_poses: + authored = matrix(self.cache.GetLocalToWorldTransform(owner)) + authored[:3, :3] /= np.linalg.norm(authored[:3, :3], axis=0) + world = np.asarray(prop_poses[str(owner.GetPath())], dtype=float) @ np.linalg.inv(authored) @ world + mesh.apply_transform(world) + # The base has intentional floor contact. Furniture, the tray, + # and every placed or shelf object remain in the 3-D environment. + if mesh.bounds[1, 2] < 0.08: + continue + self.environment.append(Body(path, mesh)) + self.low = np.asarray([body.lo for body in self.environment]) + self.high = np.asarray([body.hi for body in self.environment]) + + # The gripper is a constrained passive linkage. Independently moving + # every joint to a limit creates impossible intermediate postures, so + # use the actual 60 Hz articulation trajectory from this item's exact + # accepted place source. The new arm/waist retreat remains planned in + # the current room; only the passive finger linkage comes from source. + source = Path(place_source) + recording_path = source / "recording_60hz.npz" + metrics_path = source / "place_metrics.json" + if not recording_path.is_file() or not metrics_path.is_file(): + raise FileNotFoundError(f"accepted place source lacks release evidence: {source}") + recording = np.load(recording_path) + metrics = json.loads(metrics_path.read_text()) + if str(metrics.get("hand")) != hand: + raise ValueError("accepted place source hand does not match active hand") + source_names = recording["robot_dof_names"].tolist() + source_q = np.asarray(recording["robot_joint_position_rad_or_m"], dtype=float) + frame_position = np.asarray(recording["render_frame_position"], dtype=float) + self.gripper_indices = [i for i, name in enumerate(self.names) if f"gripper_{hand}" in name] + if len(self.gripper_indices) != 8: + raise ValueError("expected eight assigned gripper articulation joints") + try: + source_indices = [source_names.index(self.names[i]) for i in self.gripper_indices] + outer_index = source_names.index(f"idx{4 if hand == 'l' else 8}1_gripper_{hand}_outer_joint1") + except ValueError as exc: + raise ValueError("accepted place source gripper schema mismatch") from exc + estimated = int(np.searchsorted(frame_position, float(metrics["release_t"]))) + final_outer = float(np.median(source_q[-60:, outer_index])) + baseline_slice = source_q[max(0, estimated - 90):max(1, estimated - 15), outer_index] + baseline_outer = float(np.median(baseline_slice)) + threshold = max(0.01, 0.02 * abs(final_outer - baseline_outer)) + search_lo = max(0, estimated - 120) + search_hi = min(len(source_q), estimated + 60) + changed = np.flatnonzero(np.abs(source_q[search_lo:search_hi, outer_index] - baseline_outer) > threshold) + if not len(changed): + raise ValueError("accepted place source has no detectable gripper opening") + opening_start = max(search_lo, search_lo + int(changed[0]) - 1) + opening_end = None + for index in range(opening_start + 1, len(source_q) - 10): + if np.all(np.abs(source_q[index:index + 10, outer_index] - final_outer) <= 0.004): + opening_end = index + break + if opening_end is None: + raise ValueError("accepted place source gripper never reaches a stable open state") + self.source_open_path = source_q[opening_start:opening_end + 1, source_indices].copy() + self.source_open_final = np.median(source_q[-60:, source_indices], axis=0) + self.source_evidence = { + "place_source": str(source), + "recording_60hz_sha256": hashlib.sha256(recording_path.read_bytes()).hexdigest(), + "place_metrics_sha256": hashlib.sha256(metrics_path.read_bytes()).hexdigest(), + "opening_start_row": int(opening_start), + "opening_end_row": int(opening_end), + "source_opening_samples": int(len(self.source_open_path)), + "release_t_20hz": float(metrics["release_t"]), + } + + self.allowed_target_links = { + f"/World/Robot/gripper_{hand}_inner_link4", + f"/World/Robot/gripper_{hand}_outer_link4", + } + + def _check(self, q, base, phase, sample_index, fraction, allowed_contacts): + if time.monotonic() > self.deadline: + raise RuntimeError("release sweep exceeded its deadline") + fk = self.robot.fk(self.names, q, base) + okay, detail = self.pose_check(fk, self.robot.root) + if not okay: + self.last = {"kind": "self_or_wrist", "phase": phase, "sample": sample_index, "detail": detail} + return False + for body in self.pose_check.bodies: + body.update(fk[body.name.replace("/World/Robot", self.robot.root, 1)]) + + for body in self.pose_check.bodies: + if self.broad_overlap(self.target, body): + hit = self.contact(self.target, body, containment=True) + if hit is not None: + if body.name in self.allowed_target_links: + allowed_contacts.append( + {"phase": phase, "sample": int(sample_index), "fraction": float(fraction), "link": body.name} + ) + else: + self.last = { + "kind": "released_target_robot", + "phase": phase, + "sample": int(sample_index), + "target": self.target.name, + "robot": body.name, + } + return False + if len(self.environment): + indices = np.flatnonzero(np.all(self.high >= body.lo, axis=1) & np.all(self.low <= body.hi, axis=1)) + for index in indices: + other = self.environment[index] + if self.contact(body, other, containment=True) is not None: + self.last = { + "kind": "released_world", + "phase": phase, + "sample": int(sample_index), + "robot": body.name, + "obstacle": other.name, + } + return False + return True + + def check(self, landing_q, retreat_q, base, target_pose): + landing_q = np.asarray(landing_q, dtype=float) + retreat_q = np.asarray(retreat_q, dtype=float) + base = np.asarray(base, dtype=float) + target_pose = np.asarray(target_pose, dtype=float) + if landing_q.shape != retreat_q.shape or landing_q.shape != (len(self.names),): + raise ValueError("release sweep requires two full robot joint vectors") + if base.shape != (4, 4) or target_pose.shape != (4, 4): + raise ValueError("release sweep base and target pose must be 4x4") + + self.target.update(target_pose) + # First bridge the measured live grasp to the source's initial passive + # linkage state, then follow every recorded source opening sample. + source_first = self.source_open_path[0] + bridge_count = max(2, int(math.ceil(np.max(np.abs(source_first - landing_q[self.gripper_indices])) / self.joint_step_rad)) + 1) + opening_path = [] + for fraction in np.linspace(0.0, 1.0, bridge_count): + q = landing_q.copy() + q[self.gripper_indices] = landing_q[self.gripper_indices] * (1.0 - fraction) + source_first * fraction + opening_path.append(q) + for values in self.source_open_path[1:]: + q = landing_q.copy() + q[self.gripper_indices] = values + opening_path.append(q) + while len(opening_path) < 121: + opening_path.append(opening_path[-1].copy()) + opened_q = opening_path[-1].copy() + opened_q[self.gripper_indices] = self.source_open_final + retreat_open_q = np.asarray(retreat_q, dtype=float).copy() + retreat_open_q[self.gripper_indices] = self.source_open_final + opening_count = len(opening_path) + retreat_count = max(61, int(math.ceil(np.max(np.abs(retreat_open_q - opened_q)) / self.joint_step_rad)) + 1) + allowed_contacts = [] + contact_samples = {"opening": set(), "retreat": set()} + samples = 0 + self.last = None + phase_paths = ( + ("opening", opening_path), + ("retreat", [opened_q * (1.0 - u) + retreat_open_q * u for u in np.linspace(0.0, 1.0, retreat_count)]), + ) + for phase, path in phase_paths: + count = len(path) + for sample_index, q in enumerate(path): + fraction = sample_index / max(1, count - 1) + samples += 1 + before = len(allowed_contacts) + if not self._check(q, base, phase, sample_index, fraction, allowed_contacts): + return False, { + "status": "BLOCKED", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "detail": self.last, + } + if len(allowed_contacts) > before: + contact_samples[phase].add(sample_index) + + # Assigned fingertips may initially touch the released object, but the + # final quarter must be separated and contact may not reappear after + # three consecutive clean retreat samples. + late_start = int(math.ceil(0.75 * (retreat_count - 1))) + late_contacts = sorted(i for i in contact_samples["retreat"] if i >= late_start) + clean_start = None + for index in range(max(0, retreat_count - 2)): + if all(j not in contact_samples["retreat"] for j in (index, index + 1, index + 2)): + clean_start = index + break + recontacts = [] if clean_start is None else sorted(i for i in contact_samples["retreat"] if i > clean_start + 2) + if late_contacts or clean_start is None or recontacts: + self.last = { + "kind": "assigned_finger_target_failed_to_separate", + "late_contact_samples": late_contacts, + "first_three_clean_start": clean_start, + "recontact_samples": recontacts, + } + return False, { + "status": "BLOCKED", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "detail": self.last, + } + + report = { + "schema": "ziche.open_gripper_release_sweep.v1", + "status": "CLEAR", + "samples_checked": samples, + "opening_samples": opening_count, + "retreat_samples": retreat_count, + "joint_step_rad_max": self.joint_step_rad, + "passive_finger_policy": "exact_accepted_place_source_60hz_articulation_trajectory", + "source_evidence": self.source_evidence, + "assigned_contact_samples": {key: sorted(value) for key, value in contact_samples.items()}, + "first_three_clean_retreat_start": clean_start, + "final_retreat_quarter_clear": True, + "no_recontact_after_three_clean_samples": True, + "all_live_props_checked": True, + "released_target_stationary_at_goal": True, + "pose_writes_or_resets": False, + } + self.last = None + return True, report diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/repair_place_station.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/repair_place_station.py new file mode 100644 index 0000000000000000000000000000000000000000..39db9c53f92ce1dea3c64e0ad51ae1da1885c70c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/repair_place_station.py @@ -0,0 +1,224 @@ +"""Planning-boundary repair for a measured, blocked place station. + +This module never writes simulation poses. Any physical reposition uses the +runner's screened navigation primitives and normal control blending. +""" +import hashlib +import json +import time +from pathlib import Path + +import numpy as np + + +def _data_dict(data): + if isinstance(data, dict): + return {key: np.asarray(value).copy() for key, value in data.items()} + return {key: np.asarray(data[key]).copy() for key in data.files} + + +def _receipt_path(g, record, station): + token = hashlib.sha256(json.dumps(station, sort_keys=True).encode()).hexdigest()[:12] + return Path(g["out"]) / ( + f"REPOSITION_REPAIR.item_{int(record['item_index']):02d}." + f"tick_{int(record['tick']):08d}.{token}.json" + ) + + +def apply(g, item_job, record): + """Apply a source-grounded place-station repair at a frozen boundary.""" + station = item_job.get("repair_station") + if not isinstance(station, dict): + raise ValueError("item_job.repair_station must be an object") + proposed_base = np.asarray(station.get("place_base"), dtype=float) + if proposed_base.shape != (4, 4) or not np.isfinite(proposed_base).all(): + raise ValueError("repair_station.place_base must be a finite 4x4 matrix") + proposal_path = Path(station.get("proposal_path", "")) + if not proposal_path.is_absolute(): + proposal_path = Path(g["case"]) / proposal_path + if not proposal_path.is_file(): + raise FileNotFoundError(f"repair station proposal is missing: {proposal_path}") + proposal_sha256 = hashlib.sha256(proposal_path.read_bytes()).hexdigest() + + data = _data_dict(g["data"]) + control_names = data["names"].tolist() + if control_names != list(g["control_names"]): + raise ValueError("repair controls do not match the live action schema") + place_initial = np.asarray( + station.get("place_initial20", data["place_initial"]), dtype=float + ).copy() + if place_initial.shape != (20,) or not np.isfinite(place_initial).all(): + raise ValueError("repair place_initial must be a finite 20-vector") + live_control = np.asarray(g["native"])[g["ids"]].copy() + place_initial[18:20] = live_control[18:20] + data["place_initial"] = place_initial.copy() + + nominal_base_before = np.asarray(item_job.get("place_base"), dtype=float).copy() + actual_before = np.asarray(g["mat"](g["base"]), dtype=float) + authored_proposed_base = proposed_base.copy() + # The mobile base follows the actual floor; a source job may retain its + # pre-settling height. Navigation cannot execute a vertical base change. + proposed_base = proposed_base.copy() + proposed_base[2, 3] = actual_before[2, 3] + overrides = { + "place_base": proposed_base.tolist(), + "carry_policy": "precision_body_route_v2", + "carry_exit_candidates": [0.25, 0.45, 0.15, 0.05], + } + item_job.update(overrides) + g["data"] = data + g["end"] = place_initial.copy() + + tick_start = len(g["rows"]) + kind = str(record.get("kind", "")) + receipt = { + "schema": "ziche.multi_item_reposition_repair.v1", + "item_index": int(record["item_index"]), + "boundary": kind, + "tick_start": tick_start, + "source_proposal": str(proposal_path), + "source_proposal_sha256": proposal_sha256, + "nominal_base_before": nominal_base_before.tolist(), + "actual_base_before": actual_before.tolist(), + "proposed_base": proposed_base.tolist(), + "authored_proposed_base": authored_proposed_base.tolist(), + "base_height_policy": "measured_floor_height_no_vertical_navigation", + "gripper_commands_before": live_control[18:20].tolist(), + "pose_writes_or_resets": False, + } + + # Carry retry will plan and execute the new destination. A place-bridge + # retry consumes the updated end posture; it does not own base movement. + if kind in {"carry", "place_bridge"}: + receipt.update( + mode="proposal_update_at_planning_boundary", + motion_executed=False, + tick_end=len(g["rows"]), + actual_base_after=np.asarray(g["mat"](g["base"]), dtype=float).tolist(), + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} + + if kind != "place": + raise ValueError(f"unsupported repair boundary: {kind}") + + names = g["names"] + ids = g["ids"] + robot = g["robot"] + target_local = data["target_local"] + held_reference = np.asarray(g["held"](), dtype=float) + live_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + live_object = np.asarray(g["mat"](g["target"]), dtype=float) + live_props = g["prop_live"]() + repair_job = dict(item_job) + repair_job.update(overrides) + repair_job.pop("carry_route_seed", None) + # A qualified path is only a search seed; plan_carry rechecks every edge + # against the actual live grasp and props before issuing any motion. + if station.get("carry_route_seed") is not None: + repair_job["carry_route_seed"] = station["carry_route_seed"] + + # Plan the complete carried reposition before issuing a single command. + compact_q, primitives, carry_report = g["plan_carry"]( + g["stage"], + g["carry_robot"], + names, + live_q, + actual_before, + item_job["hand"], + item_job["target"], + target_local, + live_object, + live_props, + repair_job, + g["release_spec"], + ) + end_full = live_q.copy() + end_full[ids] = place_initial + checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + held_reference, + live_props, + ) + checker.deadline = time.monotonic() + 900 + if not checker.segment(compact_q, end_full, proposed_base, proposed_base): + raise RuntimeError( + "Proposed station posture bridge failed pre-motion geometry: " + + json.dumps(checker.last, default=str) + ) + + receipt["pre_motion_geometry"] = { + "carry_report": carry_report, + "primitive_count": len(primitives), + "compact_to_place_initial_clear": True, + "destination_check_base": proposed_base.tolist(), + "all_live_props_checked": True, + } + + # These are the only calls below that move the robot or held object. + g["carry_compact_q"] = np.asarray(compact_q, dtype=float).copy() + g["execute_primitives"](primitives, held_reference, return_motion=False) + + actual_arrival = np.asarray(g["mat"](g["base"]), dtype=float) + arrival_q = np.asarray(robot.get_joint_positions(), dtype=float).copy() + arrival_end_full = arrival_q.copy() + arrival_end_full[ids] = place_initial + arrival_held = np.asarray(g["held"](), dtype=float) + arrival_checker = g["Clearance"]( + g["stage"], + g["carry_robot"], + names, + item_job["hand"], + item_job["target"], + target_local, + arrival_held, + g["prop_live"](), + ) + arrival_checker.deadline = time.monotonic() + 900 + if not arrival_checker.segment( + arrival_q, arrival_end_full, actual_arrival, actual_arrival + ): + raise RuntimeError( + "Live arrival posture bridge failed geometry; boundary remains frozen: " + + json.dumps(arrival_checker.last, default=str) + ) + g["item_phase"]("bridge_to_place_initial") + g["blend"](place_initial, 6.0, held_reference) + + final_control = np.asarray(g["native"])[ids].copy() + if not np.allclose(final_control[18:20], live_control[18:20], atol=1e-8): + raise RuntimeError("repair changed a live gripper command") + g["q"] = np.asarray(robot.get_joint_positions(), dtype=float).copy() + g["basem"] = np.asarray(g["mat"](g["base"]), dtype=float) + g["end"] = place_initial.copy() + receipt.update( + mode="screened_carried_reposition_and_bridge", + motion_executed=True, + tick_end=len(g["rows"]), + actual_base_after=g["basem"].tolist(), + nominal_actual_xy_error_m=float( + np.linalg.norm(g["basem"][:2, 3] - proposed_base[:2, 3]) + ), + held_transform_drift_m=float( + np.linalg.norm(g["held"]()[:3, 3] - held_reference[:3, 3]) + ), + gripper_commands_after=final_control[18:20].tolist(), + arrival_geometry={ + "posture_bridge_clear": True, + "all_live_props_rechecked": True, + "actual_arrival_base": actual_arrival.tolist(), + }, + ) + path = _receipt_path(g, record, station) + if path.exists(): + raise FileExistsError(f"repair receipt already exists: {path}") + g["dump"](path, receipt) + return {"job_overrides": overrides, "receipt": str(path)} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/return_head_policy.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/return_head_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..04381392e5018fe8c6e8f7e3bebc4f4273e1d0f9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/return_head_policy.py @@ -0,0 +1,132 @@ +"""Physically recorded, collision-screened level gaze during empty-hand return.""" +from pathlib import Path +import hashlib +import numpy as np + +HEAD_JOINTS = ('idx11_head_joint1', 'idx12_head_joint2') +POLICY = 'empty_return_forward_head_v3' +BODY_PITCH_JOINT = 'idx02_body_joint2' +# Compensate torso pitch, retaining 0.5 degree margin inside native head limits. +PITCH_CLIP_RAD = tuple(np.deg2rad([-19.5, 29.5])) + +def head_target(q, names): + return (0.0, float(np.clip(-np.asarray(q)[list(names).index(BODY_PITCH_JOINT)], *PITCH_CLIP_RAD))) + +TRANSITION_SECONDS = 2.0 + + +def level_head(q, names): + result = np.asarray(q, dtype=float).copy() + for joint, value in zip(HEAD_JOINTS, head_target(q, names)): + result[list(names).index(joint)] = value + return result + + +def wrap_planner(planner): + def plan(stage, robot, names, q, base, prop_poses, next_pick_base, + next_pick_q, release, job): + from multi_motion import EmptyClearance + q = np.asarray(q, dtype=float) + base = np.asarray(base, dtype=float) + next_pick_base = np.asarray(next_pick_base, dtype=float) + next_pick_q = np.asarray(next_pick_q, dtype=float) + leveled = level_head(q, names) + next_leveled = level_head(next_pick_q, names) + check = EmptyClearance(stage, robot, names, prop_poses, + deadline_s=float(job.get('return_plan_budget_s', 900))) + if not check.segment(q, leveled, base, base): + raise RuntimeError('Return head raise is not clear: ' + str(check.last)) + if not check.segment(next_leveled, next_pick_q, next_pick_base, next_pick_base): + raise RuntimeError('Stopped shelf gaze restoration is not clear: ' + str(check.last)) + compact, primitives, report = planner(stage, robot, names, leveled, base, + prop_poses, next_pick_base, next_leveled, release, job) + if not any(p.get('bridge_before') for p in primitives): + raise RuntimeError('Level-head return requires an explicit shelf approach boundary') + report = dict(report) + report['head_policy'] = dict(policy=POLICY, joints=list(HEAD_JOINTS), + compensate_body_joint=BODY_PITCH_JOINT, pitch_clip_rad=list(PITCH_CLIP_RAD), transition_seconds=TRANSITION_SECONDS, + lift_before_table_retreat=True, includes_shelf_approach=True, + restore_source_gaze_only_after_stopped_arrival=True, + both_head_transitions_full_body_and_all_props_screened=True) + return compact, primitives, report + return plan + + +def transition(ctx, desired_head, kind): + from multi_motion import EmptyClearance + names = ctx['names']; controls = ctx['control_names']; ids = ctx['ids'] + native = ctx['native']; target = native[ids].copy() + head_ids = [controls.index(joint) for joint in HEAD_JOINTS] + desired_head = np.asarray(desired_head, dtype=float) + if desired_head.shape != (2,) or not np.isfinite(desired_head).all(): + raise ValueError('Expected two finite head joint targets') + if np.allclose(target[head_ids], desired_head, atol=1e-10, rtol=0): + return + ctx['base_vw'] = np.zeros(2) + ctx['constrain']('hold') + q0 = np.asarray(ctx['robot'].get_joint_positions(), dtype=float).copy() + q1 = q0.copy() + for joint, value in zip(HEAD_JOINTS, desired_head): + q1[names.index(joint)] = value + base = ctx['mat'](ctx['base']) + props = {path: ctx['mat'](body) for path, body in zip(ctx['prop_paths'], ctx['props'])} + check = EmptyClearance(ctx['stage'], ctx['carry_robot'], names, props, deadline_s=900) + if not check.segment(q0, q1, base, base): + raise RuntimeError('Live stopped head transition not clear: ' + str(check.last)) + tick0 = len(ctx['rows']) + before = target[head_ids].copy() + target[head_ids] = desired_head + ctx['item_phase']('empty_return') + ctx['blend'](target, TRANSITION_SECONDS) + actual = np.asarray(ctx['robot'].get_joint_positions(), dtype=float) + error = np.abs(actual[[names.index(j) for j in HEAD_JOINTS]] - desired_head) + if np.max(error) > np.deg2rad(0.1): + raise RuntimeError('Head did not reach return gaze before navigation: ' + str(error)) + receipt = dict(schema='multi_return_head_transition_v1', policy=POLICY, + item_index=ctx['item_index'], kind=kind, start_tick=tick0, + end_tick=len(ctx['rows']), head_joints=list(HEAD_JOINTS), + previous_command_rad=before.tolist(), target_rad=desired_head.tolist(), + measured_error_rad=error.tolist(), base_held=True, + all_live_props_screened=True, no_pose_writes=True, + source_path=str(Path(__file__).resolve()), + source_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest()) + ctx['dump'](ctx['out']/f"RETURN_HEAD_TRANSITION.item_{ctx['item_index']:02d}.{kind}.tick_{tick0:08d}.json", receipt) + + +def wrap_executor(ctx, executor): + def execute(primitives, held_reference=None, return_motion=False, item_job=None): + if not return_motion: + return executor(primitives, held_reference=held_reference, + return_motion=False, item_job=item_job) + # The planner screens every moving segment with this exact head pose. + if not ctx.get('RETURN_HEAD_WALKING_ACTIVE', False): + transition(ctx, head_target(ctx['native'], ctx['names']), 'raise_before_walking') + ctx['RETURN_HEAD_WALKING_ACTIVE'] = True + source_bridge = np.asarray(ctx['return_bridge_q'], dtype=float).copy() + ctx['return_bridge_q'] = level_head(source_bridge, ctx['control_names']) + try: + result = executor(primitives, held_reference=held_reference, + return_motion=True, item_job=item_job) + finally: + ctx['return_bridge_q'] = source_bridge + if any(p.get('bridge_before') for p in primitives): + # All base primitives have finished; only now look at the next item. + indices = [ctx['control_names'].index(j) for j in HEAD_JOINTS] + transition(ctx, source_bridge[indices], 'restore_pick_gaze_after_arrival') + ctx['RETURN_HEAD_WALKING_ACTIVE'] = False + return result + return execute + + +def install(ctx): + if ctx.get('RETURN_HEAD_POLICY_INSTALLED') == POLICY: + return + ctx['plan_return'] = wrap_planner(ctx['plan_return']) + ctx['execute_primitives'] = wrap_executor(ctx, ctx['execute_primitives']) + ctx['RETURN_HEAD_POLICY_INSTALLED'] = POLICY + ctx['dump'](ctx['out']/'RETURN_HEAD_POLICY.json', dict(policy=POLICY, + installed_at_control_tick=len(ctx['rows']), head_joints=list(HEAD_JOINTS), + compensate_body_joint=BODY_PITCH_JOINT, pitch_clip_rad=list(PITCH_CLIP_RAD), transition_seconds=TRANSITION_SECONDS, + restore_only_while_stopped_at_shelf=True, + implementation_path=str(Path(__file__).resolve()), + implementation_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest())) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/run_episode.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/run_episode.py new file mode 100644 index 0000000000000000000000000000000000000000..8640a2587c276dded659eb987f4c5d224d3e3816 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/run_episode.py @@ -0,0 +1,671 @@ +"""One continuous native rollout for every requested shelf-to-tray item. + +No pose writes after initial setup. Diagnostics never count as accepted episodes. +""" +import os,sys,json,math,time,traceback,gzip,subprocess,hashlib,importlib.util +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent;case=Path(sys.argv[1]);job=json.loads((case/'job.json').read_text());R=Path(os.environ.get('CHAIN_PROJECT_ROOT',str(W.parents[1]))) +items=job.get('items') or [dict(target=job['target'],hand=job['hand'],product=job.get('product'),pick_source=job.get('pick_source'),pick_base=job['initial_base'],controls_file=str(case/'controls.npz'),destination_xyz=job['destination_xyz'])] +if not items:raise ValueError('job.items must contain at least one item') +def controls_path(item): + p=Path(item.get('controls_file',case/'controls.npz'));return p if p.is_absolute() else case/p +def load_controls(item):return np.load(controls_path(item),allow_pickle=False) +data=load_controls(items[0]);target_paths=[str(item['target']) for item in items] +sys.path[:0]=[str(W),str(R/'tools/family3'),str(R/'code/family3_repair/real2sim/teleop'),str(R/'work/family3_repair/collision/python_deps')] +os.environ.setdefault('SPATIALINDEX_C_LIBRARY',str(R/'work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so')) +from isaacsim import SimulationApp +app=SimulationApp({'headless':True,'limit_cpu_threads':8}) +import omni.usd +from pxr import Usd,UsdGeom,UsdPhysics,PhysxSchema,Gf,PhysicsSchemaTools +from scipy.spatial.transform import Rotation as Rot,Slerp +from scipy.optimize import least_squares +from isaacsim.core.api import SimulationContext +from isaacsim.core.prims import SingleArticulation,SingleRigidPrim +from isaacsim.core.utils.types import ArticulationAction +from omni.physx import get_physx_simulation_interface +from navigation import PrimitiveController + +class PrecisionReturnController: + """The native stop/turn/drive law with pickup-safe terminal tolerances.""" + def __init__(self,primitive):self.primitive=primitive;self.speed=0.;self.done=False + def step(self,current,dt=1/60): + goal=np.asarray(self.primitive['goal'],dtype=float);yaw=float(current[2]);heading=np.array([math.cos(yaw),math.sin(yaw)]) + if self.primitive['kind']=='turn': + error=math.atan2(math.sin(goal[2]-yaw),math.cos(goal[2]-yaw));desired=math.copysign(min(.25,max(.003,1.5*abs(error))),error) if abs(error)>.00009 else 0. + self.speed+=float(np.clip(desired-self.speed,-.3*dt,.3*dt));self.done=abs(error)<.00009 and abs(self.speed)<.0002 + return np.array([0.,0.,self.speed]) + error=goal[:2]-np.asarray(current[:2]);along=float(error@heading);lateral=float(error@np.array([-heading[1],heading[0]])) + if abs(lateral)>.015:raise RuntimeError(f'Straight drive lateral tracking error {lateral:.6f} m; replan instead of strafing') + if abs(math.atan2(math.sin(goal[2]-yaw),math.cos(goal[2]-yaw)))>.01:raise RuntimeError('Straight drive heading drift; stop and replan') + desired=math.copysign(min(.18,max(.0001,min(1.5*abs(along),.8*math.sqrt(2*.12*abs(along))))),along) if abs(along)>.0002 else 0. + self.speed+=float(np.clip(desired-self.speed,-.12*dt,.12*dt));self.done=abs(along)<.0002 and abs(self.speed)<.001 + return np.r_[heading*self.speed,0.] + +PRECISION_RETURN_POLICY=dict( + controller='PrecisionReturnController',scope='primitives_with_return_stage_only', + turn_deadband_rad=.00009,turn_done_error_rad=.00009,turn_done_speed_rad_s=.0002,turn_min_speed_rad_s=.003, + turn_min_speed_basis='native_static_friction_microturn_recoveries_layout07_layout10_0p003_rad_s', + drive_deadband_m=.0002,drive_done_error_m=.0002,drive_done_speed_m_s=.001,drive_min_speed_m_s=.0001, + speed_and_acceleration_caps_unchanged=True,static_friction_endpoint_acceptance_m_rad=[.0005,.0001],arrival_translation_gate_m=.0005,arrival_yaw_gate_rad=.0001) +out=case/'rollout';out.mkdir(exist_ok=False) +def dump(p,d): + tmp=p.with_suffix(p.suffix+'.tmp');tmp.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n');tmp.replace(p) +def archive_exact(folder,digest,suffix,raw): + folder.mkdir(parents=True,exist_ok=True);path=folder/f'{digest}{suffix}' + if path.exists(): + if path.read_bytes()!=raw:raise RuntimeError('Hash archive collision or corrupted prior archive: '+str(path)) + else: + tmp=folder/f'.{digest}.{os.getpid()}.tmp';tmp.write_bytes(raw);tmp.replace(path) + return str(path) +def status(state,**extra):dump(case/'STATUS.json',dict(status=state,phase=phase,item_index=item_index,completed_objects=completed_objects,control_ticks=len(rows),updated=time.time(),base_pose=pose(base).tolist() if 'base' in globals() else None,target_pose=pose(target).tolist() if 'target' in globals() else None,**extra)) +phase='initializing';item_index=-1;completed_objects=[];item_events=[];planning_waits=[] +rows=[];target_rows=[];target_all_rows=[];active_target_rows=[];item_index_rows=[];base_rows=[];measured=[];commands=[];base_commands=[];phases=[];nav_modes=[];nav_anchors=[];times=[];joint_velocities=[];link_rows=[] +omni.usd.get_context().open_stage(job['scene']);stage=omni.usd.get_context().get_stage() +# Released rug surfaces are visual floor dressing, not physical step obstacles. +# Preserve those authored semantics; the real floor collider remains enabled. +released=Usd.Stage.Open(job['layout']);floor_dressing=[] +rug=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') +if rug: + for p in Usd.PrimRange(rug): + authored=released.GetPrimAtPath(p.GetPath()) + if p.HasAPI(UsdPhysics.CollisionAPI) and authored and not authored.HasAPI(UsdPhysics.CollisionAPI): + UsdPhysics.CollisionAPI(p).GetCollisionEnabledAttr().Set(False);floor_dressing.append(str(p.GetPath())) +dump(out/'RELEASED_FLOOR_DRESSING.json',dict(policy='preserve_original_nonphysical_floor_dressing',paths=floor_dressing,real_floor_collision_retained=True)) +# The old fixed task root joint must not make the articulation fixed-base. +root_anchor=UsdPhysics.FixedJoint(stage.GetPrimAtPath('/World/Robot/base_link/FixedJoint')) +root_anchor.CreateExcludeFromArticulationAttr(True);root_anchor.GetJointEnabledAttr().Set(False) +# A free articulation otherwise picks a graph-center link as its root. +stage.GetPrimAtPath('/World/Robot').RemoveAPI(UsdPhysics.ArticulationRootAPI) +base_prim=stage.GetPrimAtPath('/World/Robot/base_link');UsdPhysics.ArticulationRootAPI.Apply(base_prim) +PhysxSchema.PhysxArticulationAPI.Apply(base_prim).CreateEnabledSelfCollisionsAttr(True) +# Navigation uses native constraints and velocity drives, not root teleportation. +nav_joints={};nav_drives={} +for kind,cls,axis,drive_name in [('turn',UsdPhysics.RevoluteJoint,'Z','angular'),('drive',UsdPhysics.PrismaticJoint,'X','linear'),('hold',UsdPhysics.FixedJoint,None,None)]: + joint=cls.Define(stage,'/World/ChainNavigation_'+kind);joint.CreateBody1Rel().SetTargets(['/World/Robot/base_link']);joint.CreateExcludeFromArticulationAttr(True);joint.CreateJointEnabledAttr(False);joint.CreateLocalPos0Attr(Gf.Vec3f(0));joint.CreateLocalRot0Attr(Gf.Quatf(1));joint.CreateLocalPos1Attr(Gf.Vec3f(0));joint.CreateLocalRot1Attr(Gf.Quatf(1)) + if axis: + joint.CreateAxisAttr(axis);joint.CreateLowerLimitAttr(-170. if kind=='turn' else -15.);joint.CreateUpperLimitAttr(170. if kind=='turn' else 15.) + d=UsdPhysics.DriveAPI.Apply(joint.GetPrim(),drive_name);d.CreateTypeAttr('force');d.CreateStiffnessAttr(0.);d.CreateDampingAttr(100000.);d.CreateMaxForceAttr(100000.);d.CreateTargetVelocityAttr(0.);nav_drives[kind]=d + nav_joints[kind]=joint +for p in stage.Traverse(): + if p.HasAPI(UsdPhysics.RigidBodyAPI):PhysxSchema.PhysxContactReportAPI.Apply(p).CreateThresholdAttr().Set(0.) +from tray_appearance import apply as apply_tray_appearance +dump(out/'TRAY_APPEARANCE.json',apply_tray_appearance(stage,job)) +# All exact runtime settings are exported before stepping. +for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')): + if p.GetName() in ['idx41_gripper_l_outer_joint1','idx81_gripper_r_outer_joint1']: + d=UsdPhysics.DriveAPI.Apply(p,'angular');d.GetMaxForceAttr().Set(80.) +from robot_collision_profile import audit_full_robot_collision_profile +profile=audit_full_robot_collision_profile(stage);dump(out/'ROBOT_COLLISION_PROFILE.json',profile) +params={p.GetName():{a.GetName():str(a.Get()) for a in p.GetAttributes() if a.GetName().startswith(('physics:','physx','drive:'))} for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')) if p.IsA(UsdPhysics.Joint)} +dump(out/'robot_and_camera_parameters.json',params);stage.GetRootLayer().Export(str(out/'scene.usda')) +sim=SimulationContext(physics_dt=1/240,rendering_dt=1/60);sim.initialize_physics();sim.play() +assert abs(sim.get_physics_dt()-1/240)<1e-12, 'Physics timestep must be 240 Hz' +dump(out/'PHYSICS_CLOCK.json',dict(physics_dt_s=sim.get_physics_dt(),physics_hz=1/sim.get_physics_dt(),substeps_per_control=4,control_hz=60,training_hz=20)) +# Save runtime units/up-axis after SimulationContext authors them. +stage.GetRootLayer().Export(str(out/'scene.usda')) +robot=SingleArticulation('/World/Robot/base_link');robot.initialize();names=list(robot.dof_names);control_names=data['names'].tolist();ids=np.array([names.index(n) for n in control_names]) +native=np.zeros(len(names));source_names=job['source_dof_names'];sourceq=np.asarray(job['source_initial_q']) +for i,n in enumerate(names):native[i]=sourceq[source_names.index(n)] if n in source_names else 0. +native[ids]=data['initial'];base0=np.asarray(job['initial_base']);bq=Rot.from_matrix(base0[:3,:3]).as_quat();robot.set_world_pose(base0[:3,3],np.r_[bq[3],bq[:3]]);robot.set_joint_positions(native);robot.set_joint_velocities(np.zeros(len(names))) +efforts=np.asarray(robot.get_articulation_controller().get_max_efforts()).reshape(-1);drive_readback={n:float(efforts[names.index(n)]) for n in control_names[18:]} +assert all(abs(v-80.)<1e-5 for v in drive_readback.values()),drive_readback +dump(out/'LIVE_DRIVE_READBACK.json',drive_readback) +params['dof_names']=names;params['action_names']=control_names+['base_forward_m_s','base_yaw_rate_rad_s'];params['runtime_gripper_max_effort_Nm']=drive_readback;params['cameras']={} +for key,path in [('top_head','/World/Robot/head_link2/Head_Camera'),('hand_left','/World/Robot/gripper_l_base_link/Left_Camera'),('hand_right','/World/Robot/gripper_r_base_link/Right_Camera')]: + camera=stage.GetPrimAtPath(path);params['cameras'][key]=dict(path=path,attributes={a.GetName():str(a.Get()) for a in camera.GetAttributes()},world_extrinsics='camera_extrinsics_20hz.npz') +dump(out/'robot_and_camera_parameters.json',params) + +base=robot +target_objects=[] +for path in target_paths: + rb=SingleRigidPrim(path);rb.initialize();target_objects.append(rb) +target=target_objects[0];hand=SingleRigidPrim('/World/Robot/gripper_'+items[0]['hand']+'_base_link');hand.initialize() +link_paths=[str(p.GetPath()) for p in Usd.PrimRange(stage.GetPrimAtPath('/World/Robot')) if p.HasAPI(UsdPhysics.RigidBodyAPI)];links=[] +for path in link_paths: + rb=robot if path=='/World/Robot/base_link' else SingleRigidPrim(path) + if rb is not robot:rb.initialize() + links.append(rb) +prop_paths=[str(p.GetPath()) for p in stage.Traverse() if not str(p.GetPath()).startswith('/World/Robot/') and p.HasAPI(UsdPhysics.RigidBodyAPI) and UsdPhysics.RigidBodyAPI(p).GetRigidBodyEnabledAttr().Get()] +props=[];prop_rows=[] +for path in prop_paths: + rb=SingleRigidPrim(path);rb.initialize();props.append(rb) +def pose(rb): + p,q=rb.get_world_pose();return np.r_[p,q[1:],q[0]] +def mat(rb): + p=pose(rb);m=np.eye(4);m[:3,:3]=Rot.from_quat(p[3:]).as_matrix();m[:3,3]=p[:3];return m +def base_state(): + p=pose(base);return np.r_[p[:2],Rot.from_quat(p[3:]).as_euler('xyz')[2]] +def held():return np.linalg.inv(mat(hand))@mat(target) +requested_initial_target_pose=np.stack([pose(rb) for rb in target_objects]) +dump(out/'TARGET_INITIAL_POSES.json',dict(target_paths=target_paths,pose_order='x,y,z,qx,qy,qz,qw',target_pose=requested_initial_target_pose.tolist(),capture='before_episode_motion')) +nav_mode='hold';base_vw=np.zeros(2);anchor=stage.GetPrimAtPath('/World/Robot/base_link/FixedJoint');anchor.GetAttribute('physics:jointEnabled').Set(False) +def constrain(kind,recorded_anchor=None): + global nav_mode,nav_anchor + nav_mode=kind;p=pose(base) if recorded_anchor is None else np.asarray(recorded_anchor);nav_anchor=p.copy() + for j in nav_joints.values():j.GetJointEnabledAttr().Set(False) + j=nav_joints[kind];j.GetLocalPos0Attr().Set(Gf.Vec3f(*map(float,p[:3])));j.GetLocalRot0Attr().Set(Gf.Quatf(float(p[6]),Gf.Vec3f(*map(float,p[3:6]))));j.GetJointEnabledAttr().Set(True) + get_physx_simulation_interface().flush_changes() +constrain('hold') +assert np.linalg.norm(pose(base)[:3]-base0[:3,3])<.002,('Initial physical base does not match requested station',pose(base),base0[:3,3]) +# Settle the real native scene under the exact first recorded command before any +# dataset row, contact, or camera frame is captured. This is ordinary physics: +# no pose write, reset, frozen body, or hidden control row occurs here. +PREROLL_HZ=60;PREROLL_WINDOW=30;PREROLL_MAX_SAMPLES=60*PREROLL_HZ +PREROLL_THRESHOLDS=dict(prop_translation_m=0.00005,prop_rotation_rad=float(np.deg2rad(.02)), + robot_translation_m=0.00005,robot_rotation_rad=float(np.deg2rad(.02)), + camera_translation_m=0.00005,camera_rotation_rad=float(np.deg2rad(.01)),passive_finger_projected_motion_px=0.25) +camera_link_indices=[link_paths.index(p) for p in ['/World/Robot/head_link2','/World/Robot/gripper_l_base_link','/World/Robot/gripper_r_base_link']] +from preroll_stability import build_projection_model,evaluate_projection +projection_model=build_projection_model(stage,link_paths);last_projection=None +pre_q=[];pre_qv=[];pre_base=[];pre_link=[];pre_prop=[];pre_metrics=[] +initial_prop_pose_before_preroll=np.stack([pose(rb) for rb in props]);initial_robot_link_pose_before_preroll=np.stack([pose(rb) for rb in links]) +initial_prop_maxima=dict(translation_m=0.,downward_m=0.,rotation_rad=0.) +def check_initial_props(): + current=np.asarray(pre_prop[-1],dtype=np.float64);initial=np.asarray(initial_prop_pose_before_preroll,dtype=np.float64) + displacement=np.linalg.norm(current[:,:3]-initial[:,:3],axis=1);downward=initial[:,2]-current[:,2] + a=current[:,3:]/np.linalg.norm(current[:,3:],axis=1,keepdims=True);b=initial[:,3:]/np.linalg.norm(initial[:,3:],axis=1,keepdims=True) + angle=2*np.arccos(np.clip(np.abs(np.sum(a*b,axis=1)),0.,1.)) + initial_prop_maxima['translation_m']=max(initial_prop_maxima['translation_m'],float(displacement.max(initial=0.))) + initial_prop_maxima['downward_m']=max(initial_prop_maxima['downward_m'],float(downward.max(initial=0.))) + initial_prop_maxima['rotation_rad']=max(initial_prop_maxima['rotation_rad'],float(angle.max(initial=0.))) + bad=np.flatnonzero((displacement>.02)|(downward>.01)|(angle>np.deg2rad(15.))) + if len(bad): + failures=[dict(path=prop_paths[int(i)],translation_m=float(displacement[i]),downward_m=float(downward[i]),rotation_rad=float(angle[i])) for i in bad] + dump(out/'INITIAL_PROP_NO_FALL.json',dict(passed=False,physics_sample=len(pre_prop),failures=failures,thresholds=dict(translation_m=.02,downward_m=.01,rotation_deg=15.),all_requested_and_nonrequested_props_included=True)) + raise RuntimeError('Initial scene prop moved or fell before capture: '+json.dumps(failures)) + +def pose_span(a): + if a.shape[1]==0:return 0.,0. + xyz=np.asarray(a[:,:,:3],dtype=np.float64);quat=np.asarray(a[:,:,3:],dtype=np.float64) + quat/=np.linalg.norm(quat,axis=-1,keepdims=True) + dx=xyz[:,None,:,:]-xyz[None,:,:,:] + trans=float(np.linalg.norm(dx,axis=-1).max()) + dots=np.abs(np.einsum('tni,sni->tsn',quat,quat));dots=np.clip(dots,-1.,1.) + angle=float((2*np.arccos(dots)).max()) + return trans,angle +def window_metrics(): + l=np.asarray(pre_link[-PREROLL_WINDOW:]);p=np.asarray(pre_prop[-PREROLL_WINDOW:]) + m=geometry_metrics(l,p) + q=np.asarray(pre_q[-PREROLL_WINDOW:]);qv=np.asarray(pre_qv[-PREROLL_WINDOW:]) + m.update(joint_span=float(np.ptp(q,axis=0).max()),joint_speed_max=float(np.abs(qv).max())) + return m +def geometry_metrics(l,p): + lt,lr=pose_span(l);pt,pr=pose_span(p);ct,cr=pose_span(l[:,camera_link_indices]);dt,dr=pose_span(l[:,projection_model['driven_indices']]) + return dict(robot_link_translation_span_m=lt,robot_link_rotation_span_rad=lr, + prop_translation_span_m=pt,prop_rotation_span_rad=pr, + camera_link_translation_span_m=ct,camera_link_rotation_span_rad=cr, + driven_robot_link_translation_span_m=dt,driven_robot_link_rotation_span_rad=dr) +def stable(m): + t=PREROLL_THRESHOLDS + return (m['driven_robot_link_translation_span_m']<=t['robot_translation_m'] and m['driven_robot_link_rotation_span_rad']<=t['robot_rotation_rad'] + and m['prop_translation_span_m']<=t['prop_translation_m'] and m['prop_rotation_span_rad']<=t['prop_rotation_rad'] + and m['camera_link_translation_span_m']<=t['camera_translation_m'] and m['camera_link_rotation_span_rad']<=t['camera_rotation_rad']) +phase='physics_preroll_unrecorded' +if len(sys.argv)>2: + preroll_source=np.load(sys.argv[2]);preroll_source_ids=[preroll_source['dof_names'].tolist().index(n) for n in control_names] + initial_command=np.asarray(preroll_source['native_target'][0,preroll_source_ids],dtype=float).copy() +else:initial_command=np.asarray(data['initial'],dtype=float).copy() +native[ids]=initial_command +try: + for sample in range(PREROLL_MAX_SAMPLES-3): + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + if len(pre_q)>=PREROLL_WINDOW: + m=window_metrics();m['sample']=sample+1;m['time_s']=(sample+1)/PREROLL_HZ;pre_metrics.append(m) + # Exact mesh projection is intentionally sparse: it is expensive and passive + # solver jitter changes slowly. Recheck at most once per 0.5 s, while the + # strict prop/camera/driven-link metrics remain evaluated every sample. + if stable(m) and (last_projection is None or (sample+1)%PREROLL_WINDOW==0): + last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + if last_projection['status']=='PASSED':break + else:raise RuntimeError('Native scene did not meet startup stability thresholds within 60 seconds') + evidence=dict(status='PASSED',physics_hz=240,sample_hz=PREROLL_HZ,steps=4*len(pre_q),duration_s=len(pre_q)/PREROLL_HZ, + stability_window_s=PREROLL_WINDOW/PREROLL_HZ,thresholds=PREROLL_THRESHOLDS,final_window=pre_metrics[-1],passive_finger_projection=last_projection, + exact_initial_command_sha256=hashlib.sha256(initial_command.tobytes()).hexdigest(),recorded_rows=0, + contact_capture_started=False,camera_capture_started=False,no_pose_writes_or_resets=True, + initial_snapshot_semantics='Immediate native readback after scene setup and hold constraint, before the first preroll physics step; no pose write/reset.', + initial_prop_pose_before_preroll_sha256=hashlib.sha256(initial_prop_pose_before_preroll.tobytes()).hexdigest()) + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics])) + dump(out/'PREROLL_EVIDENCE.json',evidence) +except Exception as exc: + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics])) + dump(out/'PREROLL_EVIDENCE.json',dict(status='FAILED',error=repr(exc),physics_hz=240,duration_s=len(pre_q)/PREROLL_HZ, + thresholds=PREROLL_THRESHOLDS,final_window=pre_metrics[-1] if pre_metrics else None,passive_finger_projection=last_projection, + recorded_rows=0,contact_capture_started=False,camera_capture_started=False)) + status('FAILED',error='Physics preroll: '+repr(exc));app.close();raise + +contact_stream=None +contact_callback_rows=0;recent_support_contacts={path:[] for path in target_paths} +recent_grasp_contacts={path:{'inner':[],'outer':[],'environment':[]} for path in target_paths} +def on_contact(headers,points): + global contact_callback_rows + for h in headers: + if not h.num_contact_data:continue + actors=[str(PhysicsSchemaTools.intToSdfPath(x)) for x in [h.actor0,h.actor1]] + colliders=[str(PhysicsSchemaTools.intToSdfPath(x)) for x in [h.collider0,h.collider1]] + if not any(a.startswith('/World/Robot/') or any(a==p or a.startswith(p+'/') for p in target_paths) for a in actors+colliders):continue + selected=points[h.contact_data_offset:h.contact_data_offset+h.num_contact_data] + row=dict(tick=len(rows)+1,phase=phase,item_index=item_index,active_target_path=target_paths[item_index] if item_index>=0 else '',actors=actors,colliders=colliders,points=int(h.num_contact_data),min_separation_m=min(float(p.separation) for p in selected),impulse_sum=[float(sum(p.impulse[k] for p in selected)) for k in range(3)]);contact_stream.write(json.dumps(row)+'\n');contact_callback_rows+=1 + for requested in target_paths: + touches_target=any(a==requested or a.startswith(requested+'/') for a in actors+colliders) + touches_tray=any(a=='/World/Assets/Tray' or a.startswith('/World/Assets/Tray/') for a in actors+colliders) + if touches_target and touches_tray:recent_support_contacts[requested].append(len(rows)+1) + if item_index>=0: + requested=target_paths[item_index];gripper_prefix='/World/Robot/gripper_'+items[item_index]['hand'] + touches_target=any(a==requested or a.startswith(requested+'/') for a in actors+colliders) + if touches_target: + evidence=recent_grasp_contacts[requested];tick=len(rows)+1 + if any(a==gripper_prefix+'_inner_link4' or a.startswith(gripper_prefix+'_inner_link4/') for a in actors+colliders):evidence['inner'].append(tick) + if any(a==gripper_prefix+'_outer_link4' or a.startswith(gripper_prefix+'_outer_link4/') for a in actors+colliders):evidence['outer'].append(tick) + other_nonrobot=any(a and not (a==requested or a.startswith(requested+'/') or a.startswith('/World/Robot/')) for a in actors+colliders) + if other_nonrobot:evidence['environment'].append(tick) + for values in evidence.values(): + if len(values)>2000:del values[:-1000] +subscription=None +# Capture cameras from the same physical simulation and the same sampling ticks. +writers={};renderers={} +if os.environ.get('CHAIN_RENDER','1')=='1': + import omni.replicator.core as rep + import subprocess + for key,path,w,h in [('top_head','/World/Robot/head_link2/Head_Camera',1280,800),('hand_left','/World/Robot/gripper_l_base_link/Left_Camera',848,480),('hand_right','/World/Robot/gripper_r_base_link/Right_Camera',848,480)]: + product=rep.create.render_product(path,(w,h));ann=rep.AnnotatorRegistry.get_annotator('rgb');ann.attach([product]);renderers[key]=(product,ann,w,h) + # Prime the renderer after stability. Replicator's annotator graph requires a + # physics update after attachment before its first safe read, so three further + # unrecorded hold samples are included in the preroll evidence below. + for _ in range(12):sim.render() + for _ in range(3): + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera warmup frame missing') + m=window_metrics() + last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + warmup_samples=3 + while not stable(m) or last_projection['status']!='PASSED': + if len(pre_q)>=PREROLL_MAX_SAMPLES:raise RuntimeError('Camera priming did not recover strict startup stability within 60 seconds') + robot.apply_action(ArticulationAction(joint_positions=initial_command,joint_indices=ids)) + for _ in range(4):sim.step(render=False) + pre_q.append(np.asarray(robot.get_joint_positions()).copy());pre_qv.append(np.asarray(robot.get_joint_velocities()).copy()) + pre_base.append(pose(base));pre_link.append(np.stack([pose(rb) for rb in links]));pre_prop.append(np.stack([pose(rb) for rb in props]));check_initial_props() + warmup_samples+=1;sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera rewarm frame missing') + m=window_metrics() + if stable(m):last_projection=evaluate_projection(np.asarray(pre_link[-PREROLL_WINDOW:]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + m['passive_finger_max_projected_motion_px']=last_projection['max_projected_motion_px'] + evidence.update(steps=4*len(pre_q),duration_s=len(pre_q)/PREROLL_HZ,final_window=m,camera_warmup_physics_samples=warmup_samples, + discarded_render_reads=warmup_samples,passive_finger_projection=last_projection) + np.savez_compressed(out/'PREROLL_EVIDENCE.npz',q=np.asarray(pre_q),joint_velocity=np.asarray(pre_qv),base_pose=np.asarray(pre_base), + robot_link_paths=np.asarray(link_paths),robot_link_pose=np.asarray(pre_link),prop_paths=np.asarray(prop_paths), + prop_pose=np.asarray(pre_prop),initial_prop_pose_before_preroll=initial_prop_pose_before_preroll, + initial_robot_link_pose_before_preroll=initial_robot_link_pose_before_preroll,initial_command=initial_command,metrics_json=np.asarray([json.dumps(x) for x in pre_metrics]+[json.dumps(m)])) + dump(out/'PREROLL_EVIDENCE.json',evidence) + for key,(_,ann,w,h) in renderers.items(): + writers[key]=subprocess.Popen(['ffmpeg','-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{w}x{h}','-r','20','-i','-','-c:v','libx264','-preset','fast','-threads','2','-crf','21','-pix_fmt','yuv420p',str(out/(key+'.mp4'))],stdin=subprocess.PIPE) + dump(out/'CAMERA_WARMUP.json',dict(status='PASSED',after_physics_stability=True,discarded_render_reads=warmup_samples,render_only_warmups=12, + unrecorded_hold_samples=warmup_samples,captured_frames=0,final_stability_window=m)) +dump(out/'INITIAL_PROP_NO_FALL.json',dict(passed=True,physics_samples=len(pre_prop),maxima=initial_prop_maxima,thresholds=dict(translation_m=.02,downward_m=.01,rotation_deg=15.),all_requested_and_nonrequested_props_included=True)) +contact_stream=gzip.open(out/'contacts.jsonl.gz','wt',compresslevel=3) +subscription=get_physx_simulation_interface().subscribe_contact_report_events(on_contact) +camera_sample_ticks=[];camera_sample_extrinsics=[] +startup_frames=out/'startup_frames';startup_frames.mkdir(exist_ok=True) +def capture_camera_pose(): + transforms=[] + for c in projection_model['cameras']: + pp=pose(links[c['link_index']]);m=np.eye(4,dtype=np.float64);m[:3,:3]=Rot.from_quat(pp[3:]).as_matrix();m[:3,3]=pp[:3] + transforms.append(m@c['relative']) + return np.asarray(transforms,dtype=np.float64) + +def write_startup_report(): + first_n=min(30,len(link_rows));first_m=geometry_metrics(np.asarray(link_rows[:first_n]),np.asarray(prop_rows[:first_n]));first_projection=evaluate_projection(np.asarray(link_rows[:first_n]),projection_model,PREROLL_THRESHOLDS['passive_finger_projected_motion_px']) + passed=bool(first_n==30 and stable(first_m) and last_projection and last_projection['status']=='PASSED' and first_projection['status']=='PASSED') + report=dict(status='PASSED' if passed else 'FAILED',passed=passed,threshold_px=PREROLL_THRESHOLDS['passive_finger_projected_motion_px'],only_passive_distal_fingers=True,passive_path_pattern='/gripper_[lr]_(inner|outer)_link[234]$',strict_thresholds=PREROLL_THRESHOLDS, + windows=[dict(label='final_preroll_0p5s',row_range=[-30,-1],strict_geometry=evidence['final_window'],passive_projection=last_projection,passed=bool(stable(evidence['final_window']) and last_projection['status']=='PASSED')),dict(label='first_retained_30_control_rows',row_range=[0,first_n-1],strict_geometry=first_m,passive_projection=first_projection,passed=bool(first_n==30 and stable(first_m) and first_projection['status']=='PASSED'))], + evaluated_passive_paths=[b['path'] for b in projection_model['bodies']],evaluated_driven_paths=[link_paths[i] for i in projection_model['driven_indices']],evaluated_cameras=[c['path'] for c in projection_model['cameras']],scene_source=str(Path(job['scene']).resolve()),scene_sha256=hashlib.sha256(Path(job['scene']).read_bytes()).hexdigest(),projection_implementation=str((W/'preroll_stability.py').resolve()),projection_implementation_sha256=hashlib.sha256((W/'preroll_stability.py').read_bytes()).hexdigest()) + dump(out/'PASSIVE_FINGER_PROJECTION.json',report) + return passed + +def advance(q): + global native + q=np.asarray(q,dtype=float);assert q.shape==(20,) and np.isfinite(q).all() + if q[14]<-1e-6 or q[14]>.55+1e-6 or np.any(q[18:]<0) or np.any(q[18:]>np.pi/4+1e-6):raise ValueError('Action exceeds configured lift/grip limit') + native[ids]=q + robot.apply_action(ArticulationAction(joint_positions=q,joint_indices=ids)) + if nav_mode=='drive':nav_drives['drive'].GetTargetVelocityAttr().Set(float(base_vw[0])) + elif nav_mode=='turn':nav_drives['turn'].GetTargetVelocityAttr().Set(float(np.rad2deg(base_vw[1]))) + for _ in range(4):sim.step(render=False) + applied=np.asarray(robot.get_applied_action().joint_positions) + assert np.allclose(applied[ids],q,atol=1e-6), 'Applied target readback mismatch' + tick=len(rows)+1;actual=robot.get_joint_positions();bp=pose(base);tp=pose(target);all_targets=np.stack([pose(rb) for rb in target_objects]) + nav_modes.append(nav_mode);nav_anchors.append(nav_anchor.copy());rows.append(tick);measured.append(actual.copy());commands.append(native.copy());joint_velocities.append(robot.get_joint_velocities().copy());base_rows.append(bp);target_rows.append(tp);target_all_rows.append(all_targets);active_target_rows.append(target_paths[item_index] if item_index>=0 else '');item_index_rows.append(item_index);base_commands.append(base_vw.copy());phases.append(phase);link_rows.append(np.stack([pose(rb) for rb in links]));prop_rows.append(np.stack([pose(rb) for rb in props])) + if tick%3==0 and writers: + camera_sample_ticks.append(tick);camera_sample_extrinsics.append(capture_camera_pose()) + sim.render() + for key,(_,ann,w,h) in renderers.items(): + rgb=np.asarray(ann.get_data()) + if rgb.shape[:2]!=(h,w):raise RuntimeError('Camera frame missing at training tick') + writers[key].stdin.write(np.ascontiguousarray(rgb[:,:,:3]).tobytes()) + if tick<=30: + from PIL import Image + Image.fromarray(np.ascontiguousarray(rgb[:,:,:3])).save(startup_frames/f'{key}_{tick//3-1:02d}.jpg',quality=92) + if tick==30 and not write_startup_report():raise RuntimeError('First recorded 0.5 seconds failed strict stable-start gate') + if tick%60==0:status('RUNNING_PHYSICS') + if not np.isfinite(actual).all() or not np.isfinite(tp).all():raise RuntimeError('Nonfinite native physics state') + +def blend(end,seconds,held_reference=None): + start=native[ids].copy();end=np.asarray(end).copy();duration=max(seconds,abs(end[14]-start[14])*1.875/.1,abs(end[15]-start[15])*1.875/.5,float(np.max(abs(end[:14]-start[:14])))*1.875/.35) + for i in range(math.ceil(duration*60)): + u=(i+1)/math.ceil(duration*60);a=10*u**3-15*u**4+6*u**5;advance(start+a*(end-start)) + if held_reference is not None and np.linalg.norm(held()[:3,3]-held_reference[:3,3])>.025:raise RuntimeError('Target slipped during '+phase) + +def item_phase(name): + global phase + phase=f'item_{item_index:02d}_{name}' + +def full_q(control_q,control_data): + q=np.asarray(robot.get_joint_positions(),dtype=float).copy();cn=control_data['names'].tolist() + if cn!=control_names:raise ValueError('Every controls file must use the same ordered 20 action names') + q[[names.index(n) for n in cn]]=np.asarray(control_q,dtype=float);return q + +def grasp_contact_gate(path,item_job): + window_ticks=30;minimum_bilateral=max(15,int(item_job.get('pick_grasp_contact_min_bilateral_ticks',15)));tail_ticks=3;environment_clear_ticks=15 + if minimum_bilateral>window_ticks:raise ValueError('Invalid pickup bilateral grasp-contact gate configuration') + end=len(rows);start=max(1,end-window_ticks+1);tail_start=max(start,end-tail_ticks+1);environment_start=max(1,end-environment_clear_ticks+1);raw=recent_grasp_contacts[path] + inner={int(t) for t in raw['inner'] if start<=int(t)<=end};outer={int(t) for t in raw['outer'] if start<=int(t)<=end};bilateral=sorted(inner&outer);tail_bilateral=[t for t in bilateral if t>=tail_start];environment=sorted({int(t) for t in raw['environment'] if environment_start<=int(t)<=end}) + evidence=dict(active_target=path,active_hand=item_job['hand'],gripper_prefix='/World/Robot/gripper_'+item_job['hand'],window_control_ticks=window_ticks,window_start_tick=start,window_end_tick=end,inner_contact_ticks=sorted(inner),outer_contact_ticks=sorted(outer),simultaneous_bilateral_contact_ticks=bilateral,simultaneous_bilateral_contact_tick_count=len(bilateral),minimum_simultaneous_bilateral_ticks=minimum_bilateral,final_tail_control_ticks=tail_ticks,final_tail_bilateral_ticks=tail_bilateral,environment_clear_control_ticks=environment_clear_ticks,environment_contact_ticks=environment,native_contact_required=True) + if len(bilateral)xy_gate or yaw_error>yaw_gate:raise RuntimeError('Blocked navigation requires a screened live-state recovery to its endpoint: '+json.dumps(metrics,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))) + return metrics + # Include this measured failure in the repair key so a prior repair file can + # never be consumed for a later partially executed primitive. + boundary_kind=('return_motion' if return_motion else 'carry_motion')+'_'+failure_sha[:12] + recovered=planning_boundary(boundary_kind,item_job,endpoint_call) + dump(out/f'NAVIGATION_MOTION_RECOVERY.item_{item_index:02d}.{failure_sha[:12]}.json',recovered) + return recovered + +def execute_primitives(primitives,held_reference=None,return_motion=False,item_job=None): + global base_vw + if item_job is None:item_job=globals().get('item_job') + if not isinstance(item_job,dict) or item_index<0 or item_index>=len(target_paths) or item_job.get('target')!=target_paths[item_index]:raise ValueError('execute_primitives requires the active item_job matching item_index and target') + for k,p in enumerate(primitives): + if return_motion and p.get('bridge_before'): + base_vw=np.zeros(2);constrain('hold');blend(return_bridge_q,4.) + if not return_motion and k==1: + base_vw=np.zeros(2);constrain('hold');item_phase('carry_raise') + raised=native[ids].copy();raised[14]=.55;blend(raised,3.,held_reference) + item_phase('carry_compact');compact=native[ids].copy();compact[:14]=carry_compact_q[ids][:14];blend(compact,4.,held_reference) + if return_motion:item_phase('empty_return') + else:item_phase('shelf_exit_reverse' if k==0 else 'forward_navigation') + constrain(p['kind']);precision=bool(return_motion and p.get('return_stage'));controller=PrecisionReturnController(p) if precision else PrimitiveController(p);last_progress=base_state().copy();stalled_ticks=0 + primitive_complete=False;failure_reason=None + for _ in range(90*60): + cur=base_state() + try:v=controller.step(cur) + except Exception as exc: + failure_reason='controller_error: '+repr(exc);break + base_vw=np.array([np.cos(cur[2])*v[0]+np.sin(cur[2])*v[1],v[2]]);advance(native[ids]);post=base_state() + if held_reference is not None and np.linalg.norm(held()[:3,3]-held_reference[:3,3])>.025:raise RuntimeError('Target slipped in navigation') + if controller.done: + if precision: + goal=np.asarray(p['goal'],dtype=float);xy_error=float(np.linalg.norm(post[:2]-goal[:2]));yaw_error=float(abs(math.atan2(math.sin(post[2]-goal[2]),math.cos(post[2]-goal[2])))) + endpoint=dict(schema='ziche.precision_return_endpoint.v1',primitive_index=k,primitive=p,base=post.tolist(),xy_error_m=xy_error,yaw_error_rad=yaw_error,xy_gate_m=PRECISION_RETURN_POLICY['arrival_translation_gate_m'],yaw_gate_rad=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'],measured_after_advance=True) + dump(out/f'PRECISION_RETURN_ENDPOINT.item_{item_index:02d}.primitive_{k:02d}.tick_{len(rows):08d}.json',endpoint) + if xy_error>PRECISION_RETURN_POLICY['arrival_translation_gate_m'] or yaw_error>PRECISION_RETURN_POLICY['arrival_yaw_gate_rad']: + failure_reason='precision_controller_done_outside_measured_endpoint_gate: '+json.dumps(endpoint);break + primitive_complete=True;break + moved=np.linalg.norm(post[:2]-last_progress[:2]) if p['kind']=='drive' else abs(math.atan2(math.sin(post[2]-last_progress[2]),math.cos(post[2]-last_progress[2]))) + progress_epsilon=(.00001 if p['kind']=='drive' else .000002) if precision else .003 + if moved>progress_epsilon:last_progress=post.copy();stalled_ticks=0 + else:stalled_ticks+=1 + if stalled_ticks>=6*60: + if precision: + terminal_state=base_state();goal=np.asarray(p['goal'],dtype=float);xy_error=float(np.linalg.norm(terminal_state[:2]-goal[:2]));yaw_error=float(abs(math.atan2(math.sin(terminal_state[2]-goal[2]),math.cos(terminal_state[2]-goal[2])))) + within_terminal=xy_error<=PRECISION_RETURN_POLICY['arrival_translation_gate_m'] and yaw_error<=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'] + if within_terminal: + dump(out/f'PRECISION_STATIC_FRICTION_TERMINAL.item_{item_index:02d}.tick_{len(rows):08d}.json',dict(primitive_index=k,primitive=p,base=terminal_state.tolist(),command=base_vw.tolist(),stalled_seconds=stalled_ticks/60.,xy_error_m=xy_error,yaw_error_rad=yaw_error,xy_gate_m=PRECISION_RETURN_POLICY['arrival_translation_gate_m'],yaw_gate_rad=PRECISION_RETURN_POLICY['arrival_yaw_gate_rad'],measured_after_advance=True,accepted_by_measured_endpoint_gate=True));primitive_complete=True;break + dump(out/f'NAVIGATION_BLOCKED.item_{item_index:02d}.json',dict(primitive_index=k,primitive=p,base=base_state().tolist(),command=base_vw.tolist(),stalled_seconds=stalled_ticks/60.)) + failure_reason='navigation_blocked_for_6_seconds';break + if not primitive_complete: + if failure_reason is None:failure_reason='navigation_primitive_timeout_90_seconds' + motion_recovery_boundary(p,k,failure_reason,item_job,return_motion) + base_vw=np.zeros(2);constrain('hold') + +def planning_boundary(kind,item_job,planner): + """Retry only a failed planner in-process after an explicit repair artifact.""" + repair_dir=case/'repairs';repair_dir.mkdir(exist_ok=True);repair=repair_dir/f'item_{item_index:02d}_{kind}.json';seen=None + while True: + try:return planner() + except Exception as exc: + traceback.print_exc();record=dict(item_index=item_index,kind=kind,error=repr(exc),tick=len(rows),repair_file=str(repair),time=time.time()) + planning_waits.append(record);dump(out/'PLANNING_WAITS.json',planning_waits);write_checkpoint();status('WAITING_FOR_PLANNING_REPAIR',planning_error=repr(exc),repair_file=str(repair)) + while True: + while True: + if repair.exists(): + raw=repair.read_bytes();digest=hashlib.sha256(raw).hexdigest() + if digest!=seen:break + time.sleep(2.) + seen=digest + try: + import copy + item_job_before=copy.deepcopy(item_job);repair_tick_before=len(rows) + repair_archive=archive_exact(repair_dir/'applied',digest,'.json',raw);record['repair_archive']=repair_archive + patch=json.loads(raw) + if patch.get('abort') is True:raise RuntimeError('Planning repair explicitly aborted: '+str(patch.get('reason'))) + overrides=patch.get('job_overrides',{}) + if not isinstance(overrides,dict):raise ValueError('planning repair job_overrides must be an object') + # A repair module may consume the configured station or target slot while + # performing its screened motion, so apply the hash-archived patch first. + item_job.update(overrides);record['repair_sha256']=digest;record['overrides']=overrides + if patch.get('repair_module'): + module_path=(repair_dir/patch['repair_module']).resolve() + if not module_path.is_relative_to(repair_dir.resolve()):raise ValueError('repair_module must remain inside this case repair directory') + module_raw=module_path.read_bytes();module_sha=hashlib.sha256(module_raw).hexdigest() + if module_sha!=patch.get('repair_module_sha256'):raise ValueError('repair_module_sha256 mismatch') + module_archive=archive_exact(repair_dir/'applied',module_sha,module_path.suffix or '.py',module_raw) + spec=importlib.util.spec_from_file_location(f'planning_repair_{item_index}_{kind}_{module_sha[:12]}',module_path);module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module) + outcome=module.apply(globals(),item_job,dict(record)) + if outcome is not None: + if not isinstance(outcome,dict):raise ValueError('repair module result must be an object or None') + item_job.update(outcome.get('job_overrides',{})) + record['repair_module']=str(module_path);record['repair_module_sha256']=module_sha;record['repair_module_archive']=module_archive;record['repair_module_result']=outcome + dump(out/'PLANNING_WAITS.json',planning_waits);break + except Exception as repair_exc: + traceback.print_exc();motion_rows=max(0,len(rows)-repair_tick_before) + if motion_rows==0:item_job.clear();item_job.update(item_job_before) + failure=dict(item_index=item_index,kind=kind,event='repair_failed',repair_sha256=digest,error=repr(repair_exc),tick=len(rows),time=time.time(),motion_rows_executed=motion_rows,job_overrides_rolled_back=motion_rows==0,measured_prefix_preserved=True) + planning_waits.append(failure);dump(out/'PLANNING_WAITS.json',planning_waits);write_checkpoint();status('WAITING_FOR_PLANNING_REPAIR',planning_error=repr(repair_exc),repair_file=str(repair),failed_repair_sha256=digest) + +def support_gate(item_job,start_tick): + from shapely.geometry import Point,Polygon + path=target_paths[item_index];current=pose(target);window=np.asarray(target_all_rows[-min(60,len(target_all_rows)):])[:,item_index] + drift=float(np.max(np.linalg.norm(window[:,:3]-window[-1,:3],axis=1))) + linear_speed=float(np.linalg.norm(window[-1,:3]-window[0,:3])/max((len(window)-1)/60.,1/60.)) + angular_speed=float(Rot.from_quat(window[0,3:]).inv().__mul__(Rot.from_quat(window[-1,3:])).magnitude()/max((len(window)-1)/60.,1/60.)) + inside=Polygon(item_job['tray_polygon_xy']).buffer(-.005).covers(Point(current[:2])) + hand_clearance=float(np.linalg.norm(current[:3]-pose(hand)[:3]));support_ticks=[t for t in recent_support_contacts[path] if t>=start_tick] + metrics=dict(target=path,tick=len(rows),pose=current.tolist(),inside_tray=inside,drift_last_second_m=drift,linear_speed_m_s=linear_speed,angular_speed_rad_s=angular_speed,hand_clearance_m=hand_clearance,tray_support_contact_ticks=support_ticks[-20:],support_contact_observed=bool(support_ticks)) + if not inside or drift>.012 or linear_speed>.025 or angular_speed>.35 or hand_clearance<.07 or not support_ticks:raise RuntimeError('Physical tray support gate failed: '+json.dumps(metrics)) + return metrics + +def write_checkpoint(): + tmp=out/'checkpoint_60hz.npz.tmp';final=out/'checkpoint_60hz.npz' + with tmp.open('wb') as f:np.savez_compressed(f,q=np.array(measured),native_target=np.array(commands),dof_names=np.array(names),joint_velocity=np.array(joint_velocities),base_action_body=np.array(base_commands),base_pose=np.array(base_rows),nav_constraint_mode=np.array(nav_modes),nav_constraint_pose=np.array(nav_anchors),robot_link_paths=np.array(link_paths),robot_link_pose=np.array(link_rows),rigid_body_paths=np.array(prop_paths),prop_pose=np.array(prop_rows),target_paths=np.array(target_paths),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),item_index=np.array(item_index_rows,dtype=np.int16),active_target_path=np.array(active_target_rows),phase=np.array(phases),tick=np.array(rows)) + tmp.replace(final);dump(out/'CHECKPOINT.json',dict(schema='ziche.multi_checkpoint.v1',completed_objects=completed_objects,item_index=item_index,control_ticks=len(rows),recording=str(final),continuous_process=True,time=time.time())) + +def save(result,error=None): + for p in writers.values(): + p.stdin.close() + if p.wait()!=0:result='FAILED';error='Camera encoder exited unsuccessfully' + contact_stream.close() + ticks=np.asarray(camera_sample_ticks,dtype=np.int64) + np.savez_compressed(out/'camera_extrinsics_20hz.npz',camera_keys=np.asarray(['top_head','hand_left','hand_right']),control_tick=ticks,timestamp=ticks.astype(np.float64)/60.,global_physics_step=4*len(pre_q)+4*ticks,world_T_camera=np.asarray(camera_sample_extrinsics,dtype=np.float64).reshape(-1,3,4,4)) + if rows:write_startup_report() + dump(out/'PHYSICS_CLOCK.json',dict(schema='startup_preroll_clock_v1',physics_dt_s=1/240.,physics_hz=240,control_hz=60,training_hz=20,substeps_per_control=4,unrecorded_preroll_physics_steps=4*len(pre_q),unrecorded_preroll_duration_s=len(pre_q)/60.,retained_control_rows=len(rows),retained_first_control_row=0,retained_timestamp_formula='(row_index+1)/60',retained_first_post_step_global_physics_step=4*len(pre_q)+4 if rows else None,retained_post_step_global_physics_step_formula='unrecorded_preroll_physics_steps + 4*(row_index+1)',retained_camera_row_formula='control_tick % 3 == 0',retained_camera_frame_count=len(camera_sample_ticks),command_semantics='Native continuous multi-item control after stable initial hold; no object or robot pose writes after setup.',contact_semantics='Contact subscription begins after preroll and camera warmup; preroll contacts are excluded.')) + if rows: + np.savez_compressed(out/'recording_60hz.npz',q=np.array(measured),native_target=np.array(commands),dof_names=np.array(names),base_action_body=np.array(base_commands),base_pose=np.array(base_rows),target_pose=np.array(target_rows),target_paths=np.array(target_paths),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),active_target_path=np.array(active_target_rows),item_index=np.array(item_index_rows,dtype=np.int16),phase=np.array(phases),timestamp=np.array(rows)/60.,nav_constraint_mode=np.array(nav_modes),nav_constraint_pose=np.array(nav_anchors),tick=np.array(rows),joint_velocity=np.array(joint_velocities),robot_link_paths=np.array(link_paths),robot_link_pose=np.array(link_rows)) + if rows: + lp=np.asarray(link_rows);pp=np.asarray(prop_rows);uid=f"chain50_layout_{job['layout_id']:02d}" + payload=dict(episode_uid=uid,target_prim=target_paths[0],requested_target_paths=np.array(target_paths),active_target_path=np.array(active_target_rows),item_index=np.array(item_index_rows,dtype=np.int16),target_initial_pose=requested_initial_target_pose,target_pose_all=np.array(target_all_rows),fps=60.,frame_index=np.arange(len(rows)),robot_dof_names=np.array(names),robot_joint_position_rad_or_m=np.array(measured),robot_joint_velocity_radps_or_mps=np.array(joint_velocities),robot_root_xyz_m=base0[:3,3],robot_root_wxyz=np.r_[bq[3],bq[:3]],robot_link_paths=np.array(link_paths),robot_link_xyz_m=lp[:,:,:3],robot_link_wxyz=lp[:,:,[6,3,4,5]],rigid_body_paths=np.array(prop_paths),prop_xyz_m=pp[:,:,:3],prop_wxyz=pp[:,:,[6,3,4,5]],robot_collision_profile_complete=True,robot_collision_profile_id=profile['profile_id'],robot_collision_enabled_paths=np.array(profile['collision_enabled_paths']),robot_collision_enabled_path_count=len(profile['collision_enabled_paths']),robot_collision_filtered_body_pairs=np.array(profile['filtered_body_pairs'])) + (out/'physics_ticks').mkdir(exist_ok=True);np.savez_compressed(out/'physics_ticks/full_state.npz',**payload);subprocess.run(['cp','--reflink=auto',str(out/'physics_ticks/full_state.npz'),str(out/'full_state.npz')],check=True) + if result=='PHYSICS_COMPLETE_UNVERIFIED' and (not json.loads((out/'PASSIVE_FINGER_PROJECTION.json').read_text())['passed'] or len(camera_sample_ticks)!=len(rows)//3):result='FAILED';error='Stable start or camera sampling gate failed' + if result=='PHYSICS_COMPLETE_UNVERIFIED': + try: + from runtime_collision_readback import CaptureCollisionExporter + from collision_inventory import enabled_nonrobot_convex_colliders + uid=f"chain50_layout_{job['layout_id']:02d}" + for folder,paths,selector in [('collision_readback',profile['collision_enabled_paths'],None),('collision_readback_nonrobot',[str(p.GetPath()) for p in enabled_nonrobot_convex_colliders(stage)],enabled_nonrobot_convex_colliders)]: + reader=CaptureCollisionExporter(stage,out/folder,uid,len(rows)//3-1,len(rows)-1,expected_paths=paths,collider_selector=selector,run_asynchronously=False);reader.start();receipt=reader.finish(len(rows)//3-1,len(rows)-1) + if receipt['status']!='CONTEMPORARY_USD_REPRESENTATIONS_RECORDED':raise RuntimeError('Incomplete capture-end geometry readback: '+folder) + except Exception as exc:result='FAILED';error='Capture-end collision representation: '+repr(exc) + dump(out/'ITEM_EVENTS.json',dict(schema='ziche.multi_item_events.v1',requested_objects=target_paths,completed_objects=completed_objects,events=item_events)) + dump(out/'CONTACT_CAPTURE.json',dict(schema='ziche.contact_capture.v1',requested_targets=target_paths,filter_semantics='robot_or_any_requested_target',all_physics_ticks=True,control_ticks=len(rows),dropped_rows=0,callback_rows=contact_callback_rows,stream='contacts.jsonl.gz')) + dump(out/'place_metrics.json',dict(hands=[item['hand'] for item in items],destination_body_path='/World/Assets/Tray',completed_objects=completed_objects)) + dump(out/'recording_metadata.json',dict(physics_hz=240,control_hz=60,training_hz=20,action_schema='g1_omnipicker_jointpos20_basevw_v1',gripper_max_force={'l':80,'r':80},pose_order='x,y,z,qx,qy,qz,qw',row_clock='post-control-step; same-step applied command',continuous_no_pose_reset=True,multi_item=True,item_count=len(items))) + dump(out/'RESULT.json',dict(status=result,accepted=False,error=error,control_ticks=len(rows),rendered=bool(writers),requested_objects=target_paths,completed_objects=completed_objects,item_count=len(items),requires='independent collision, task, drive, continuity and export validation')) + status(result,error=error) +if len(sys.argv)>2: + try: + replay=np.load(sys.argv[2]);replay_ids=[replay['dof_names'].tolist().index(n) for n in control_names];previous=None + for tick in range(len(replay['q'])): + if 'item_index' in replay: + item_index=int(replay['item_index'][tick]);target=target_objects[item_index];hand=SingleRigidPrim('/World/Robot/gripper_'+items[item_index]['hand']+'_base_link');hand.initialize() + phase=str(replay['phase'][tick]);kind=str(replay['nav_constraint_mode'][tick]);anchor_pose=replay['nav_constraint_pose'][tick];signature=(kind,anchor_pose.tobytes()) + if previous!=signature:constrain(kind,anchor_pose);previous=signature + base_vw=replay['base_action_body'][tick].copy();advance(replay['native_target'][tick,replay_ids]) + save('REPLAY_COMPLETE_UNVERIFIED') + except Exception as e:traceback.print_exc();save('FAILED',repr(e)) + finally:app.close() + sys.exit(0) +try: + from collision_scene import Robot,ROBOT + sys.path.insert(0,str(R/'work/place_training_tray_20260918')) + from carry_transition import plan as plan_carry,Clearance + from multi_motion import plan_return + from return_head_policy import install as install_return_head_policy + install_return_head_policy(globals()) + from placement import solve + from native_planner import build + from place_tray_layout import mesh_world_points + from shapely.geometry import Polygon + from scipy.spatial import ConvexHull + source_layout_id=int(job.get('source_layout_id',job['layout_id'])) + release_spec=json.loads((Path(job['layout']).parents[1]/'audits_release'/f"{source_layout_id:02d}.json").read_text()) + carry_robot=Robot(ROBOT);carry_robot.bodies=[];pose_check=build(stage) + phase='initial_upper_shelf_posture';blend(native[ids],.5) + for i,item in enumerate(items): + item_index=i;data=load_controls(item);target=target_objects[i];hand=SingleRigidPrim('/World/Robot/gripper_'+item['hand']+'_base_link');hand.initialize() + item_job=dict(job);item_job.update(item);item_job['target']=item['target'];item_job['hand']=item['hand'] + item_job['reserved_tray_footprints']=[x['placement_slot']['footprint_xy'] for x in items[i+1:] if x.get('placement_slot')] + target_initial=pose(target).copy();expected_pick_pose=requested_initial_target_pose[i].copy();item_job['expected_pick_pose']=expected_pick_pose.tolist();item_events.append(dict(event='item_started',item_index=i,target=item['target'],tick=len(rows)+1,pose=target_initial.tolist())) + item_phase('waist_to_selected_shelf');blend(data['pick'][0],1.) + item_phase('shelf_pick') + def pick_preflight_call(): + current=pose(target);translation=float(np.linalg.norm(current[:3]-expected_pick_pose[:3]));rotation=float(Rot.from_quat(expected_pick_pose[3:]).inv().__mul__(Rot.from_quat(current[3:])).magnitude()) + metrics=dict(target=item['target'],expected_pose=expected_pick_pose.tolist(),live_pose=current.tolist(),translation_error_m=translation,rotation_error_rad=rotation) + if translation>float(item_job.get('pick_preflight_translation_m',.025)) or rotation>float(item_job.get('pick_preflight_rotation_rad',.35)):raise RuntimeError('Target disturbed before source pickup: '+json.dumps(metrics)) + return metrics + pick_preflight=planning_boundary('pick_preflight',item_job,pick_preflight_call);dump(out/f'PICK_PREFLIGHT.item_{i:02d}.json',pick_preflight);target_initial=pose(target).copy() + for control in data['pick']: + for _ in range(3):advance(control) + def pick_gate_call(): + current=pose(target);lift=float(current[2]-target_initial[2]);contact_evidence=grasp_contact_gate(item['target'],item_job);metrics=dict(target=item['target'],pre_pick_pose=target_initial.tolist(),live_pose=current.tolist(),lift_m=lift,grasp_contact=contact_evidence) + if lift<.025:raise RuntimeError('Pickup did not lift target at least 25 mm: '+json.dumps(metrics)) + return metrics + pick_gate=planning_boundary('pick_recovery',item_job,pick_gate_call);dump(out/f'PICK_GATE.item_{i:02d}.json',pick_gate) + held0=held();grip_idx=18 if item['hand']=='l' else 19 + item_phase('carry_planning');status('RUNNING_PHYSICS') + prop_live=lambda:{p:mat(rb) for p,rb in zip(prop_paths,props)} + def carry_call():return plan_carry(stage,carry_robot,names,np.asarray(robot.get_joint_positions(),dtype=float),mat(base),item['hand'],item['target'],data['target_local'],mat(target),prop_live(),item_job,release_spec) + carry_compact_q,carry_primitives,carry_report=planning_boundary('carry',item_job,carry_call) + dump(out/f'CARRY_PLAN.item_{i:02d}.json',carry_report);dump(out/f'navigation_primitives.item_{i:02d}.json',carry_primitives) + execute_primitives(carry_primitives,held0,return_motion=False,item_job=item_job) + item_phase('bridge_to_place_initial');end=data['place_initial'].copy();end[18:]=native[ids][18:] + def bridge_call(): + start_full=np.asarray(robot.get_joint_positions(),dtype=float).copy();end_full=start_full.copy();end_full[ids]=end + checker=Clearance(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],held(),prop_live());checker.deadline=time.monotonic()+900 + candidates=[[]] + high_end=end_full.copy();high_end[names.index('idx01_body_joint1')]=start_full[names.index('idx01_body_joint1')];candidates.append([high_end]) + for waypoints in candidates: + chain=[start_full]+waypoints+[end_full] + if all(checker.segment(a,b,mat(base),mat(base)) for a,b in zip(chain,chain[1:])):return [q[ids].copy() for q in waypoints]+[end.copy()] + raise RuntimeError('No collision-screened held-object posture bridge: '+str(checker.last)) + bridge_controls=planning_boundary('place_bridge',item_job,bridge_call) + for bridge_target in bridge_controls:blend(bridge_target,6.,held0) + item_phase('place_planning');status('RUNNING_PHYSICS');obstacles=[] + for path in completed_objects: + rb=props[prop_paths.index(path)];prim=stage.GetPrimAtPath(path);world=mesh_world_points(stage,prim) + authored=np.asarray(UsdGeom.XformCache().GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=mat(rb);verts=local@current[:3,:3].T+current[:3,3];obstacles.append(Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2])) + def place_call(): + from release_sweep import ReleaseSweep + item_job['require_release_sweep']=True + release_checker=ReleaseSweep(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],prop_live(),item['place_source'],deadline_s=900) + live_q=np.asarray(robot.get_joint_positions(),dtype=np.float64).copy();live_base=mat(base) + world_check=Clearance(stage,carry_robot,names,item['hand'],item['target'],data['target_local'],held(),prop_live());world_check.deadline=time.monotonic()+900 + return solve(carry_robot,names,live_q,live_base,mat(target),data['target_local'],item_job,obstacles,pose_check,world_check=world_check,release_sweep=release_checker) + planned,retreat,placement_report=planning_boundary('place',item_job,place_call) + dump(out/f'PLACEMENT_PLAN.item_{i:02d}.json',placement_report);np.savez_compressed(out/f'place_planned_targets.item_{i:02d}.npz',q=planned[:,ids],names=control_names) + item_phase('place_descent') + for targetq in planned[:,ids]: + for _ in range(3):advance(targetq) + item_phase('place_release');opened=native[ids].copy();opened[grip_idx]=np.pi/4;blend(opened,2.) + item_phase('release_retreat');retreat_control=retreat[ids].copy();retreat_control[grip_idx]=np.pi/4;blend(retreat_control,3.) + settle_start=len(rows)+1;item_phase('settle');blend(native[ids],2.) + gate=support_gate(item_job,settle_start);completed_objects.append(item['target']);item_events.append(dict(event='item_supported',item_index=i,target=item['target'],tick=len(rows),metrics=gate));write_checkpoint() + base_error=None;yaw_error=None + if i+1PRECISION_RETURN_POLICY['arrival_translation_gate_m'] or rotation>PRECISION_RETURN_POLICY['arrival_yaw_gate_rad']:raise RuntimeError('Return did not reach precision pickup gate: '+json.dumps(metrics)) + return metrics + arrival=planning_boundary('return_arrival',item_job,return_arrival_call);dump(out/f'RETURN_ARRIVAL.item_{i:02d}.json',arrival);base_error=arrival['translation_error_m'];yaw_error=arrival['yaw_error_rad'] + item_phase('complete');blend(native[ids],.25);item_events.append(dict(event='item_completed',item_index=i,target=item['target'],tick=len(rows),support=gate,return_base_error_m=base_error,return_yaw_error_rad=yaw_error)) + write_checkpoint();status('RUNNING_PHYSICS') + save('PHYSICS_COMPLETE_UNVERIFIED') +except Exception as e: + traceback.print_exc();save('FAILED',repr(e)) +finally:app.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/runtime_evidence.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/runtime_evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..5db31f321346fb68eb4f49f4e09c97b4d61b7587 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/runtime_evidence.py @@ -0,0 +1,15 @@ +"""Reconcile snapshot metadata only from matching actual capture readbacks.""" +import json,shutil,time +from pathlib import Path +from pxr import Usd,UsdGeom + +def reconcile(case): + out=case/'rollout';a=json.loads((out/'collision_readback/INPUT.json').read_text());b=json.loads((out/'collision_readback_nonrobot/INPUT.json').read_text()) + for key in ['episode_uid','stage_id','root_layer','request_tick','stage_meters_per_unit','stage_up_axis']: + assert a.get(key)==b.get(key),key + p=out/'scene.usda';s=Usd.Stage.Open(str(p));old=[UsdGeom.GetStageMetersPerUnit(s),str(UsdGeom.GetStageUpAxis(s))];new=[a['stage_meters_per_unit'],a['stage_up_axis']] + if old!=new: + backup=out/'scene.before_runtime_metadata_fix.usda' + if not backup.exists():shutil.copy2(p,backup) + UsdGeom.SetStageMetersPerUnit(s,new[0]);UsdGeom.SetStageUpAxis(s,new[1]);tmp=p.with_suffix('.metadata.tmp');tmp.write_text(s.GetRootLayer().ExportToString());tmp.replace(p) + (out/'SCENE_METADATA_REPAIR.json').write_text(json.dumps(dict(before=old,after=new,source='matching actual robot/nonrobot capture INPUT.json',updated=time.time()),indent=2)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/startup_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/startup_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c29f5518d4f29aa76af23d7ddf8920e362086c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/startup_validation.py @@ -0,0 +1,690 @@ +"""Strict startup, no-fall, camera-clock, video, and immutable-gate helpers. + +This module is deliberately independent of the controller. It consumes only +producer evidence and the completed native recording. Missing evidence fails; +there are no layout-specific exceptions. +""" +from __future__ import annotations + +import hashlib +import json +import os +import re +import shutil +import time +from pathlib import Path + +import numpy as np + + +CAMERAS = { + "top_head": (1280, 800), + "hand_left": (848, 480), + "hand_right": (848, 480), +} +CAMERA_LINKS = { + "top_head": "/World/Robot/head_link2", + "hand_left": "/World/Robot/gripper_l_base_link", + "hand_right": "/World/Robot/gripper_r_base_link", +} +PASSIVE_FINGER = re.compile(r"^/World/Robot/gripper_[lr]_(?:inner|outer)_link[234]$") +STRICT = { + "prop_translation_m": 5e-5, + "prop_rotation_rad": float(np.deg2rad(0.02)), + "camera_translation_m": 5e-5, + "camera_rotation_rad": float(np.deg2rad(0.01)), + "driven_robot_translation_m": 5e-5, + "driven_robot_rotation_rad": float(np.deg2rad(0.02)), + "passive_finger_projected_motion_px": 0.25, +} +NO_FALL = { + "max_translation_m": 0.020, + "max_downward_m": 0.010, + "max_rotation_deg": 15.0, + "authored_to_first_translation_m": 0.001, + "authored_to_first_downward_m": 0.001, + "authored_to_first_rotation_deg": 0.5, +} + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with Path(path).open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def write_json(path: Path, payload: dict) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + pending = path.with_suffix(path.suffix + ".pending") + pending.write_text(json.dumps(payload, indent=2, default=str) + "\n") + os.replace(pending, path) + + +def _load_state(out: Path): + path = out / "full_state.npz" + if not path.is_file(): + path = out / "physics_ticks/full_state.npz" + return path, np.load(path, allow_pickle=False) + + +def _normalize_quaternion(q): + q = np.asarray(q, dtype=np.float64) + norm = np.linalg.norm(q, axis=-1, keepdims=True) + if np.any(norm < 1e-15): + raise ValueError("zero quaternion") + return q / norm + + +def angular_from_reference(q, reference): + q = _normalize_quaternion(q) + reference = _normalize_quaternion(np.asarray(reference, dtype=np.float64)) + dots = np.clip(np.abs(np.sum(q * reference, axis=-1)), 0.0, 1.0) + return 2.0 * np.arccos(dots) + + +def pairwise_span(pose): + pose = np.asarray(pose, dtype=np.float64) + if pose.shape[0] < 1 or pose.shape[1] < 1: + return 0.0, 0.0 + xyz = pose[:, :, :3] + q = _normalize_quaternion(pose[:, :, 3:]) + translation = float(np.linalg.norm(xyz[:, None] - xyz[None, :], axis=-1).max()) + dots = np.clip(np.abs(np.einsum("tni,sni->tsn", q, q)), 0.0, 1.0) + rotation = float((2.0 * np.arccos(dots)).max()) + return translation, rotation + + +def clock_audit(out: Path, recording, preroll_npz) -> dict: + out = Path(out) + path = out / "PHYSICS_CLOCK.json" + failures = [] + try: + clock = json.loads(path.read_text()) + except Exception as exc: + return {"schema": "multi_startup_clock_audit_v1", "passed": False, + "failures": ["missing_or_invalid_clock"], "error": repr(exc)} + rows = len(recording["q"]) + preroll_rows = len(preroll_npz["q"]) + expected_steps = 4 * preroll_rows + checks = { + "schema": clock.get("schema") == "startup_preroll_clock_v1", + "rates": clock.get("physics_hz") == 240 and clock.get("control_hz") == 60 + and clock.get("training_hz") == 20 and clock.get("substeps_per_control") == 4, + "unrecorded_preroll_steps": clock.get("unrecorded_preroll_physics_steps") == expected_steps, + "unrecorded_preroll_duration": abs(float(clock.get("unrecorded_preroll_duration_s", -1)) + - expected_steps / 240.0) <= 1e-12, + "retained_rows": clock.get("retained_control_rows") == rows + and clock.get("retained_first_control_row") == 0, + "first_global_step": clock.get("retained_first_post_step_global_physics_step") == expected_steps + 4, + "timestamp_formula": clock.get("retained_timestamp_formula") == "(row_index+1)/60", + "global_step_formula": clock.get("retained_post_step_global_physics_step_formula") + == "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "camera_formula": clock.get("retained_camera_row_formula") == "control_tick % 3 == 0" + and clock.get("retained_camera_frame_count") == rows // 3, + "command_semantics": bool(clock.get("command_semantics")), + "contact_semantics": bool(clock.get("contact_semantics")), + "recording_tick": np.array_equal(recording["tick"], np.arange(1, rows + 1)), + "recording_timestamp": np.allclose(recording["timestamp"], np.arange(1, rows + 1) / 60.0, + atol=1e-12, rtol=0), + } + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_startup_clock_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "clock": clock, + "clock_sha256": sha256(path), "retained_rows": rows, + "preroll_rows_60hz": preroll_rows, + } + + +def _pose_matrix_xyzw(pose): + from scipy.spatial.transform import Rotation as Rot + pose = np.asarray(pose, dtype=np.float64) + matrices = np.zeros(pose.shape[:-1] + (4, 4), dtype=np.float64) + matrices[..., 3, 3] = 1.0 + matrices[..., :3, :3] = Rot.from_quat(pose[..., 3:]).as_matrix() + matrices[..., :3, 3] = pose[..., :3] + return matrices + + +def camera_extrinsics_audit(out: Path, recording, clock: dict) -> dict: + out = Path(out) + path = out / "camera_extrinsics_20hz.npz" + failures = [] + try: + camera = np.load(path, allow_pickle=False) + required = {"camera_keys", "control_tick", "timestamp", "global_physics_step", "world_T_camera"} + missing = sorted(required - set(camera.files)) + if missing: + raise ValueError(f"missing keys {missing}") + keys = camera["camera_keys"].tolist() + ticks = np.asarray(camera["control_tick"], dtype=np.int64) + timestamp = np.asarray(camera["timestamp"], dtype=np.float64) + global_steps = np.asarray(camera["global_physics_step"], dtype=np.int64) + transforms = np.asarray(camera["world_T_camera"], dtype=np.float64) + except Exception as exc: + return {"schema": "multi_camera_extrinsics_audit_v1", "passed": False, + "failures": ["missing_or_invalid_camera_extrinsics"], "error": repr(exc)} + rows = len(recording["q"]) + expected_ticks = np.arange(3, rows + 1, 3, dtype=np.int64) + preroll_steps = int(clock.get("unrecorded_preroll_physics_steps", -1)) + checks = { + "camera_order": keys == list(CAMERAS), + "shape": transforms.shape == (len(expected_ticks), 3, 4, 4), + "control_tick_mapping": np.array_equal(ticks, expected_ticks), + "timestamp_mapping": np.allclose(timestamp, expected_ticks / 60.0, atol=1e-12, rtol=0), + "global_step_mapping": np.array_equal(global_steps, preroll_steps + 4 * expected_ticks), + "finite": bool(np.isfinite(transforms).all()), + "homogeneous_row": bool(np.allclose(transforms[..., 3, :], [0, 0, 0, 1], atol=1e-10)), + } + if checks["shape"] and checks["finite"]: + rotation = transforms[..., :3, :3] + identity_error = float(np.max(np.abs(rotation.swapaxes(-1, -2) @ rotation - np.eye(3)))) + determinant_error = float(np.max(np.abs(np.linalg.det(rotation) - 1.0))) + else: + identity_error = determinant_error = float("inf") + checks["rigid_rotation"] = identity_error <= 1e-5 and determinant_error <= 1e-5 + + # Prove every camera stays rigidly mounted to the recorded robot link at the + # exact control row associated with that encoded frame. The authored mount + # can differ by camera, but it must be constant throughout the episode. + mount_errors = {} + checks["rigid_mount_alignment"] = False + if checks["shape"] and checks["control_tick_mapping"]: + paths = recording["robot_link_paths"].tolist() + try: + link_index = [paths.index(CAMERA_LINKS[key]) for key in keys] + link_pose = np.asarray(recording["robot_link_pose"], dtype=np.float64)[ticks - 1][:, link_index] + world_T_link = _pose_matrix_xyzw(link_pose) + relative = np.linalg.inv(world_T_link) @ transforms + for index, key in enumerate(keys): + reference = relative[0, index] + translation = float(np.linalg.norm(relative[:, index, :3, 3] - reference[:3, 3], axis=1).max()) + from scipy.spatial.transform import Rotation as Rot + delta = Rot.from_matrix(reference[:3, :3]).inv() * Rot.from_matrix(relative[:, index, :3, :3]) + rotation = float(delta.magnitude().max()) + mount_errors[key] = {"translation_m": translation, "rotation_rad": rotation} + checks["rigid_mount_alignment"] = all( + row["translation_m"] <= 1e-5 and row["rotation_rad"] <= 1e-5 + for row in mount_errors.values()) + except Exception as exc: + mount_errors["error"] = repr(exc) + failures.extend(name for name, passed in checks.items() if not passed) + return { + "schema": "multi_camera_extrinsics_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "frames": len(expected_ticks), + "rotation_orthogonality_max_error": identity_error, + "rotation_determinant_max_error": determinant_error, + "mount_errors": mount_errors, "camera_extrinsics_sha256": sha256(path), + } + + +def camera_warmup_audit(out: Path, preroll_evidence: dict, result: dict) -> dict: + """Require native three-view capture to start only after stable priming.""" + out = Path(out) + path = out / "CAMERA_WARMUP.json" + failures = [] + try: + report = json.loads(path.read_text()) + final = report.get("final_stability_window", {}) + startup = out / "startup_frames" + names = sorted(p.name for p in startup.glob("*.jpg")) + expected = sorted(f"{camera}_{index:02d}.jpg" for camera in CAMERAS for index in range(10)) + checks = { + "native_rendered": result.get("rendered") is True, + "status": report.get("status") == "PASSED", + "after_stability": report.get("after_physics_stability") is True, + "render_warmups": int(report.get("render_only_warmups", -1)) >= 12, + "discarded_reads": int(report.get("discarded_render_reads", -1)) >= 3, + "unrecorded_hold_samples": int(report.get("unrecorded_hold_samples", -1)) >= 3, + "preroll_hold_accounting": int(preroll_evidence.get("camera_warmup_physics_samples", -1)) + == int(report.get("unrecorded_hold_samples", -2)), + "no_captured_warmup_frames": int(report.get("captured_frames", -1)) == 0, + "final_prop_stable": float(final.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera_stable": float(final.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven_stable": float(final.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "startup_frame_set": names == expected, + "startup_frames_nonempty": names == expected and all((startup / name).stat().st_size > 0 for name in names), + } + failures.extend(name for name, passed in checks.items() if not passed) + except Exception as exc: + return {"schema": "multi_camera_warmup_audit_v1", "passed": False, + "checks": {}, "failures": ["missing_or_invalid_camera_warmup"], "error": repr(exc)} + return {"schema": "multi_camera_warmup_audit_v1", "passed": not failures, + "checks": checks, "failures": failures, "receipt": report, + "receipt_sha256": sha256(path), + "startup_frame_sha256": {name: sha256(out / "startup_frames" / name) for name in names}} + + +def _projection_audit(out: Path, pre_path: Path, state_path: Path) -> dict: + path = out / "PASSIVE_FINGER_PROJECTION.json" + try: + report = json.loads(path.read_text()) + windows = {row.get("label"): row for row in report.get("windows", [])} + required = {"final_preroll_0p5s", "first_retained_30_control_rows"} + implementation = Path(report["projection_implementation"]) + checks = { + "status": report.get("status") == "PASSED" and report.get("passed") is True, + "threshold": report.get("threshold_px") == STRICT["passive_finger_projected_motion_px"], + "scope": report.get("only_passive_distal_fingers") is True, + "windows": set(windows) == required and all( + row.get("passed") is True + and row.get("passive_projection", {}).get("status") == "PASSED" + and 0 <= float(row.get("passive_projection", {}).get("max_projected_motion_px", 1)) < 0.25 + for row in windows.values()), + "bodies": bool(report.get("evaluated_passive_paths")) and bool(report.get("evaluated_driven_paths")), + "cameras": len(report.get("evaluated_cameras", [])) == 3, + "implementation_hash": implementation.is_file() + and report.get("projection_implementation_sha256") == sha256(implementation), + } + failures = [name for name, passed in checks.items() if not passed] + return {"passed": not failures, "checks": checks, "failures": failures, + "receipt": report, "receipt_sha256": sha256(path), "windows": windows, + "independently_bound_pose_sha256": { + pre_path.name: sha256(pre_path), state_path.name: sha256(state_path)}} + except Exception as exc: + return {"passed": False, "checks": {}, "failures": ["missing_or_invalid_projection"], + "error": repr(exc), "windows": {}} + + +def startup_stability_audit(case: Path, recording, action_names) -> dict: + case = Path(case) + out = case / "rollout" + pre_json_path = out / "PREROLL_EVIDENCE.json" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + failures = [] + try: + evidence = json.loads(pre_json_path.read_text()) + pre = np.load(pre_path, allow_pickle=False) + except Exception as exc: + return {"schema": "multi_startup_stability_audit_v1", "passed": False, + "failures": ["missing_preroll_evidence"], "error": repr(exc)} + projection = _projection_audit(out, pre_path, state_path) + rows = min(30, len(recording["q"])) + paths = recording["robot_link_paths"].tolist() + try: + camera_indices = [paths.index(path) for path in CAMERA_LINKS.values()] + driven_indices = [index for index, path in enumerate(paths) if not PASSIVE_FINGER.match(path)] + retained_links = np.asarray(recording["robot_link_pose"][:rows], dtype=np.float64) + retained_props = np.concatenate([ + np.asarray(state["prop_xyz_m"][:rows], dtype=np.float64), + np.asarray(state["prop_wxyz"][:rows, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + prop_translation, prop_rotation = pairwise_span(retained_props) + camera_translation, camera_rotation = pairwise_span(retained_links[:, camera_indices]) + driven_translation, driven_rotation = pairwise_span(retained_links[:, driven_indices]) + except Exception as exc: + prop_translation = prop_rotation = camera_translation = camera_rotation = float("inf") + driven_translation = driven_rotation = float("inf") + failures.append("retained_pose_metrics") + metric_error = repr(exc) + else: + metric_error = None + first_projection = projection.get("windows", {}).get("first_retained_30_control_rows", {}) + retained_checks = { + "rows": rows == 30, + "prop_translation": prop_translation < STRICT["prop_translation_m"], + "prop_rotation": prop_rotation < STRICT["prop_rotation_rad"], + "camera_translation": camera_translation < STRICT["camera_translation_m"], + "camera_rotation": camera_rotation < STRICT["camera_rotation_rad"], + "driven_translation": driven_translation < STRICT["driven_robot_translation_m"], + "driven_rotation": driven_rotation < STRICT["driven_robot_rotation_rad"], + "passive_projection": first_projection.get("passed") is True, + } + failures.extend("first_retained_" + name for name, passed in retained_checks.items() if not passed) + thresholds = evidence.get("thresholds", {}) + final_window = evidence.get("final_window", {}) + names = recording["dof_names"].tolist() + try: + command_indices = [names.index(name) for name in action_names[:20]] + exact_initial = np.array_equal(np.asarray(pre["initial_command"], dtype=np.float64), + np.asarray(recording["native_target"][0, command_indices], dtype=np.float64)) + retained_initial_hold = (rows == 30 + and np.array_equal(np.asarray(recording["native_target"][:rows, command_indices], dtype=np.float64), + np.repeat(np.asarray(pre["initial_command"], dtype=np.float64)[None], rows, axis=0)) + and np.array_equal(np.asarray(recording["base_action_body"][:rows], dtype=np.float64), + np.zeros((rows, 2), dtype=np.float64)) + and set(np.asarray(recording["phase"][:rows]).astype(str).tolist()) == {"initial_upper_shelf_posture"} + and set(np.asarray(recording["item_index"][:rows], dtype=np.int64).tolist()).issubset({-1, 0})) + except Exception: + exact_initial = False + retained_initial_hold = False + preroll_checks = { + "status": evidence.get("status") == "PASSED", + "rates": evidence.get("physics_hz") == 240 and evidence.get("sample_hz") == 60, + "unrecorded": evidence.get("recorded_rows") == 0 + and evidence.get("contact_capture_started") is False + and evidence.get("camera_capture_started") is False, + "no_pose_writes": evidence.get("no_pose_writes_or_resets") is True, + "duration": float(evidence.get("duration_s", 0)) >= 0.5, + "thresholds": thresholds.get("prop_translation_m") == STRICT["prop_translation_m"] + and thresholds.get("prop_rotation_rad") == STRICT["prop_rotation_rad"] + and thresholds.get("camera_translation_m") == STRICT["camera_translation_m"] + and thresholds.get("camera_rotation_rad") == STRICT["camera_rotation_rad"] + and thresholds.get("passive_finger_projected_motion_px") == 0.25, + "final_prop": float(final_window.get("prop_translation_span_m", 1)) <= STRICT["prop_translation_m"] + and float(final_window.get("prop_rotation_span_rad", 1)) <= STRICT["prop_rotation_rad"], + "final_camera": float(final_window.get("camera_link_translation_span_m", 1)) <= STRICT["camera_translation_m"] + and float(final_window.get("camera_link_rotation_span_rad", 1)) <= STRICT["camera_rotation_rad"], + "final_driven": float(final_window.get("driven_robot_link_translation_span_m", 1)) <= STRICT["driven_robot_translation_m"] + and float(final_window.get("driven_robot_link_rotation_span_rad", 1)) <= STRICT["driven_robot_rotation_rad"], + "exact_initial_command": exact_initial, + "first_retained_30_exact_initial_hold": retained_initial_hold, + "projection": projection["passed"], + } + failures.extend("preroll_" + name for name, passed in preroll_checks.items() if not passed) + return { + "schema": "multi_startup_stability_audit_v1", "passed": not failures, + "failures": failures, "strict_thresholds": STRICT, + "retained_first_30_control_rows": { + "checks": retained_checks, "prop_translation_span_m": prop_translation, + "prop_rotation_span_rad": prop_rotation, + "camera_translation_span_m": camera_translation, + "camera_rotation_span_rad": camera_rotation, + "driven_translation_span_m": driven_translation, + "driven_rotation_span_rad": driven_rotation, + "metric_error": metric_error, + }, + "preroll_checks": preroll_checks, "preroll_evidence": evidence, + "projection_audit": projection, + "evidence_sha256": {"PREROLL_EVIDENCE.json": sha256(pre_json_path), + "PREROLL_EVIDENCE.npz": sha256(pre_path), + state_path.name: sha256(state_path)}, + } + + +def prop_no_fall_audit(case: Path, recording, requested: list[dict]) -> dict: + """Audit every prop from the authored pose over its physically relevant scopes. + + Non-target props cover the entire preroll and episode. Requested targets + cover preroll + all rows before their own shelf_pick, and then the whole + interval from their first settle row through episode end. The manipulation + interval is intentionally excluded; placement gates cover it separately. + """ + case = Path(case) + out = case / "rollout" + pre_path = out / "PREROLL_EVIDENCE.npz" + state_path, state = _load_state(out) + try: + pre = np.load(pre_path, allow_pickle=False) + pre_paths = pre["prop_paths"].tolist() + initial = np.asarray(pre["initial_prop_pose_before_preroll"], dtype=np.float64) + pre_pose = np.asarray(pre["prop_pose"], dtype=np.float64) + full_paths = state["rigid_body_paths"].tolist() + if len(set(pre_paths)) != len(pre_paths) or set(pre_paths) != set(full_paths): + raise ValueError("preroll/full-state prop inventory mismatch") + order = [full_paths.index(path) for path in pre_paths] + retained = np.concatenate([ + np.asarray(state["prop_xyz_m"][:, order], dtype=np.float64), + np.asarray(state["prop_wxyz"][:, order][:, :, [1, 2, 3, 0]], dtype=np.float64), + ], axis=2) + except Exception as exc: + return {"schema": "multi_prop_no_fall_audit_v1", "passed": False, + "failures": ["missing_or_invalid_prop_evidence"], "error": repr(exc)} + producer_path = out / "INITIAL_PROP_NO_FALL.json" + try: + producer = json.loads(producer_path.read_text()) + maxima = producer.get("maxima", {}) + producer_checks = { + "passed": producer.get("passed") is True, + "all_props": producer.get("all_requested_and_nonrequested_props_included") is True, + "all_preroll_samples": int(producer.get("physics_samples", -1)) == len(pre_pose), + "translation": float(maxima.get("translation_m", 1)) <= NO_FALL["max_translation_m"], + "downward": float(maxima.get("downward_m", 1)) <= NO_FALL["max_downward_m"], + "rotation": float(maxima.get("rotation_rad", 1)) <= np.deg2rad(NO_FALL["max_rotation_deg"]), + } + except FileNotFoundError: + producer = {"present": False, "semantics": "optional producer fail-fast; independent full trace remains mandatory"} + producer_checks = {"optional_receipt_absent": True} + except Exception as exc: + producer = {"present": True, "error": repr(exc)} + producer_checks = {"receipt_valid_if_present": False} + phases = np.asarray(recording["phase"]).astype(str) + item_index = np.asarray(recording["item_index"], dtype=np.int64) + try: + events = json.loads((out / "ITEM_EVENTS.json").read_text()).get("events", []) + except Exception: + events = [] + target_to_index = {row["target"]: index for index, row in enumerate(requested)} + failures = [] + rows = [] + + def segment_metrics(trace, reference): + trace = np.asarray(trace, dtype=np.float64) + if len(trace) == 0: + return {"rows": 0, "max_translation_m": float("inf"), + "max_downward_m": float("inf"), "max_rotation_deg": float("inf"), "passed": False} + translation = np.linalg.norm(trace[:, :3] - reference[:3], axis=1) + rotation = np.degrees(angular_from_reference(trace[:, 3:], reference[3:])) + metrics = { + "rows": len(trace), "max_translation_m": float(translation.max()), + "max_downward_m": float(max(0.0, reference[2] - trace[:, 2].min())), + "max_rotation_deg": float(rotation.max()), + } + metrics["passed"] = (metrics["max_translation_m"] <= NO_FALL["max_translation_m"] + and metrics["max_downward_m"] <= NO_FALL["max_downward_m"] + and metrics["max_rotation_deg"] <= NO_FALL["max_rotation_deg"]) + return metrics + + for prop_index, path in enumerate(pre_paths): + first = pre_pose[0, prop_index] + authored = initial[prop_index] + supplemental = { + "authored_to_first_translation_m": float(np.linalg.norm(first[:3] - authored[:3])), + "authored_to_first_downward_m": float(max(0.0, authored[2] - first[2])), + "authored_to_first_rotation_deg": float(np.degrees(angular_from_reference(first[None, 3:], authored[3:]))[0]), + } + supplemental_checks = {name: supplemental[name] <= NO_FALL[name] for name in supplemental} + segments = [] + if path not in target_to_index: + trace = np.concatenate([pre_pose[:, prop_index], retained[:, prop_index]], axis=0) + segments.append({"scope": "full_preroll_and_capture", **segment_metrics(trace, authored)}) + else: + index = target_to_index[path] + pick = np.flatnonzero((item_index == index) & (phases == f"item_{index:02d}_shelf_pick")) + completed = [row for row in events if row.get("event") == "item_completed" + and row.get("item_index") == index and row.get("target") == path] + if len(pick): + pre_pick = np.concatenate([pre_pose[:, prop_index], retained[:int(pick[0]), prop_index]], axis=0) + segments.append({"scope": "preroll_and_rows_before_own_pick", **segment_metrics(pre_pick, authored)}) + else: + segments.append({"scope": "preroll_and_rows_before_own_pick", "rows": 0, "passed": False, + "reason": "missing own shelf_pick"}) + if len(completed) == 1 and 1 <= int(completed[0].get("tick", 0)) <= len(retained): + start = int(completed[0]["tick"]) - 1 + post = retained[start:, prop_index] + segments.append({"scope": "item_completed_through_episode_end", + "source_control_tick_start": start + 1, + **segment_metrics(post, post[0])}) + else: + segments.append({"scope": "item_completed_through_episode_end", "rows": 0, "passed": False, + "reason": "missing or ambiguous item_completed event"}) + passed = all(supplemental_checks.values()) and all(row.get("passed") is True for row in segments) + if not passed: + failures.append(path) + rows.append({"path": path, "requested_item_index": target_to_index.get(path), + "authored_to_first_preroll": supplemental, + "authored_to_first_checks": supplemental_checks, + "segments": segments, "passed": passed}) + producer_passed = all(producer_checks.values()) + if not producer_passed: + failures.append("producer_initial_prop_no_fall_receipt") + return { + "schema": "multi_prop_no_fall_audit_v1", "passed": not failures, + "thresholds": NO_FALL, "quaternions_normalized_float64": True, + "reference": "authored/reset pose captured immediately before the first unrecorded preroll physics step", + "non_target_scope": "every preroll sample and every retained control row through episode end", + "requested_target_scope": "preroll and retained rows before its own pick, plus item_completed through episode end", + "manipulation_scope": "excluded here and independently covered by lift/grasp/place/support/collision gates", + "paths": rows, "failures": failures, "producer_initial_prop_no_fall": producer, + "producer_initial_prop_no_fall_checks": producer_checks, + "evidence_sha256": {**{"PREROLL_EVIDENCE.npz": sha256(pre_path), state_path.name: sha256(state_path)}, + **({"INITIAL_PROP_NO_FALL.json": sha256(producer_path)} if producer_path.is_file() else {})}, + } + + +def video_decode_audit(out: Path, expected_frames: int) -> dict: + """Perform the one authoritative sequential decode of all three videos.""" + import av + out = Path(out) + videos = {} + all_passed = True + for camera, (width, height) in CAMERAS.items(): + path = out / f"{camera}.mp4" + total = np.zeros(3, dtype=np.float64) + total2 = np.zeros(3, dtype=np.float64) + low = np.ones(3, dtype=np.float64) + high = np.zeros(3, dtype=np.float64) + sample_count = 0 + frame_count = 0 + first_gray = [] + near_black = 0 + nonfinite = 0 + previous_digest = None + exact_run = longest_exact_run = 0 + with av.open(str(path)) as container: + stream = container.streams.video[0] + fps = float(stream.average_rate) + actual_width, actual_height = stream.width, stream.height + for frame in container.decode(video=0): + rgb = frame.to_ndarray(format="rgb24") + gray = rgb.astype(np.float32).mean(axis=2) / 255.0 + if frame_count < 4: + first_gray.append(gray) + fraction = float((gray < (4.0 / 255.0)).mean()) + near_black += int(fraction > 0.98) + nonfinite += int(not np.isfinite(gray).all()) + digest = hashlib.sha256(rgb.tobytes()).digest() + exact_run = exact_run + 1 if digest == previous_digest else 1 + longest_exact_run = max(longest_exact_run, exact_run) + previous_digest = digest + if frame_count % max(1, expected_frames // 100) == 0: + sampled = rgb[::16, ::16].reshape(-1, 3) / 255.0 + total += sampled.sum(axis=0) + total2 += (sampled * sampled).sum(axis=0) + low = np.minimum(low, sampled.min(axis=0)) + high = np.maximum(high, sampled.max(axis=0)) + sample_count += len(sampled) + frame_count += 1 + adjacent = [float(np.mean(np.abs(first_gray[i + 1] - first_gray[i]))) + for i in range(max(0, len(first_gray) - 1))] + checks = { + "frame_count": frame_count == expected_frames, + "fps": fps == 20, + "dimensions": (actual_width, actual_height) == (width, height), + "first_frame_synchronized": len(adjacent) == 3 and adjacent[0] < 0.05, + "no_near_black_frames": near_black == 0, + "finite_metrics": nonfinite == 0, + "no_long_exact_frame_run": longest_exact_run < 10, + } + passed = all(checks.values()) + all_passed &= passed + mean = total / max(sample_count, 1) + videos[camera] = { + "passed": passed, "path": str(path), "sha256": sha256(path), + "frames": frame_count, "fps": fps, "width": actual_width, "height": actual_height, + "checks": checks, "first_three_adjacent_mean_abs_gray_differences": adjacent, + "near_black_gt_0_98_frames": near_black, "nonfinite_metric_count": nonfinite, + "longest_exact_frame_run": longest_exact_run, + "image_stats": { + "min": low.reshape(3, 1, 1).tolist(), "max": high.reshape(3, 1, 1).tolist(), + "mean": mean.reshape(3, 1, 1).tolist(), + "std": np.sqrt(np.maximum(total2 / max(sample_count, 1) - mean * mean, 0)).reshape(3, 1, 1).tolist(), + "count": [frame_count], + }, + } + report = { + "schema": "multi_video_decode_audit_v1", "passed": all_passed, + "method": "one authoritative sequential PyAV decode of every frame in all three views", + "expected_frames": expected_frames, "videos": videos, + } + write_json(out / "VIDEO_DECODE_AUDIT.json", report) + return report + + +FROZEN_AUDITS = [ + "TASK_SCHEMA_AUDIT.json", "CONTACT_CAPTURE_AUDIT.json", "GRASP_RETENTION_AUDIT.json", + "GRASP_STABILITY_AUDIT.json", "NON_TARGET_PROP_STABILITY_AUDIT.json", + "PHYSICS_CLOCK_AUDIT.json", "STARTUP_STABILITY_AUDIT.json", "CAMERA_WARMUP_AUDIT.json", + "CAMERA_EXTRINSICS_AUDIT.json", + "VIDEO_DECODE_AUDIT.json", + "SELF_COLLISION_AUDIT.json", "WORLD_COLLISION_AUDIT.json", "WRIST_AUDIT.json", + "VISIBILITY_AUDIT.json", +] +RAW_EVIDENCE = [ + "recording_60hz.npz", "full_state.npz", "PHYSICS_CLOCK.json", "PREROLL_EVIDENCE.json", + "PREROLL_EVIDENCE.npz", "PASSIVE_FINGER_PROJECTION.json", "CAMERA_WARMUP.json", + "camera_extrinsics_20hz.npz", "RESULT.json", "ITEM_EVENTS.json", "CHECKPOINT.json", + "CONTACT_CAPTURE.json", "recording_metadata.json", "LIVE_DRIVE_READBACK.json", + "scene.usda", "contacts.jsonl.gz", "top_head.mp4", "hand_left.mp4", "hand_right.mp4", +] +OPTIONAL_RAW_EVIDENCE = ["INITIAL_PROP_NO_FALL.json"] + + +def freeze_physical_gate(case: Path, world_audit_shards: int | None = None) -> dict: + case = Path(case).resolve() + out = case / "rollout" + destination = out / "physical_evidence" + if destination.exists(): + raise FileExistsError(destination) + pending = out / f".physical_evidence.pending.{os.getpid()}" + pending.mkdir() + try: + frozen = {} + for name in FROZEN_AUDITS: + source = out / name + target = pending / name + shutil.copy2(source, target) + frozen[name] = sha256(target) + os.replace(pending, destination) + except Exception: + shutil.rmtree(pending, ignore_errors=True) + raise + raw_names = RAW_EVIDENCE + [name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()] + raw = {name: sha256(out / name) for name in raw_names} + report = { + "schema": "multi_startup_physical_gate_v1", "passed": True, + "case": str(case), "visual_acceptance_pending": True, + "world_audit_shards": world_audit_shards, + "frozen_audit_dir": str(destination), "frozen_audit_sha256": frozen, + "raw_evidence_sha256": raw, + "semantics": "Physical and startup acceptance is frozen before staged export. Visual/manual and official-reader acceptance remain separate required gates.", + "completed_unix": time.time(), + } + write_json(out / "PHYSICAL_GATE_RESULT.json", report) + return report + + +def verify_physical_gate(case: Path) -> dict: + case = Path(case).resolve() + out = case / "rollout" + failures = [] + try: + receipt = json.loads((out / "PHYSICAL_GATE_RESULT.json").read_text()) + frozen_dir = Path(receipt["frozen_audit_dir"]).resolve() + if frozen_dir != (out / "physical_evidence").resolve(): + failures.append("frozen_directory") + for name, digest in receipt.get("frozen_audit_sha256", {}).items(): + path = frozen_dir / name + if not path.is_file() or sha256(path) != digest: + failures.append("frozen:" + name) + for name, digest in receipt.get("raw_evidence_sha256", {}).items(): + path = out / name + if not path.is_file() or sha256(path) != digest: + failures.append("raw:" + name) + if set(receipt.get("frozen_audit_sha256", {})) != set(FROZEN_AUDITS): + failures.append("frozen_scope") + expected_raw = set(RAW_EVIDENCE) | {name for name in OPTIONAL_RAW_EVIDENCE if (out / name).is_file()} + if set(receipt.get("raw_evidence_sha256", {})) != expected_raw: + failures.append("raw_scope") + except Exception as exc: + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": False, + "failures": ["missing_or_invalid_receipt"], "error": repr(exc)} + return {"schema": "multi_startup_physical_gate_verification_v1", "passed": not failures, + "failures": failures, "receipt_sha256": sha256(out / "PHYSICAL_GATE_RESULT.json")} diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/supervise.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/supervise.py new file mode 100644 index 0000000000000000000000000000000000000000..3a3cdaef555b58edcd81e007f1cef7324fe126cc --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/supervise.py @@ -0,0 +1,75 @@ +"""Own new multi-item cases only; preserve every failed or paused native prefix.""" +import os,sys,time,json,fcntl,subprocess +from pathlib import Path +W=Path(__file__).resolve().parent +CPU='/mnt/agent-bench-data/lingjun/envs/mesh311/bin/python' +lock=(W/'supervisor.lock').open('a+');fcntl.flock(lock,fcntl.LOCK_EX|fcntl.LOCK_NB) +children={};reviews={};logs=[] +def read(p,default=None): + try:return json.loads(p.read_text()) + except (FileNotFoundError,json.JSONDecodeError):return default + +def write(p,d): + t=p.with_suffix('.tmp');t.write_text(json.dumps(d,indent=2)+'\n');t.replace(p) + +def start(cmd,log,env=None): + f=log.open('a');logs.append(f) + return subprocess.Popen(cmd,stdout=f,stderr=subprocess.STDOUT,env=env) + +def validation_files_writable(case): + """Do not race the launcher's final recursive chown. + + RESULT is written by the container before launch.py exits. It is not a handoff: + launch.py still owns the case until its finally block has completed. Once that + owner has exited, prove the supervisor account can update STATUS and create an + audit file in rollout before changing status or spawning validate_case. + """ + out=case/'rollout';required=[out/'RESULT.json',out/'recording_60hz.npz',out/'full_state.npz', + out/'PHYSICS_CLOCK.json',out/'top_head.mp4',out/'hand_left.mp4',out/'hand_right.mp4'] + if not all(p.is_file() and os.access(p,os.R_OK) for p in required):return False + if not os.access(case/'STATUS.json',os.W_OK) or not os.access(out,os.W_OK):return False + probe=out/f'.validation_write_probe.{os.getpid()}' + try: + fd=os.open(probe,os.O_WRONLY|os.O_CREAT|os.O_EXCL,0o600);os.close(fd);probe.unlink();return True + except OSError: + try:probe.unlink(missing_ok=True) + except OSError:pass + return False + +while True: + control=read(W/'CONTROL.json',{'enabled':False,'allowed_layouts':[],'gpus':[0]}) + for gpu,(case,p) in list(children.items()): + if p.poll() is None:continue + s=read(case/'STATUS.json',{}) + if s.get('status') not in ['FAILED','PHYSICS_COMPLETE_UNVERIFIED','REVIEW_REQUIRED','ACCEPTED']: + write(case/'STATUS.json',dict(status='REVIEW_REQUIRED',phase='launcher_exit',exit_code=p.returncode,updated=time.time())) + del children[gpu] + for name,(case,p) in list(reviews.items()): + if p.poll() is None:continue + if p.returncode and read(case/'STATUS.json',{}).get('status')!='REVIEW_REQUIRED': + write(case/'STATUS.json',dict(status='REVIEW_REQUIRED',phase='validation_exit',exit_code=p.returncode,updated=time.time())) + del reviews[name] + cases=sorted((W/'cases').glob('layout_*')) + if control.get('enabled'): + for gpu in control.get('gpus',[0]): + if gpu in children:continue + candidates=[c for c in cases if read(c/'STATUS.json',{}).get('status')=='PREPARED' and read(c/'job.json',{}).get('layout_id') in control.get('allowed_layouts',[]) and not (c/'rollout').exists()] + if not candidates:break + case=candidates[0];layout=read(case/'job.json')['layout_id'] + write(case/'STATUS.json',dict(status='LAUNCHING',phase='gpu_reservation',gpu=gpu,updated=time.time())) + p=start([sys.executable,str(W/'launch.py'),str(layout),str(gpu)],case/'launcher.log');children[gpu]=(case,p) + if (W/'VALIDATION_READY').exists(): + native_owned={c.resolve() for c,p in children.values() if p.poll() is None} + for case in cases: + if len(reviews)>=1:break + if (read(case/'STATUS.json',{}).get('status')!='PHYSICS_COMPLETE_UNVERIFIED' + or case.name in reviews or case.resolve() in native_owned + or not validation_files_writable(case)):continue + write(case/'STATUS.json',dict(status='QUEUED_VALIDATION',phase='validation_start',updated=time.time())) + env=dict(os.environ,CHAIN_WORLD_AUDIT_SHARDS='8',CHAIN_PARALLEL_COLLISION_AUDITS='1',OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='1') + p=start([CPU,str(W/'validate_case.py'),str(case)],case/'validation.log',env);reviews[case.name]=(case,p) + states=[{**read(c/'STATUS.json',{}), 'layout_id':read(c/'job.json',{}).get('layout_id'), 'case':str(c)} for c in cases] + accepted=sum(s.get('status')=='ACCEPTED' for s in states) + write(W/'SUPERVISOR.json',dict(pid=os.getpid(),updated=time.time(),requested=10,accepted=accepted,native=[dict(gpu=g,case=str(c),pid=p.pid) for g,(c,p) in children.items()],reviews=[dict(case=str(c),pid=p.pid) for c,p in reviews.values()],cases=states)) + if accepted==10 and not children and not reviews:break + time.sleep(5) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/task_collision_scope.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/task_collision_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..5e29f2540b8f62498affa56eb14fa3e2fa003494 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/task_collision_scope.py @@ -0,0 +1,32 @@ +"""Robot and every requested target stay in scope for the full multi-item run.""" +import copy +POLICY=dict(version='robot_and_all_requested_targets_vs_environment_v2',included=['robot_vs_environment','robot_vs_any_requested_target','every_requested_target_vs_environment','requested_target_vs_requested_target'],excluded=['environment_vs_environment','unrelated_stock_support'],separate_required=['robot_self_collision_all_ticks','per_item_physical_pick_place_success','final_multi_item_nonoverlap'],authority='Multi-item task retains the task-scoped environment omission while keeping the robot and every requested target in scope at every tick.') + +def in_scope(pair,targets): + targets=tuple(targets) + def task_body(path): + if path.startswith(('/G01/','/World/Robot/')):return True + return any(path==target or path.startswith(target+'/') or path.startswith(target+'#') for target in targets) + return any(task_body(str(pair.get(k,''))) for k in ('a','b','a_owner','b_owner')) + +def summarize_scoped(original,report,tolerance,targets,nonphysical_paths=()): + scoped=copy.copy(report);pairs=report.get('pairs',[]);scoped['pairs']=[p for p in pairs if in_scope(p,targets) and not any(str(p.get(k,'')).split('#')[0] in nonphysical_paths for k in ('a','b'))];scoped['unrelated_stock_support']={} + # Keep all_scene_pairs_checked truthful. Remove only the deliberate scope omission. + result=original(scoped,tolerance) + result['unresolved_review']=[x for x in result['unresolved_review'] if x['reason']!='unrelated scene pairs were not checked'] + result['geometry_status']='REJECT' if result['hard_failures'] else 'REVIEW' if result['unresolved_review'] else 'CLEAR_AT_CHECKED_SAMPLES' + result['omitted_nonphysical_decorative_paths']=list(nonphysical_paths);result['collision_scope']=POLICY;result['excluded_environment_pair_count']=len(pairs)-len(scoped['pairs']);result['all_scene_pairs_checked']=bool(report.get('all_scene_pairs_checked')) + result['requested_targets']=list(targets) + result['limitations'].append('Clearance applies to the robot and every requested target, not environment assembly or unrelated object pairs.') + return result + +def install(acceptance,targets,nonphysical_paths=()): + original=acceptance.summarize + acceptance.summarize=lambda report,contact_tolerance_mm=None:summarize_scoped(original,report,contact_tolerance_mm,targets,nonphysical_paths) + + +def nonphysical_rug_paths(stage): + from pxr import Usd,UsdPhysics + root=stage.GetPrimAtPath('/World/Assets/LivingRoomRug') + if not root:return [] + return [str(p.GetPath()) for p in Usd.PrimRange(root) if p.HasAPI(UsdPhysics.CollisionAPI) and p.GetAttribute('physics:collisionEnabled').Get() is False] diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..f9959a05280f366084a12f4d2009627868d11d6b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_carry_target_robot_regression.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +"""Read-only regression for the held-target/robot carry preflight rule.""" +import hashlib +import json +import os +import pathlib +import sys +import time + +import numpy as np +from scipy.spatial.transform import Rotation + +W = pathlib.Path(__file__).resolve().parent +R = W.parents[1] +C = W / "cases/layout_05" +sys.path[:0] = [str(W), str(R / "tools/family3"), str(R / "work/family3_repair/collision/python_deps"), str(R / "code/family3_repair/real2sim/teleop")] +os.environ["SPATIALINDEX_C_LIBRARY"] = str(R / "work/family3_repair/collision/python_deps/rtree.libs/libspatialindex-e5350069.so") + +from pxr import Usd, UsdGeom # noqa: E402 +from collision_scene import Body, ROBOT, Robot, collider_mesh, matrix as usd_matrix, rigid_part # noqa: E402 +from carry_transition import Clearance # noqa: E402 + + +def matrix(pose): + result = np.eye(4) + result[:3, 3] = pose[:3] + result[:3, :3] = Rotation.from_quat(pose[3:]).as_matrix() + return result + + +def main(): + job = json.loads((C / "job.json").read_text()) + item = job["items"][1] + checkpoint_path = C / "rollout/checkpoint_60hz.npz" + checkpoint = np.load(checkpoint_path, allow_pickle=True) + ticks = checkpoint["tick"].astype(int) + phases = np.array([value.decode() if isinstance(value, bytes) else str(value) for value in checkpoint["phase"]]) + names = checkpoint["dof_names"].tolist() + hand_path = "/World/Robot/gripper_r_base_link" + hand_index = checkpoint["robot_link_paths"].tolist().index(hand_path) + target_index = checkpoint["target_paths"].tolist().index(item["target"]) + # Deepest native assigned-finger contact in the accepted pickup prefix. + safe_frame = int(np.flatnonzero(ticks == 10297)[0]) + bad_frames = [int(np.flatnonzero(ticks == tick)[0]) for tick in (15138, 15139)] + prop_poses = { + str(path): matrix(pose) + for path, pose in zip(checkpoint["rigid_body_paths"], checkpoint["prop_pose"][safe_frame]) + } + stage = Usd.Stage.Open(job["scene"]) + robot = Robot(ROBOT) + robot.bodies = [] + vertices = np.load(item["controls_file"])["target_local"] + initial_held = np.linalg.inv(matrix(checkpoint["robot_link_pose"][safe_frame, hand_index])) @ matrix( + checkpoint["target_pose_all"][safe_frame, target_index] + ) + checker = Clearance(stage, robot, names, item["hand"], item["target"], vertices, initial_held, prop_poses) + checker.deadline = time.monotonic() + 900 + allowed = {f"/World/Robot/gripper_r_{side}_link4" for side in ("inner", "outer")} + # Also retain the exact authored physical target cube. The planner target + # hull can sit inside PhysX's contact offsets and have no literal crossing; + # the exact cube exercises the intended-link 10 mm witness on the accepted + # physical grasp state without changing Clearance's production geometry. + cache = UsdGeom.XformCache() + target_prim = stage.GetPrimAtPath(item["target"] + "/CanonicalGraspCollision") + target_owner = stage.GetPrimAtPath(item["target"]) + target_mesh, target_method, target_approximation = collider_mesh(target_prim) + target_mesh.apply_transform( + np.linalg.inv(rigid_part(usd_matrix(cache.GetLocalToWorldTransform(target_owner)))) + @ usd_matrix(cache.GetLocalToWorldTransform(target_prim)) + ) + physical_target = Body(item["target"] + "/CanonicalGraspCollision", target_mesh) + + def target_robot_detail(frame): + q = checkpoint["q"][frame].astype(float) + base = matrix(checkpoint["base_pose"][frame]) + recorded_hand = matrix(checkpoint["robot_link_pose"][frame, hand_index]) + recorded_target = matrix(checkpoint["target_pose_all"][frame, target_index]) + held = np.linalg.inv(recorded_hand) @ recorded_target + checker.held = held + checker.results.clear() + local = robot.fk(names, q, np.eye(4)) + self_ok, self_detail = checker.self_check(local, robot.root) + fk = {name: base @ transform for name, transform in local.items()} + for body in checker.self_check.bodies: + body.update(fk[body.name.replace("/World/Robot", robot.root, 1)]) + checker.target.update(fk[robot.root + "/gripper_r_base_link"] @ held) + physical_target.update(recorded_target) + rows = [] + for body in checker.self_check.bodies: + if not checker.broad_overlap(checker.target, body): + continue + hit = checker.contact(checker.target, body, containment=True) + if hit is None: + continue + witness = None + permitted = False + if body.name in allowed: + witness = checker.separation_witness(checker.target, body, hit, tolerance_mm=10.0, vertical_support=False) + permitted = bool(witness.get("certified")) + rows.append({"robot_body": body.name, "contact": hit, "allowed_distal": body.name in allowed, "separation_witness": witness, "permitted": permitted}) + physical_rows = [] + for body in checker.self_check.bodies: + if not checker.broad_overlap(physical_target, body): + continue + hit = checker.contact(physical_target, body, containment=True) + if hit is None: + continue + witness = None + permitted = False + if body.name in allowed: + witness = checker.separation_witness(physical_target, body, hit, tolerance_mm=10.0, vertical_support=False) + permitted = bool(witness.get("certified")) + physical_rows.append({"robot_body": body.name, "contact": hit, "allowed_distal": body.name in allowed, "separation_witness": witness, "permitted": permitted}) + overall = checker.check(q, base) + return { + "source_control_tick": int(ticks[frame]), + "phase": phases[frame], + "self_and_wrist_ok": bool(self_ok), + "self_and_wrist_detail": self_detail, + "target_robot_intersections": rows, + "target_robot_rule_passed": all(row["permitted"] for row in rows), + "authored_physical_target_robot_intersections": physical_rows, + "authored_physical_target_robot_rule_passed": all(row["permitted"] for row in physical_rows), + "clearance_check_passed": bool(overall), + "clearance_last": None if overall else checker.last, + } + + safe = target_robot_detail(safe_frame) + bad = [target_robot_detail(frame) for frame in bad_frames] + safe_ok = (safe["clearance_check_passed"] and safe["target_robot_rule_passed"] + and bool(safe["authored_physical_target_robot_intersections"]) + and safe["authored_physical_target_robot_rule_passed"] + and {row["robot_body"] for row in safe["authored_physical_target_robot_intersections"]} <= allowed) + bad_ok = all( + not row["clearance_check_passed"] + and row["clearance_last"].get("kind") == "held_target_robot" + and row["clearance_last"].get("robot") == "/World/Robot/body_link2" + for row in bad + ) + result = { + "schema": "multi_item.carry_target_robot_regression.v1", + "passed": bool(safe_ok and bad_ok), + "read_only": True, + "carry_transition_sha256": hashlib.sha256((W / "carry_transition.py").read_bytes()).hexdigest(), + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": hashlib.sha256(checkpoint_path.read_bytes()).hexdigest(), + "accepted_prefix_state": safe, + "confirmed_bad_states": bad, + "assertions": { + "accepted_source_final_pick_has_only_certified_assigned_distal_contacts": bool(safe_ok), + "case5_ticks_15138_15139_reject_body_link2": bool(bad_ok), + "whole_gripper_prefix_exemption_absent": True, + }, + "authored_physical_target_geometry": {"method": target_method, "approximation": target_approximation}, + "limitations": "The safe state is the physically retained case5 final shelf-pick state from the same continuous prefix; runtime bilateral contact evidence remains a separate pickup gate.", + "active_case_written": False, + "physics_executed": False, + } + output = W / "CARRY_TARGET_ROBOT_REGRESSION.json" + output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps({"output": str(output), "passed": result["passed"], "safe": safe, "bad": bad}, indent=2)) + raise SystemExit(0 if result["passed"] else 2) + + +if __name__ == "__main__": + main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_multi_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_multi_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..ab4d7850c3e29e863c0de4285f06b0ae81c68cf4 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_multi_validation.py @@ -0,0 +1,107 @@ +"""Focused contract tests; these do not launch or step the simulator.""" +import gzip +import json +import tempfile +import unittest +from pathlib import Path + +import numpy as np + +from multi_validation import audit_ranges, contact_capture_audit, grasp_retention_audit, recording_contract, validate_phase_order + + +def phases(count=3): + names = ["initial_upper_shelf_posture"] + indices = [-1] + core = ["waist_to_selected_shelf", "shelf_pick", "shelf_exit_reverse", + "forward_navigation", "bridge_to_place_initial", "place_descent", + "place_release", "release_retreat", "settle"] + for item in range(count): + suffixes = core + (["empty_return"] if item < count - 1 else []) + ["complete"] + names += [f"item_{item:02d}_{suffix}" for suffix in suffixes] + indices += [item] * len(suffixes) + return names, np.asarray(indices) + + +class Recording: + def __init__(self, names, indices, targets): + n = len(names) + self.files = ["q", "phase", "item_index", "active_target_path", "target_paths", "target_pose_all"] + self.values = dict(q=np.zeros((n, 1)), phase=np.asarray(names), item_index=indices, + active_target_path=np.asarray(["" if i < 0 else targets[i] for i in indices]), + target_paths=np.asarray(targets), target_pose_all=np.zeros((n, len(targets), 7))) + + def __getitem__(self, key): + return self.values[key] + + +class MultiValidationTests(unittest.TestCase): + def test_valid_order_and_no_final_return(self): + names, indices = phases() + self.assertEqual(len(validate_phase_order(names, indices, 3)), 3) + + def test_live_pilot_initial_item_zero_encoding_is_authentic(self): + names, indices = phases();indices[0]=0;targets=["/A","/B","/C"] + recording=Recording(names,indices,targets);recording.values["active_target_path"][0]=targets[0] + recording_contract(recording,[dict(target=x) for x in targets]) + self.assertEqual(len(validate_phase_order(names,indices,3)),3) + + def test_final_return_is_rejected(self): + names, indices = phases() + names.insert(-1, "item_02_empty_return"); indices = np.insert(indices, -1, 2) + with self.assertRaisesRegex(ValueError, "final item"): + validate_phase_order(names, indices, 3) + + def test_skipping_nonfinal_return_is_rejected(self): + names, indices = phases();at = names.index("item_00_empty_return") + names.pop(at);indices=np.delete(indices,at) + with self.assertRaisesRegex(ValueError, "return empty-handed"): + validate_phase_order(names, indices, 3) + + def test_audit_ranges_are_target_homogeneous_and_complete(self): + names, indices = phases();targets=["/A", "/B", "/C"] + requested=[dict(target=x) for x in targets];ranges=audit_ranges(Recording(names,indices,targets),requested,4,16) + self.assertEqual([x for a,b,_ in ranges for x in range(a,b)],list(range(len(names)))) + active=indices.copy();active[active<0]=0 + self.assertTrue(all(np.all(active[a:b]==item) for a,b,item in ranges)) + + def test_contact_receipt_requires_every_target(self): + requested=[dict(target="/A"),dict(target="/B"),dict(target="/C")] + with tempfile.TemporaryDirectory() as td: + out=Path(td);receipt=dict(requested_targets=["/A","/B","/C"],filter_semantics="robot_or_any_requested_target",all_physics_ticks=True,dropped_rows=0,control_ticks=9,callback_rows=2) + (out/"CONTACT_CAPTURE.json").write_text(json.dumps(receipt)) + with gzip.open(out/"contacts.jsonl.gz","wt") as stream: + for tick,target in enumerate(("/A","/B"),1):stream.write(json.dumps(dict(tick=tick,actors=[target,"/World/Assets/Tray"]))+"\n") + self.assertFalse(contact_capture_audit(out,requested,9)["passed"]) + + def test_contact_rows_align_with_active_item(self): + requested=[dict(target="/A"),dict(target="/B"),dict(target="/C")] + with tempfile.TemporaryDirectory() as td: + out=Path(td);receipt=dict(requested_targets=["/A","/B","/C"],filter_semantics="robot_or_any_requested_target",all_physics_ticks=True,dropped_rows=0,control_ticks=3,callback_rows=3) + (out/"CONTACT_CAPTURE.json").write_text(json.dumps(receipt));indices=np.array([0,1,2]);active=np.array(["/A","/B","/C"]);phase=np.array(["item_00_settle","item_01_settle","item_02_settle"]) + with gzip.open(out/"contacts.jsonl.gz","wt") as stream: + for tick,target in enumerate(active,1):stream.write(json.dumps(dict(tick=tick,item_index=tick-1,active_target_path=target,phase=phase[tick-1],actors=[target,"/World/Assets/Tray"]))+"\n") + self.assertTrue(contact_capture_audit(out,requested,3,indices,active,phase)["passed"]) + + def test_grasp_retention_requires_bilateral_final_contact_and_environment_clearance(self): + phase=np.asarray(["item_00_shelf_pick"]*30);indices=np.zeros(30,dtype=int) + requested=[dict(target="/Target",hand="r")] + rows=[] + for tick in range(1,31): + for finger in ("inner","outer"): + rows.append(dict(tick=tick,actors=["/Target",f"/World/Robot/gripper_r_{finger}_link4"],colliders=[])) + self.assertTrue(grasp_retention_audit(rows,phase,indices,requested)["passed"]) + rows.append(dict(tick=30,actors=["/Target","/World/Assets/Shelf"],colliders=[])) + report=grasp_retention_audit(rows,phase,indices,requested) + self.assertFalse(report["passed"]) + self.assertEqual(report["items"][0]["environment_contact_ticks"],[30]) + + def test_grasp_retention_rejects_height_only_pick_without_contacts(self): + phase=np.asarray(["item_00_shelf_pick"]*30);indices=np.zeros(30,dtype=int) + report=grasp_retention_audit([],phase,indices,[dict(target="/Target",hand="l")]) + self.assertFalse(report["passed"]) + self.assertEqual(report["items"][0]["simultaneous_bilateral_contact_tick_count"],0) + + +if __name__ == "__main__": + unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_return_head_policy.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_return_head_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..0a12ec943068ab5aa12d4e6023fd70c099201c87 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_return_head_policy.py @@ -0,0 +1,74 @@ +"""Behavior checks for head commands at return navigation boundaries.""" +import unittest +from unittest.mock import patch +import numpy as np +import return_head_policy as policy + +NAMES=[f'joint{i}' for i in range(15)]+[policy.BODY_PITCH_JOINT]+list(policy.HEAD_JOINTS)+['grip_l','grip_r'] + +class Robot: + def __init__(self,ctx):self.ctx=ctx + def get_joint_positions(self):return self.ctx['native'].copy() + +class ReturnHeadTests(unittest.TestCase): + def context(self): + q=np.zeros(20);q[15]=np.deg2rad(30);q[16:18]=[.1,.7] + ctx=dict(names=NAMES,control_names=NAMES,ids=np.arange(20),native=q, + return_bridge_q=np.r_[np.zeros(16),.0,.45,.0,.0],rows=[],item_index=0, + stage=None,carry_robot=None,base=None,prop_paths=[],props=[],out=__import__('pathlib').Path('/unused')) + ctx['robot']=Robot(ctx);ctx['mat']=lambda body:np.eye(4) + ctx['constrain']=lambda mode:None;ctx['item_phase']=lambda phase:None;ctx['dump']=lambda path,record:None + def blend(target,seconds): + ctx['native'][:]=target;ctx['rows'].extend(range(round(seconds*60))) + ctx['blend']=blend + return ctx + + @patch('multi_motion.EmptyClearance') + def test_every_return_drive_is_level_then_source_gaze_restored(self,check): + check.return_value.segment.return_value=True + ctx=self.context();moving=[] + def original(primitives,**kwargs): + for primitive in primitives: + if primitive.get('bridge_before'):ctx['blend'](ctx['return_bridge_q'],4.) + moving.append(ctx['native'][16:18].copy()) + ctx['base_vw']=np.zeros(2) + execute=policy.wrap_executor(ctx,original) + source=ctx['return_bridge_q'].copy() + execute([dict(kind='drive',return_stage='table_retreat')],return_motion=True,item_job={}) + self.assertTrue(np.array_equal(ctx['native'][16:18],[0,policy.PITCH_CLIP_RAD[0]])) + execute([dict(kind='drive',return_stage='room_navigation'),dict(kind='drive',bridge_before=True)],return_motion=True,item_job={}) + np.testing.assert_array_equal(moving, [[0,policy.PITCH_CLIP_RAD[0]],[0,policy.PITCH_CLIP_RAD[0]],[0,0]]) + np.testing.assert_array_equal(ctx['native'][16:18],source[16:18]) + np.testing.assert_array_equal(ctx['return_bridge_q'],source) + self.assertEqual(check.return_value.segment.call_count,2) + + @patch('multi_motion.EmptyClearance') + def test_unsafe_head_raise_stops_before_navigation(self,check): + check.return_value.segment.return_value=False;check.return_value.last='collision' + ctx=self.context();calls=[];before=ctx['native'].copy() + with self.assertRaisesRegex(RuntimeError,'not clear'): + policy.wrap_executor(ctx,lambda *a,**k:calls.append(1))([{}],return_motion=True,item_job={}) + self.assertEqual(calls,[]);self.assertEqual(len(ctx['rows']),0) + np.testing.assert_array_equal(ctx['native'],before) + + @patch('multi_motion.EmptyClearance') + def test_planner_receives_same_level_head_as_executor_and_preserves_inputs(self,check): + check.return_value.segment.return_value=True + q=np.arange(20)/100;next_q=q+.02;original=q.copy();next_original=next_q.copy();received=[] + def planner(stage,robot,names,start,base,props,next_base,end,release,job): + received.extend([start.copy(),end.copy()]);return start.copy(),[{'bridge_before':True}],{} + # Job JSON supplies the base transforms as nested lists. The wrapper + # must normalize them before its collision checks. + _,_,report=policy.wrap_planner(planner)(None,None,NAMES,q,np.eye(4).tolist(),{},np.eye(4).tolist(),next_q,{}, {}) + for r,s in zip(received,[q,next_q]): + np.testing.assert_array_equal(r[16:18],policy.head_target(s,NAMES));np.testing.assert_array_equal(r[:16],s[:16]);np.testing.assert_array_equal(r[18:],s[18:]) + np.testing.assert_array_equal(q,original);np.testing.assert_array_equal(next_q,next_original) + self.assertTrue(report['head_policy']['includes_shelf_approach']) + self.assertEqual(check.return_value.segment.call_count,2) + + def test_carry_motion_is_unmodified(self): + ctx=self.context();before=ctx['native'].copy();seen=[] + policy.wrap_executor(ctx,lambda *a,**k:seen.append(k))([{}],return_motion=False,item_job={}) + np.testing.assert_array_equal(ctx['native'],before);self.assertFalse(seen[0]['return_motion']) + +if __name__=='__main__':unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_startup_validation.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_startup_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba19d8e1f9dcee21bcb51a8178537d04f5100d9 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/test_startup_validation.py @@ -0,0 +1,106 @@ +import json +import tempfile +import unittest +from pathlib import Path + +import numpy as np + +import startup_validation as sv + + +class StartupValidationTests(unittest.TestCase): + def test_quaternions_are_normalized_before_angle(self): + q = np.array([[0.0, 0.0, 0.0, 0.99999994]], dtype=np.float32) + self.assertEqual(float(sv.angular_from_reference(q, q[0])[0]), 0.0) + + def test_clock_contract_and_wrong_rate(self): + with tempfile.TemporaryDirectory() as folder: + out = Path(folder) + recording = {"q": np.zeros((31, 1)), "tick": np.arange(1, 32), + "timestamp": np.arange(1, 32) / 60.0} + preroll = {"q": np.zeros((33, 1))} + clock = {"schema": "startup_preroll_clock_v1", "physics_hz": 240, + "control_hz": 60, "training_hz": 20, "substeps_per_control": 4, + "unrecorded_preroll_physics_steps": 132, + "unrecorded_preroll_duration_s": 33 / 60, + "retained_control_rows": 31, "retained_first_control_row": 0, + "retained_first_post_step_global_physics_step": 136, + "retained_timestamp_formula": "(row_index+1)/60", + "retained_post_step_global_physics_step_formula": "unrecorded_preroll_physics_steps + 4*(row_index+1)", + "retained_camera_row_formula": "control_tick % 3 == 0", + "retained_camera_frame_count": 10, "command_semantics": "native", + "contact_semantics": "post-preroll"} + sv.write_json(out / "PHYSICS_CLOCK.json", clock) + self.assertTrue(sv.clock_audit(out, recording, preroll)["passed"]) + clock["physics_hz"] = 120 + sv.write_json(out / "PHYSICS_CLOCK.json", clock) + self.assertFalse(sv.clock_audit(out, recording, preroll)["passed"]) + + def test_camera_exact_tick_and_mount_mapping(self): + with tempfile.TemporaryDirectory() as folder: + out = Path(folder) + rows = 9 + paths = list(sv.CAMERA_LINKS.values()) + pose = np.zeros((rows, 3, 7)); pose[..., 6] = 1 + pose[:, :, 0] = np.arange(rows)[:, None] * 1e-4 + recording = {"q": np.zeros((rows, 1)), "robot_link_paths": np.asarray(paths), + "robot_link_pose": pose} + ticks = np.arange(3, rows + 1, 3) + transforms = sv._pose_matrix_xyzw(pose[ticks - 1]) + np.savez(out / "camera_extrinsics_20hz.npz", camera_keys=np.asarray(list(sv.CAMERAS)), + control_tick=ticks, timestamp=ticks / 60, global_physics_step=120 + 4 * ticks, + world_T_camera=transforms) + clock = {"unrecorded_preroll_physics_steps": 120} + self.assertTrue(sv.camera_extrinsics_audit(out, recording, clock)["passed"]) + np.savez(out / "camera_extrinsics_20hz.npz", camera_keys=np.asarray(list(sv.CAMERAS)), + control_tick=ticks + 1, timestamp=ticks / 60, global_physics_step=120 + 4 * ticks, + world_T_camera=transforms) + self.assertFalse(sv.camera_extrinsics_audit(out, recording, clock)["passed"]) + + def test_prop_scope_rejects_distractor_drop_and_excludes_active_pick(self): + with tempfile.TemporaryDirectory() as folder: + case = Path(folder); out = case / "rollout"; (out / "physics_ticks").mkdir(parents=True) + paths = np.asarray(["/distractor", "/target"]) + initial = np.zeros((2, 7)); initial[:, 6] = 1; initial[:, 2] = 1 + pre = np.repeat(initial[None], 30, axis=0) + np.savez(out / "PREROLL_EVIDENCE.npz", q=np.zeros((30, 1)), prop_paths=paths, + prop_pose=pre, initial_prop_pose_before_preroll=initial) + sv.write_json(out / "INITIAL_PROP_NO_FALL.json", {"passed": True, "physics_samples": 30, + "maxima": {"translation_m": 0, "downward_m": 0, "rotation_rad": 0}, + "all_requested_and_nonrequested_props_included": True}) + retained = np.repeat(initial[None], 8, axis=0) + retained[3:, 0, 2] -= 0.011 + retained[2:4, 1, 0] += 0.20 + np.savez(out / "physics_ticks/full_state.npz", rigid_body_paths=paths, + prop_xyz_m=retained[:, :, :3], prop_wxyz=retained[:, :, [6, 3, 4, 5]]) + sv.write_json(out / "ITEM_EVENTS.json", {"events": [ + {"event": "item_completed", "item_index": 0, "target": "/target", "tick": 5}]}) + recording = {"phase": np.asarray(["initial", "initial", "item_00_shelf_pick", + "item_00_shelf_pick", "item_00_complete", + "item_00_complete", "item_00_complete", "item_00_complete"]), + "item_index": np.zeros(8, dtype=int)} + report = sv.prop_no_fall_audit(case, recording, [{"target": "/target"}]) + self.assertFalse(report["passed"]) + rows = {row["path"]: row for row in report["paths"]} + self.assertFalse(rows["/distractor"]["passed"]) + self.assertTrue(rows["/target"]["passed"]) + + def test_frozen_gate_detects_later_mutation(self): + with tempfile.TemporaryDirectory() as folder: + case = Path(folder); out = case / "rollout"; out.mkdir() + old_frozen, old_raw = sv.FROZEN_AUDITS, sv.RAW_EVIDENCE + try: + sv.FROZEN_AUDITS = ["audit.json"] + sv.RAW_EVIDENCE = ["raw.bin"] + sv.write_json(out / "audit.json", {"passed": True}) + (out / "raw.bin").write_bytes(b"native") + sv.freeze_physical_gate(case, 4) + self.assertTrue(sv.verify_physical_gate(case)["passed"]) + (out / "raw.bin").write_bytes(b"changed") + self.assertFalse(sv.verify_physical_gate(case)["passed"]) + finally: + sv.FROZEN_AUDITS, sv.RAW_EVIDENCE = old_frozen, old_raw + + +if __name__ == "__main__": + unittest.main() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/tray_appearance.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/tray_appearance.py new file mode 100644 index 0000000000000000000000000000000000000000..95a16cd84f09a11ba51ce976946600a06e1af299 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/tray_appearance.py @@ -0,0 +1,10 @@ +"""Original tray, same white/gray/black solid palette as the standalone task.""" +import random,json +from pxr import UsdShade,Sdf,Gf + +def apply(stage,job): + rng=random.Random(json.dumps(['chain_original_tray',job['seed']]));palette={'white':.85,'gray':.14,'black':.025};name=rng.choice(list(palette));value=palette[name] + material=UsdShade.Material.Define(stage,'/World/Looks/ChainOriginalTray');shader=UsdShade.Shader.Define(stage,'/World/Looks/ChainOriginalTray/Surface');shader.CreateIdAttr('UsdPreviewSurface') + shader.CreateInput('diffuseColor',Sdf.ValueTypeNames.Color3f).Set(Gf.Vec3f(value));shader.CreateInput('roughness',Sdf.ValueTypeNames.Float).Set(.5);shader.CreateOutput('surface',Sdf.ValueTypeNames.Token);material.CreateSurfaceOutput().ConnectToSource(shader.ConnectableAPI(),'surface') + UsdShade.MaterialBindingAPI.Apply(stage.GetPrimAtPath('/World/Assets/Tray')).Bind(material,UsdShade.Tokens.strongerThanDescendants) + return dict(policy='standalone_original_neutral_tray_palette',color=name,linear_rgb=[value]*3,texture=None,geometry_changed=False) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_case.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_case.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd041017eeb792fea5d4c07b4a67ba899aae96b --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_case.py @@ -0,0 +1,113 @@ +"""Validate one completed native episode and stage it for independent review. + +This program never publishes or accepts an episode. It freezes physical and +startup evidence, exports a candidate, and leaves manual visual plus official +LeRobot reader QA as separate mandatory gates. +""" +import fcntl +import json +import os +import subprocess +import sys +import time +from pathlib import Path + +W = Path(__file__).resolve().parent +case = Path(sys.argv[1]).resolve() +out = case / "rollout" +CPU = sys.executable +sys.path.insert(0, "/mnt/agent-bench-data/lingjun/ziche/work/family3_repair/collision/python_deps") +from startup_validation import freeze_physical_gate, video_decode_audit +from validate_export import task_audit, write + + +def run(script, args, log, env=None): + with (out / log).open("w") as stream: + result = subprocess.run([CPU, str(W / script), str(case), *args], stdout=stream, + stderr=subprocess.STDOUT, env=env) + if result.returncode: + raise RuntimeError(f"{script} {args} exited {result.returncode}; see {log}") + + +lock_dir = W / "validation_locks" +lock_dir.mkdir(exist_ok=True) +case_lock = (lock_dir / f"{case.name}.lock").open("a+") +fcntl.flock(case_lock, fcntl.LOCK_EX | fcntl.LOCK_NB) +capacity = None +while capacity is None: + for index in range(int(os.environ.get("CHAIN_VALIDATION_CAPACITY", "4"))): + candidate = (lock_dir / f"capacity_{index}.lock").open("a+") + try: + fcntl.flock(candidate, fcntl.LOCK_EX | fcntl.LOCK_NB) + capacity = candidate + break + except BlockingIOError: + candidate.close() + if capacity is None: + time.sleep(10) + +try: + write(case / "STATUS.json", dict(status="VERIFYING", phase="startup_task_views_collisions", + updated=time.time())) + from runtime_evidence import reconcile + reconcile(case) + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Task/startup/schema: " + str(report["failures"])) + + run("audit_views.py", [], "views_audit.log") + for name in ("WRIST_AUDIT.json", "VISIBILITY_AUDIT.json"): + if json.loads((out / name).read_text()).get("passed") is not True: + raise RuntimeError(name + " failed") + + run("audit_geometry.py", ["self"], "self_audit.log") + if json.loads((out / "SELF_COLLISION_AUDIT.json").read_text()).get("passed") is not True: + raise RuntimeError("Native self collision requires replanning") + + world_env = dict(os.environ, OPENBLAS_NUM_THREADS="1", OMP_NUM_THREADS="1") + run("audit_geometry.py", ["world"], "world_audit.log", world_env) + world = json.loads((out / "WORLD_COLLISION_AUDIT.json").read_text()) + if world.get("geometry_status") != "CLEAR_AT_CHECKED_SAMPLES" or world.get("complete_trajectory_checked") is not True: + raise RuntimeError("World collision review is not clear and complete") + + report = task_audit(case) + if not report["passed"]: + raise RuntimeError("Final task/startup/schema: " + str(report["failures"])) + decode = video_decode_audit(out, report["training_frames"]) + if not decode["passed"]: + raise RuntimeError("Full three-view video decode audit failed") + gate = freeze_physical_gate(case, world.get("parallel_shards")) + if gate["passed"] is not True: + raise RuntimeError("Physical gate freeze failed") + + export_python = "/mnt/agent-bench-data/lingjun/ziche/work/shelf_training_target_layer60_20260916/lerobot_env/bin/python" + with (out / "export.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "validate_export.py"), str(case)], + stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Staged export verification failed; see export.log") + run("prepare_visual_qa.py", [], "visual_evidence.log") + dataset = W / "staged_datasets" / case.name + layout = int(json.loads((case / "job.json").read_text())["layout_id"]) + reader_report = W / "qa" / f"layout_{layout:02d}" / "OFFICIAL_LEROBOT_READER_QA.json" + reader_report.parent.mkdir(parents=True, exist_ok=True) + with (out / "official_reader.log").open("w") as stream: + result = subprocess.run([export_python, str(W / "official_lerobot_reader_qa.py"), + "--layout", str(layout), "--dataset", str(dataset), + "--output", str(reader_report)], stdout=stream, stderr=subprocess.STDOUT) + if result.returncode: + raise RuntimeError("Official LeRobot reader QA failed; see official_reader.log") + write(case / "STATUS.json", dict(status="MANUAL_REVIEW_PENDING", phase="first_0p5s_and_all_phase_panels", + frames=report["training_frames"], updated=time.time(), + staged_dataset=str(dataset), physical_gate=str(out / "PHYSICAL_GATE_RESULT.json"), + official_reader=str(reader_report), accepted=False)) +except Exception as exc: + write(case / "STATUS.json", dict(status="REVIEW_REQUIRED", phase="validation_failure", + error=repr(exc), updated=time.time(), accepted=False)) + raise +finally: + if capacity is not None: + fcntl.flock(capacity, fcntl.LOCK_UN) + capacity.close() + fcntl.flock(case_lock, fcntl.LOCK_UN) + case_lock.close() diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_export.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_export.py new file mode 100644 index 0000000000000000000000000000000000000000..ef2944855983730ca700c1c1b78803a54dd62949 --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_export.py @@ -0,0 +1,318 @@ +"""Verify schema/task evidence; export only after independent collision gates pass.""" +import json,sys,shutil,os,hashlib +from pathlib import Path +import numpy as np +W=Path(__file__).resolve().parent +sys.path.insert(0,str(W.parents[1]/'work/family3_repair/collision/python_deps')) +sys.path.insert(0,str(W.parents[1]/'tools/family3')) +# The requested CPU 3.11 environment carries AV but shares the existing +# Python-3.11 Arrow build used by the export workers. +PYARROW_SITE=Path('/mnt/agent-bench-data/lingjun/runs/microwave-batch-20260911/export_env/lib/python3.11/site-packages') +if PYARROW_SITE.exists():sys.path.append(str(PYARROW_SITE)) + +def write(p,d): + p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(d,indent=2,default=lambda x:x.tolist() if hasattr(x,'tolist') else str(x))+'\n') +def link_or_copy(source,destination): + # Container-owned read-only snapshots can forbid hardlinks; retain source permissions. + import errno + try:os.link(source,destination) + except OSError as exc: + if exc.errno not in (errno.EPERM,errno.EACCES,errno.EXDEV):raise + shutil.copy2(source,destination) + with open(source,'rb') as a,open(destination,'rb') as b: + assert hashlib.file_digest(a,'sha256').digest()==hashlib.file_digest(b,'sha256').digest() + return str(destination) + +def pack(z,schema): + names=z['dof_names'].tolist();ids=[names.index(n) for n in schema['action_names'][:20]];samples=np.arange(2,len(z['q']),3) + quat=z['base_pose'][:,3:];x,y,zq,w=quat.T;yaw=np.unwrap(np.arctan2(2*(w*zq+x*y),1-2*(y*y+zq*zq))) + action=np.c_[z['native_target'][:,ids],z['base_action_body']].astype(np.float32) + state=np.c_[z['q'][:,ids],z['base_pose'][:,:2],yaw].astype(np.float32) + assert action.shape==(len(z['q']),22) and state.shape==(len(z['q']),23) + assert np.isfinite(action).all() and np.isfinite(state).all() + return samples,action[samples],state[samples] + +def task_audit(case): + from scipy.spatial.transform import Rotation as Rot + from scipy.spatial import ConvexHull + from shapely.geometry import Polygon + from multi_validation import (contact_capture_audit,controls,grasp_retention_audit,items,item_phase_indices, + recording_contract,validate_phase_order) + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + z=np.load(out/'recording_60hz.npz',allow_pickle=False);phases,item_index,active,target_pose_all=recording_contract(z,requested);phases=np.asarray(phases) + phase_error=None + try:phase_cycles=validate_phase_order(phases,item_index,len(requested)) + except ValueError as exc: + # Preserve a useful audit report for immutable failed native attempts. A + # completed episode still fails this gate; diagnostics continue so schema, + # alignment, contact capture, and completed-prefix behavior can be assessed. + phase_cycles=None;phase_error=str(exc) + schema=json.loads((W/'action_space.schema.json').read_text());samples,action,state=pack(z,schema) + failures=[];checks={} + def check(key,value,detail=None): + checks[key]=dict(passed=bool(value),detail=detail) + if not value:failures.append(key) + result=json.loads((out/'RESULT.json').read_text());expected_targets=[row['target'] for row in requested] + check('physics_finished',result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED',result) + check('all_requested_objects_completed_in_order',result.get('completed_objects')==expected_targets and result.get('requested_objects')==expected_targets and result.get('item_count')==len(requested),result) + # Stable-start evidence is independent of the task and collision reviewers. + # Every report is persisted even on failure so rejected native captures remain + # diagnosable without changing their bytes. + from startup_validation import (camera_extrinsics_audit,camera_warmup_audit,clock_audit, + prop_no_fall_audit,startup_stability_audit) + pre=np.load(out/'PREROLL_EVIDENCE.npz',allow_pickle=False) + clock_report=clock_audit(out,z,pre);write(out/'PHYSICS_CLOCK_AUDIT.json',clock_report) + check('strict_240hz_preroll_retained_clock',clock_report['passed'],clock_report) + startup_report=startup_stability_audit(case,z,schema['action_names']);write(out/'STARTUP_STABILITY_AUDIT.json',startup_report) + check('strict_unrecorded_preroll_and_first_retained_0p5s_stable',startup_report['passed'],startup_report) + preroll_evidence=json.loads((out/'PREROLL_EVIDENCE.json').read_text()) + warmup_report=camera_warmup_audit(out,preroll_evidence,result);write(out/'CAMERA_WARMUP_AUDIT.json',warmup_report) + check('native_three_view_warmup_after_stability',warmup_report['passed'],warmup_report) + camera_report=camera_extrinsics_audit(out,z,clock_report.get('clock',{}));write(out/'CAMERA_EXTRINSICS_AUDIT.json',camera_report) + check('camera_extrinsics_exact_clock_and_rigid_mount',camera_report['passed'],camera_report) + prop_report=prop_no_fall_audit(case,z,requested);write(out/'NON_TARGET_PROP_STABILITY_AUDIT.json',prop_report) + check('all_props_no_fall_and_completed_items_remain_stable',prop_report['passed'],prop_report) + readback_input=out/'collision_readback/INPUT.json' + evidence=json.loads(readback_input.read_text()) if readback_input.exists() else None + check('capture_end_collision_readback_present',evidence is not None,str(readback_input)) + if evidence: + rates=[scene.get('physxScene:timeStepsPerSecond') for scene in evidence['physics_scenes'].values()] + rate_source='capture_end_collision_readback' + else: + clock_path=out/'PHYSICS_CLOCK.json';clock=json.loads(clock_path.read_text()) if clock_path.exists() else {} + rates=[clock.get('physics_hz')] if clock.get('physics_hz') is not None else [] + rate_source='native_physics_clock_receipt' + check('actual_physics_240_hz',bool(rates) and all(hz==240 for hz in rates),rates) + checks['actual_physics_240_hz']['source']=rate_source + check('native_control_60_hz',np.array_equal(z['tick'],np.arange(1,len(z['q'])+1)) and np.allclose(z['timestamp'],np.arange(1,len(z['q'])+1)/60,atol=1e-12)) + readback=json.loads((out/'LIVE_DRIVE_READBACK.json').read_text());check('both_runtime_grippers_80',len(readback)==2 and all(abs(x-80)<1e-5 for x in readback.values()),readback) + check('continuous_per_item_phase_order',phase_error is None,phase_cycles if phase_error is None else phase_error) + # Native continuity and alignment: a single full-state stream must match every + # control row and every requested target pose. No concatenated replay is admitted. + full=np.load(out/'physics_ticks/full_state.npz',allow_pickle=False);n=len(z['q']) + aligned=(len(full['robot_joint_position_rad_or_m'])==n and np.array_equal(full['frame_index'],np.arange(n)) + and full['robot_dof_names'].tolist()==z['dof_names'].tolist() + and np.array_equal(full['robot_joint_position_rad_or_m'],z['q']) + and full['requested_target_paths'].tolist()==expected_targets + and np.array_equal(full['item_index'],z['item_index']) + and full['active_target_path'].tolist()==z['active_target_path'].tolist() + and np.array_equal(full['target_pose_all'],z['target_pose_all']) + and full['robot_link_paths'].tolist()==z['robot_link_paths'].tolist() + and np.array_equal(full['robot_link_xyz_m'],z['robot_link_pose'][:,:,:3]) + and np.array_equal(full['robot_link_wxyz'][:,:,[1,2,3,0]],z['robot_link_pose'][:,:,3:])) + paths=full['rigid_body_paths'].tolist();prop_aligned=True;target_errors={} + for i,target in enumerate(expected_targets): + if target not in paths:prop_aligned=False;target_errors[target]='missing from full-state rigid bodies';continue + p=paths.index(target);pose=np.c_[full['prop_xyz_m'][:,p],full['prop_wxyz'][:,p][:,[1,2,3,0]]] + error=float(np.max(abs(pose-target_pose_all[:,i])));target_errors[target]=error;prop_aligned &= error<=1e-6 + check('single_native_stream_source_alignment',aligned and prop_aligned,dict(episode_uid=str(full['episode_uid']),frames=n,target_max_abs_errors=target_errors)) + metadata=json.loads((out/'recording_metadata.json').read_text());checkpoint=json.loads((out/'CHECKPOINT.json').read_text()) + completed_prefix=result.get('completed_objects',[]) + checkpoint_prefix=checkpoint.get('completed_objects',[]) + prefix_consistent=(checkpoint_prefix==completed_prefix[:len(checkpoint_prefix)] and len(checkpoint_prefix)<=len(completed_prefix) + and int(checkpoint.get('control_ticks',0))<=n) + if result.get('status')=='PHYSICS_COMPLETE_UNVERIFIED':prefix_consistent &= checkpoint_prefix==completed_prefix + check('no_pose_reset_or_stitched_replay',metadata.get('continuous_no_pose_reset') is True and metadata.get('multi_item') is True and checkpoint.get('continuous_process') is True and prefix_consistent,dict(recording_metadata=metadata,checkpoint=checkpoint,result_completed_objects=completed_prefix)) + events=json.loads((out/'ITEM_EVENTS.json').read_text());event_rows=events.get('events',[]);event_order=[(x.get('event'),x.get('item_index'),x.get('target')) for x in event_rows] + expected_events=[(event,i,item['target']) for i,item in enumerate(requested) for event in ('item_started','item_supported','item_completed')] + ticks=[int(x.get('tick',0)) for x in event_rows] + check('item_event_evidence_complete_ordered',events.get('requested_objects')==expected_targets and events.get('completed_objects')==expected_targets and event_order==expected_events and ticks==sorted(ticks) and all(1<=x<=n for x in ticks),dict(events=event_order,ticks=ticks)) + for i,item in enumerate(requested): + supported=event_rows[3*i+1] if len(event_rows)>3*i+1 else {};metrics=supported.get('metrics',{}) + physical=(metrics.get('target')==item['target'] and metrics.get('inside_tray') is True and metrics.get('support_contact_observed') is True + and float(metrics.get('drift_last_second_m',float('inf')))<=.012 + and float(metrics.get('linear_speed_m_s',float('inf')))<=.025 + and float(metrics.get('angular_speed_rad_s',float('inf')))<=.35 + and float(metrics.get('hand_clearance_m',-float('inf')))>=.07 + and bool(metrics.get('tray_support_contact_ticks'))) + check(f'item_{i:02d}_producer_physical_support_gate',physical,metrics) + completed=event_rows[3*i+2] if len(event_rows)>3*i+2 else {} + if i+1=-.001)) + base=z['base_pose'];steps=np.linalg.norm(np.diff(base[:,:2],axis=0),axis=1);check('base_no_teleport',steps.max()<.006,float(steps.max())) + yaw=Rot.from_quat(base[:,3:]).as_euler('xyz')[:,2];velocity=np.diff(base[:,:2],axis=0)*60;lateral=-np.sin(yaw[:-1])*velocity[:,0]+np.cos(yaw[:-1])*velocity[:,1];check('base_no_strafe',np.max(abs(lateral))<.02,float(np.max(abs(lateral)))) + ids=[z['dof_names'].tolist().index(n) for n in schema['action_names'][:20]];targets=z['native_target'][:,ids] + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());limits=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;entry=runtime[name];limits.append([float(entry['physics:lowerLimit'])*unit,float(entry['physics:upperLimit'])*unit]) + limits=np.asarray(limits);check('actual_configured_command_joint_limits',np.all(targets>=limits[:,0]-2e-5) and np.all(targets<=limits[:,1]+2e-5),limits.tolist()) + check('reference_command_envelope_reported',True,dict(within_reference=bool(np.all(targets>=LOWER-2e-5) and np.all(targets<=UPPER+2e-5)),authored_robot_limits_preserved=True)) + check('waist_speed_limit',np.max(abs(np.diff(targets[:,14])))*60<=.1001 and np.max(abs(np.diff(targets[:,15])))*60<=.5001) + from grasp_stability import metrics as stability_metrics + stability=stability_metrics(z,requested);write(out/'GRASP_STABILITY_AUDIT.json',stability);check('held_object_and_hand_stability_every_item',stability['passed'],stability) + floor=Polygon(job['tray_polygon_xy']);final=np.arange(max(0,n-120),n);final_footprints={};item_geometry=[] + for i,item in enumerate(requested): + control=controls(case,item);local=np.asarray(control['target_local']);p=target_pose_all[:,i];settle=item_phase_indices(phases,item_index,i,'settle');pick=item_phase_indices(phases,item_index,i,'shelf_pick') + check(f'item_{i:02d}_target_lifted',len(pick)>0 and p[pick[-1],2]>p[pick[0],2]+.025,dict(start_z=float(p[pick[0],2]) if len(pick) else None,end_z=float(p[pick[-1],2]) if len(pick) else None)) + check(f'item_{i:02d}_settle_duration',len(settle)>=120,len(settle)) + rot=Rot.from_quat(p[final,3:]);verts=np.einsum('nij,vj->nvi',rot.as_matrix(),local)+p[final,None,:3] + footprints=[Polygon(x[ConvexHull(x[:,:2]).vertices,:2]) for x in verts];final_footprints[i]=footprints + bottom=verts[:,:,2].min(1);inside=all(floor.buffer(.001).covers(f) for f in footprints) + check(f'item_{i:02d}_final_tray_containment',inside) + check(f'item_{i:02d}_final_tray_support',np.max(abs(bottom-job['tray_floor_z']))<.006,[float(bottom.min()),float(bottom.max()),job['tray_floor_z']]) + check(f'item_{i:02d}_final_upright',np.min(rot.as_matrix()[:,2,2])>np.cos(np.deg2rad(8))) + check(f'item_{i:02d}_final_stable',np.ptp(p[final,:3],axis=0).max()<.004,np.ptp(p[final,:3],axis=0).tolist()) + completed=item_phase_indices(phases,item_index,i,'complete');after=np.arange(settle[-1],n) if len(settle) else np.array([],dtype=int) + displacement=float(np.max(np.linalg.norm(p[after,:3]-p[after[0],:3],axis=1))) if len(after) else float('inf') + angular=float(np.max((Rot.from_quat(p[after[0],3:]).inv()*Rot.from_quat(p[after,3:])).magnitude())) if len(after) else float('inf') + check(f'item_{i:02d}_not_displaced_by_later_cycles',len(completed)>0 and displacement<.004 and angular.15,float(distance.min())) + if i+1max_overlap:max_overlap=overlap;worst=dict(source_control_tick=int(final[frame_number]+1),item_a=a,item_b=b,overlap_m2=overlap) + check('all_final_items_nonoverlapping',max_overlap<=1e-8,worst or dict(max_overlap_m2=0.)) + # The requested items all begin off the empty tray. + initial_intersection=[] + for i,item in enumerate(requested): + local=np.asarray(controls(case,item)['target_local']);p=target_pose_all[0,i];verts=local@Rot.from_quat(p[3:]).as_matrix().T+p[:3];foot=Polygon(verts[ConvexHull(verts[:,:2]).vertices,:2]);initial_intersection.append(float(foot.intersection(floor).area)) + check('initial_tray_empty_of_requested_items',max(initial_intersection)<=1e-8,initial_intersection) + # Independently inspect every captured nonrobot rigid prop, including objects + # that are not task targets or declared clutter. A metadata count alone does + # not establish that the physical tray began empty. + from pxr import Usd,UsdGeom + from place_tray_layout import mesh_world_points + stage=Usd.Stage.Open(str(out/'scene.usda'));cache=UsdGeom.XformCache();occupants=[];unresolved=[] + for prop_index,path in enumerate(paths): + if path=='/World/Assets/Tray' or path.startswith('/World/Assets/Tray/'): + continue + try: + prim=stage.GetPrimAtPath(path);world=np.asarray(mesh_world_points(stage,prim),float);authored=np.asarray(cache.GetLocalToWorldTransform(prim)).T + local=(np.c_[world,np.ones(len(world))]@np.linalg.inv(authored).T)[:,:3]*np.linalg.norm(authored[:3,:3],axis=0) + current=np.eye(4);current[:3,:3]=Rot.from_quat(full['prop_wxyz'][0,prop_index,[1,2,3,0]]).as_matrix();current[:3,3]=full['prop_xyz_m'][0,prop_index] + vertices=local@current[:3,:3].T+current[:3,3] + footprint=Polygon(vertices[ConvexHull(vertices[:,:2]).vertices,:2]);overlap=float(footprint.intersection(floor).area);bottom=float(vertices[:,2].min());top=float(vertices[:,2].max()) + if overlap>1e-8 and bottom<=job['tray_floor_z']+.025 and top>=job['tray_floor_z']-.005: + occupants.append(dict(path=path,footprint_overlap_m2=overlap,bottom_z_m=bottom,top_z_m=top)) + except Exception as exc: + unresolved.append(dict(path=path,error=repr(exc))) + check('initial_tray_empty_all_recorded_props',not occupants and not unresolved,dict(recorded_props=len(paths),occupants=occupants,unresolved_geometry=unresolved)) + report=dict(schema_version='ziche.shelf_multi10_stable.task_schema_audit.v3',recording_sha256=hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest(),passed=not failures,checks=checks,failures=failures,training_frames=len(samples),action_schema=schema['version'],item_geometry=item_geometry,collision_validation='separate_required_all_ticks_all_requested_targets',startup_validation='strict_unrecorded_preroll_plus_first_retained_0p5s',prop_stability_scope='all_non_targets_full_trace; requested targets pre-pick and after completion') + write(out/'TASK_SCHEMA_AUDIT.json',report);return report + +def export(case): + import pyarrow as pa,pyarrow.parquet as pq + from startup_validation import CAMERAS,sha256,verify_physical_gate + from multi_validation import controls_path,items + case=Path(case);out=case/'rollout';job=json.loads((case/'job.json').read_text());requested=items(job) + physical_verification=verify_physical_gate(case);assert physical_verification['passed'],physical_verification + frozen=out/'physical_evidence';audit=json.loads((frozen/'TASK_SCHEMA_AUDIT.json').read_text());assert audit['passed'],audit['failures'];assert audit['recording_sha256']==hashlib.sha256((out/'recording_60hz.npz').read_bytes()).hexdigest() + for file,field,value in [('SELF_COLLISION_AUDIT.json','passed',True),('WORLD_COLLISION_AUDIT.json','geometry_status','CLEAR_AT_CHECKED_SAMPLES'),('WRIST_AUDIT.json','passed',True),('VISIBILITY_AUDIT.json','passed',True)]: + assert json.loads((frozen/file).read_text())[field]==value,file + schema=json.loads((W/'action_space.schema.json').read_text());z=np.load(out/'recording_60hz.npz');samples,action,state=pack(z,schema);n=len(samples) + decode=json.loads((frozen/'VIDEO_DECODE_AUDIT.json').read_text());assert decode['passed'] and decode['expected_frames']==n + cameras=CAMERAS;image_stats={camera:decode['videos'][camera]['image_stats'] for camera in cameras} + for camera in cameras:assert decode['videos'][camera]['sha256']==sha256(out/(camera+'.mp4')) + destination=W/'staged_datasets'/f"layout_{job['layout_id']:02d}";temp=destination.with_name(destination.name+'.pending');assert not destination.exists() + if temp.exists(): + import time + archive=W/'history/incomplete_exports';archive.mkdir(parents=True,exist_ok=True);temp.rename(archive/(temp.name+'_'+str(time.time_ns()))) + temp.mkdir(parents=True,exist_ok=False) + meta=temp/'meta';meta.mkdir();data=temp/'data/chunk-000';data.mkdir(parents=True) + phase_names=list(dict.fromkeys(z['phase'][samples].tolist()));products=', '.join(row['product'] for row in requested);task=f"Starting with an empty tray, pick up {products} from the shelves one at a time, carry and place each on the coffee-table tray, returning to the next shelf between items." + clock=json.loads((out/'PHYSICS_CLOCK.json').read_text());preroll_duration=float(clock['unrecorded_preroll_duration_s']) + columns={'action':pa.array(action.tolist(),type=pa.list_(pa.float32(),22)),'observation.state':pa.array(state.tolist(),type=pa.list_(pa.float32(),23)),'timestamp':pa.array(np.arange(n,dtype=np.float32)/20),'simulator_time_s':pa.array((preroll_duration+(samples+1)/60).astype(np.float32)),'source_control_tick':pa.array(samples+1),'frame_index':pa.array(np.arange(n)),'index':pa.array(np.arange(n)),'episode_index':pa.array(np.zeros(n,dtype=np.int64)),'task_index':pa.array(np.zeros(n,dtype=np.int64)),'phase_index':pa.array([phase_names.index(p) for p in z['phase'][samples]]),'item_index':pa.array(z['item_index'][samples].astype(np.int64)),'next.done':pa.array(np.arange(n)==n-1)} + parquet=data/'episode_000000.parquet';pq.write_table(pa.table(columns),parquet,compression='zstd');read=pq.read_table(parquet) + assert np.array_equal(np.array(read['action'].to_pylist(),np.float32),action) and np.array_equal(np.array(read['observation.state'].to_pylist(),np.float32),state) + assert np.array_equal(read['source_control_tick'].to_numpy(),samples+1) + assert np.array_equal(read['item_index'].to_numpy(),z['item_index'][samples]) + assert np.array_equal(read['phase_index'].to_numpy(),np.array([phase_names.index(p) for p in z['phase'][samples]])) + def statistics(a):return {k:v.tolist() for k,v in dict(min=a.min(0),max=a.max(0),mean=a.mean(0),std=a.std(0),count=np.array([len(a)])).items()} + stats={'action':statistics(action.astype(float)),'observation.state':statistics(state.astype(float))};features={k:dict(dtype='float32',shape=[len(schema[n])],names=schema[n]) for k,n in [('action','action_names'),('observation.state','state_names')]} + for key in columns: + if key not in features:features[key]=dict(dtype='bool' if key=='next.done' else 'float32' if 'time' in key else 'int64',shape=[1],names=None) + for camera,(width,height) in cameras.items(): + key='observation.images.'+camera;p=temp/'videos/chunk-000'/key;p.mkdir(parents=True);link_or_copy(out/(camera+'.mp4'),p/'episode_000000.mp4') + features[key]=dict(dtype='video',shape=[height,width,3],names=['height','width','channel'],video_info={'video.fps':20,'video.codec':'h264','video.pix_fmt':'yuv420p','video.is_depth_map':False,'has_audio':False});stats[key]=image_stats[camera] + write(meta/'info.json',dict(codebase_version='v2.1',robot_type='G1_omnipicker',total_episodes=1,total_frames=n,total_tasks=1,total_videos=3,total_chunks=1,chunks_size=1000,fps=20,splits={'train':'0:1'},features=features,data_path='data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet',video_path='videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4')) + write(meta/'stats.json',stats) + for name,row in [('tasks',dict(task_index=0,task=task)),('episodes',dict(episode_index=0,tasks=[task],length=n)),('episodes_stats',dict(episode_index=0,stats=stats))]:(meta/(name+'.jsonl')).write_text(json.dumps(row)+'\n') + sim=temp/'sim_info/episode_000000';sim.mkdir(parents=True) + for root in [out,case]: + for p in root.iterdir(): + if p.is_file() and p.suffix in ['.json','.npz','.usda'] and not (sim/p.name).exists():link_or_copy(p,sim/p.name) + if (out/'contacts.jsonl.gz').exists():link_or_copy(out/'contacts.jsonl.gz',sim/'contacts.jsonl.gz') + shutil.copytree(out/'physical_evidence',sim/'physical_evidence',copy_function=link_or_copy) + if (out/'startup_frames').exists():shutil.copytree(out/'startup_frames',sim/'startup_frames',copy_function=link_or_copy) + shutil.copytree(case/'code_snapshot',sim/'replay_code');shutil.copy2(W/'action_space.schema.json',sim/'action_space.schema.json') + validation_code=sim/'validation_code';validation_code.mkdir();validation_hashes={} + for name in ['validate_case.py','validate_export.py','audit_geometry.py','audit_views.py','grasp_stability.py','parallel_world_audit.py','task_collision_scope.py','multi_validation.py','startup_validation.py','prepare_visual_qa.py','official_lerobot_reader_qa.py','record_manual_review.py','validate_staged.py']: + source=W/name;shutil.copy2(source,validation_code/name);validation_hashes[name]=hashlib.sha256(source.read_bytes()).hexdigest() + write(sim/'VALIDATION_CODE_HASHES.json',validation_hashes) + # Planning repairs are part of the native execution history. Keep the + # producer-authored files byte-for-byte, and add a portable manifest because + # PLANNING_WAITS records the producer's absolute repair path. + repairs=case/'repairs';repair_evidence=[] + if repairs.exists(): + exported_repairs=sim/'repairs';exported_repairs.mkdir() + for source in sorted(p for p in repairs.rglob('*') if p.is_file()): + relative=source.relative_to(repairs);destination=exported_repairs/relative + destination.parent.mkdir(parents=True,exist_ok=True);link_or_copy(source,destination) + repair_evidence.append(dict(source_case_relative=str(Path('repairs')/relative), + exported_file=str(Path('repairs')/relative), + sha256=hashlib.sha256(destination.read_bytes()).hexdigest(), + bytes=destination.stat().st_size)) + waits=[] + waits_path=out/'PLANNING_WAITS.json' + if waits_path.exists(): + for row in json.loads(waits_path.read_text()): + source_path=Path(row['repair_file']) if row.get('repair_file') else None + # A later retry may overwrite the ordinary filename after archiving the + # applied bytes as repairs/applied/.json. The native receipt digest + # is authoritative; filenames are only a fallback for legacy receipts. + recorded_hash=row.get('repair_sha256') + match=next((entry for entry in repair_evidence if recorded_hash and entry['sha256']==recorded_hash),None) + if match is None and not recorded_hash and source_path is not None: + match=next((entry for entry in repair_evidence + if Path(entry['source_case_relative']).name==source_path.name),None) + waits.append(dict(item_index=row.get('item_index'),kind=row.get('kind'),tick=row.get('tick'), + native_repair_file=row.get('repair_file'),native_repair_sha256=row.get('repair_sha256'), + exported_file=match['exported_file'] if match else None, + portable_match=match is not None)) + manifest=dict(schema='ziche.repair_evidence.v1', + source_case=str(case),files=repair_evidence,planning_wait_references=waits, + native_planning_waits_preserved=True,all_referenced_repairs_portable=all(row['portable_match'] for row in waits)) + write(sim/'REPAIR_EVIDENCE_MANIFEST.json',manifest) + assert manifest['all_referenced_repairs_portable'],waits + control_sources=[] + for i,row in enumerate(requested): + source=controls_path(case,row);destination=sim/f'item_{i:02d}_controls.npz';link_or_copy(source,destination) + control_sources.append(dict(item_index=i,target=row['target'],source=str(source),exported_file=destination.name,sha256=hashlib.sha256(destination.read_bytes()).hexdigest())) + write(sim/'MULTI_ITEM_CONTROLS.json',dict(items=control_sources,all_controls_preserved=True)) + for name in ['collision_readback','collision_readback_nonrobot','world_geometry']: + if (out/name).exists():shutil.copytree(out/name,sim/name,copy_function=link_or_copy) + from motion_limits import LOWER,UPPER + runtime=json.loads((out/'robot_and_camera_parameters.json').read_text());actual=[] + for name in schema['action_names'][:20]: + unit=1 if name=='idx01_body_joint1' else np.pi/180;actual.append([float(runtime[name]['physics:lowerLimit'])*unit,float(runtime[name]['physics:upperLimit'])*unit]) + write(sim/'ROBOT_CONFIGURATION_DIFFERENCES.json',dict(fields_units_timing_match_reference=True,authored_robot_parameters_preserved_except_requested_gripper_effort=True,runtime_gripper_effort_Nm={'left':80,'right':80},joint_names=schema['action_names'][:20],actual_joint_limits=actual,reference_joint_limits=np.c_[LOWER,UPPER].tolist(),joint_limits_match_reference=bool(np.allclose(actual,np.c_[LOWER,UPPER],atol=2e-5)),note='The source shelf robot arm joint configuration differs from the reference robot. Joint-limit and drive settings were not replaced to unify data fields. Use actual limits and raw data statistics for training.')) + + write(sim/'sim_info.json',dict(action_schema=schema['version'],physics_hz=240,control_hz=60,training_hz=20,action_names=schema['action_names'],state_names=schema['state_names'],phase_names=phase_names,item_index_semantics='0..M-1 identify the active requested target; initial_upper_shelf_posture may authentically use either -1 with an empty active path or 0 with target0, as disambiguated by phase',requested_items=[dict(item_index=i,target=row['target'],product=row['product'],hand=row['hand'],pick_source=row['pick_source'],controls_file=row['controls_file'],destination_xyz=row['destination_xyz']) for i,row in enumerate(requested)],initial_tray_items=0,continuous_native_episode=True,action_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['action_names'][:20]]+['m/s','rad/s'],state_units=['m' if n=='idx01_body_joint1' else 'rad' for n in schema['state_names'][:20]]+['m','m','rad'],normalization=None,time_semantics='same-step applied command and post-step measured state; training samples arange(2,N,3); dataset timestamp begins at zero while simulator_time_s includes the unrecorded stable-start preroll',unrecorded_preroll_duration_s=preroll_duration,image_generation_mode='native_live_three_view',manual_visual_review_required=True,gripper_max_effort_Nm={'left':80,'right':80},base_policy='stationary_turn_or_straight_body_forward_v1',source_layout=job['layout'])) + alignment=dict(passed=True,checked_frames=n,action_exact_float32=True,state_exact_float32=True,item_index_exact=True,phase_index_exact=True,source_ticks=(samples+1).tolist(),simulator_time_includes_preroll=True,unrecorded_preroll_duration_s=preroll_duration) + write(sim/'PARQUET_ALIGNMENT_CHECK.json',alignment) + provenance_files=['PHYSICAL_GATE_RESULT.json','PHYSICS_CLOCK.json','PREROLL_EVIDENCE.json','PREROLL_EVIDENCE.npz','PASSIVE_FINGER_PROJECTION.json','CAMERA_WARMUP.json','camera_extrinsics_20hz.npz','VIDEO_DECODE_AUDIT.json','TASK_SCHEMA_AUDIT.json','NON_TARGET_PROP_STABILITY_AUDIT.json','STARTUP_STABILITY_AUDIT.json','CAMERA_WARMUP_AUDIT.json','CAMERA_EXTRINSICS_AUDIT.json'] + provenance=dict(schema='ziche.multi10_stable_start_provenance.v1',passed=True,image_generation_mode='native_live_three_view',source_case=str(case),continuous_native_episode=True,actual_physics_hz=240,all_original_command_rows_retained=True,unrecorded_preroll_duration_s=preroll_duration,physical_gate_verification=physical_verification,evidence_sha256={name:sha256(sim/name) for name in provenance_files},video_sha256={camera:sha256(out/(camera+'.mp4')) for camera in cameras},parquet_sha256=sha256(parquet),validation_code_sha256=validation_hashes,visual_acceptance_pending=True,official_reader_pending=True) + write(sim/'STARTUP_PREROLL_PROVENANCE.json',provenance) + temp.rename(destination);return destination +if __name__=='__main__': + case=Path(sys.argv[1]);print(task_audit(case) if '--audit-only' in sys.argv else export(case)) diff --git a/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_staged.py b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_staged.py new file mode 100644 index 0000000000000000000000000000000000000000..88d5f8ee86d702f799a7b69c4d5b66d63e51f97c --- /dev/null +++ b/auxiliary/retained5080/auxiliary/previous5066/auxiliary/previous5048/auxiliary/previous5026/auxiliary/additional_shelf10_20261025/auxiliary/native_source_files/episode_000008/sim_info/episode_000000/replay_code/validate_staged.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +"""Admit a staged candidate to VALIDATION_READY after independent QA only.""" +import argparse +import hashlib +import json +import time +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from startup_validation import sha256, verify_physical_gate, write_json + +W = Path(__file__).resolve().parent +CAMERAS = ("top_head", "hand_left", "hand_right") + + +def load(path): + return json.loads(Path(path).read_text()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--layout", type=int, required=True) + args = parser.parse_args() + lid = f"{args.layout:02d}" + case = (W / "cases" / f"layout_{lid}").resolve() + out = case / "rollout" + dataset = W / "staged_datasets" / f"layout_{lid}" + sim = dataset / "sim_info/episode_000000" + evidence_dir = W / "qa" / f"layout_{lid}" / "visual_evidence" + panels_path = evidence_dir / "PANELS.json" + manual_path = W / "qa" / f"layout_{lid}" / "ROOT_MANUAL_REVIEW.json" + reader_path = W / "qa" / f"layout_{lid}" / "OFFICIAL_LEROBOT_READER_QA.json" + failures = [] + + physical = verify_physical_gate(case) + if not physical["passed"]: + failures.append("immutable_physical_gate") + provenance = load(sim / "STARTUP_PREROLL_PROVENANCE.json") + evidence_hashes = provenance.get("evidence_sha256", {}) + bad_evidence = [name for name, digest in evidence_hashes.items() + if not (sim / name).is_file() or sha256(sim / name) != digest] + if bad_evidence: + failures.append("exported_provenance:" + ",".join(bad_evidence)) + + panels = load(panels_path) + panel_hashes = {Path(row["path"]).name: row["sha256"] for row in panels["panels"]} + if any(not (evidence_dir / name).is_file() or sha256(evidence_dir / name) != digest + for name, digest in panel_hashes.items()): + failures.append("visual_panel_hashes") + manual = load(manual_path) + manual_ok = (manual.get("schema") == "ziche.multi10.root_manual_visual_review.v1" + and manual.get("layout_id") == args.layout and manual.get("passed") is True + and manual.get("panels") == panel_hashes + and manual.get("reviewed_all_three_views") is True + and manual.get("reviewed_first_retained_0p5s") is True + and manual.get("reviewed_every_phase_midpoint") is True) + if not manual_ok: + failures.append("manual_visual_review") + + reader = load(reader_path) + parquet = dataset / "data/chunk-000/episode_000000.parquet" + reader_ok = (reader.get("passed") is True and reader.get("layout_id") == args.layout + and reader.get("parquet_sha256") == sha256(parquet)) + if not reader_ok: + failures.append("official_lerobot_reader") + + decode = load(sim / "VIDEO_DECODE_AUDIT.json") + video_hashes = {} + for camera in CAMERAS: + video = dataset / "videos/chunk-000" / f"observation.images.{camera}" / "episode_000000.mp4" + video_hashes[camera] = sha256(video) + if (video_hashes[camera] != provenance["video_sha256"].get(camera) + or video_hashes[camera] != decode["videos"][camera]["sha256"] + or decode["videos"][camera].get("passed") is not True): + failures.append("video:" + camera) + + table = pq.read_table(parquet) + recording = np.load(out / "recording_60hz.npz", allow_pickle=False) + samples = np.arange(2, len(recording["q"]), 3) + ticks = np.asarray(table["source_control_tick"].to_numpy(), dtype=np.int64) + items = np.asarray(table["item_index"].to_numpy(), dtype=np.int64) + clock = load(out / "PHYSICS_CLOCK.json") + sim_time = np.asarray(table["simulator_time_s"].to_numpy(), dtype=np.float64) + alignment = (len(table) == len(samples) and np.array_equal(ticks, samples + 1) + and np.array_equal(items, recording["item_index"][samples]) + and np.allclose(sim_time, float(clock["unrecorded_preroll_duration_s"]) + + (samples + 1) / 60.0, atol=2e-6, rtol=0)) + if not alignment: + failures.append("staged_source_alignment") + + report = {"schema": "ziche.multi10.staged_final_qa.v1", "layout_id": args.layout, + "passed": not failures, "failures": failures, "case": str(case), + "dataset": str(dataset), "physical_gate_verification": physical, + "provenance_sha256": sha256(sim / "STARTUP_PREROLL_PROVENANCE.json"), + "manual_review": {"path": str(manual_path), "sha256": sha256(manual_path), "passed": manual_ok}, + "official_reader": {"path": str(reader_path), "sha256": sha256(reader_path), "passed": reader_ok}, + "panels_receipt_sha256": sha256(panels_path), "video_sha256": video_hashes, + "parquet_sha256": sha256(parquet), "staged_source_alignment": alignment, + "status_semantics": "VALIDATION_READY is review complete but not publication or ACCEPTED", + "completed_unix": time.time()} + qa_path = W / "qa" / f"layout_{lid}.json" + write_json(qa_path, report) + if not report["passed"]: + raise SystemExit(1) + write_json(case / "STATUS.json", {"status": "VALIDATION_READY", "phase": "review_complete", + "accepted": False, "dataset": str(dataset), "qa": str(qa_path), + "qa_sha256": sha256(qa_path), "updated": time.time()}) + print(qa_path) + + +if __name__ == "__main__": + main()