Consolidate paper experiment assets by task and run (1-150)

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/README.md

@@ -0,0 +1,5 @@
+# Ant QwenOFT mean-profile demonstrations
+
+100 accepted unique-seed episodes, each1000steps, strict raw episode return>2500. Split90training/10validation episodes,90000/10000rows. State27D, eight native continuous torques, current224x224RGB image, horizon1. Fixed mean90.56460638563993ms from the archived RTX3090 OpenVLA/QwenOFT profile; quantized to one10Hz raw frame,100ms effective. This new leaf preserves existing OpenVLA folder naming while identifying the actual downstream model asQwenOFT.
+
+Only raw demonstrations are distributed, preserving the repository convention. Training-host LeRobot conversion completed; recipe/expected metadata/audit/source versions are included. Teacher completed10,006,528frames and inference weights were uploaded/readback verified; teacher best is selected after full10Mbudget. This is data/teacher preparation, not completed VLA training or benchmark acceptance.

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/README.md

@@ -0,0 +1 @@
+Use latency-sensitive-bench@c3c6a39365a151e9b7a5e215452fd64e957c2b29 plus published preparation patches. Run convert_to_lerobot.py demonstrations/raw DEST/lerobot. The converter also creates DEST/lerobot__val and DEST/_generated_mixtures. Expected metadata and audit are included; raw image/state/action values are retained. LeRobot data remain on training host, following this repository raw-only distribution.

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/convert_to_lerobot.py

@@ -0,0 +1,4 @@
+import sys
+from pathlib import Path
+from latency_bench.data.exporters.starvla_lerobot import convert_canonical_rollout_to_lerobot
+convert_canonical_rollout_to_lerobot(Path(sys.argv[1]), Path(sys.argv[2]), action_carrier="native")

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/_generated_mixtures/lerobot.json

@@ -0,0 +1,16 @@
+{
+  "lerobot": [
+    [
+      "lerobot",
+      1.0,
+      "rl_games_gymnasium"
+    ]
+  ],
+  "lerobot__val": [
+    [
+      "lerobot__val",
+      1.0,
+      "rl_games_gymnasium"
+    ]
+  ]
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot/manifest.json

@@ -0,0 +1,232 @@
+{
+  "dataset_name": "lerobot",
+  "env_name": "ant_rgb_state",
+  "episodes": 90,
+  "frames": 90000,
+  "task_prompts": [
+    "Make the Ant move forward as fast as possible without falling. Predict eight continuous torques in [-1, 1] ordered as back right hip, back right ankle, front left hip, front left ankle, front right hip, front right ankle, back left hip, and back left ankle. Current action latency is 1 raw frames (100.00 ms).  The environment runs at 10 FPS and observations are emitted at 10 FPS. Choose the best next action."
+  ],
+  "format": "lerobot_v2_image_parquet",
+  "source": "/home/ubuntu/lzj/mean-profiling/ant/data/raw",
+  "integration_name": "gymnasium",
+  "task_name": "ant_rgb_state",
+  "action_layout": "gymnasium_continuous_v1",
+  "action_labels": [
+    "back_right_hip_torque",
+    "back_right_ankle_torque",
+    "front_left_hip_torque",
+    "front_left_ankle_torque",
+    "front_right_hip_torque",
+    "front_right_ankle_torque",
+    "back_left_hip_torque",
+    "back_left_ankle_torque"
+  ],
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+    "back_right_ankle_torque",
+    "front_left_hip_torque",
+    "front_left_ankle_torque",
+    "front_right_hip_torque",
+    "front_right_ankle_torque",
+    "back_left_hip_torque",
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+  ],
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+  "action_carrier": "native",
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+  "uses_state": true,
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+    "torso_quaternion_w",
+    "torso_quaternion_x",
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+    "front_left_hip_angle",
+    "front_left_ankle_angle",
+    "front_right_hip_angle",
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+    "torso_x_velocity",
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+    "torso_angular_velocity_x",
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+    "front_left_hip_angular_velocity",
+    "front_left_ankle_angular_velocity",
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+    "front_right_ankle_angular_velocity",
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+    "back_left_ankle_angular_velocity",
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+  "custom_mixtures_path": "/home/ubuntu/lzj/mean-profiling/ant/data/_generated_mixtures/lerobot.json",
+  "gymnasium_task": {
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+    "base_prompt": "Make the Ant move forward as fast as possible without falling. Predict eight continuous torques in [-1, 1] ordered as back right hip, back right ankle, front left hip, front left ankle, front right hip, front right ankle, back left hip, and back left ankle.",
+    "env_fps": 10.0,
+    "env_id": "LatencyBench/AntContinuous-v0",
+    "frame_stack": 1,
+    "make_kwargs": {
+      "base_env_id": "Ant-v4",
+      "base_make_kwargs": {
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+        "use_contact_forces": false
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+    "registration_imports": [
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+    ],
+    "state_space": {
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+        "torso_quaternion_w",
+        "torso_quaternion_x",
+        "torso_quaternion_y",
+        "torso_quaternion_z",
+        "front_left_hip_angle",
+        "front_left_ankle_angle",
+        "front_right_hip_angle",
+        "front_right_ankle_angle",
+        "back_left_hip_angle",
+        "back_left_ankle_angle",
+        "back_right_hip_angle",
+        "back_right_ankle_angle",
+        "torso_x_velocity",
+        "torso_y_velocity",
+        "torso_z_velocity",
+        "torso_angular_velocity_x",
+        "torso_angular_velocity_y",
+        "torso_angular_velocity_z",
+        "front_left_hip_angular_velocity",
+        "front_left_ankle_angular_velocity",
+        "front_right_hip_angular_velocity",
+        "front_right_ankle_angular_velocity",
+        "back_left_hip_angular_velocity",
+        "back_left_ankle_angular_velocity",
+        "back_right_hip_angular_velocity",
+        "back_right_ankle_angular_velocity"
+      ]
+    },
+    "task_name": "ant_rgb_state"
+  },
+  "validation_dataset_name": "lerobot__val",
+  "validation_episodes": 10,
+  "validation_frames": 10000
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot/meta/episodes.jsonl

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+{"episode_index": 0, "length": 1000}
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latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot/meta/info.json

@@ -0,0 +1,111 @@
+{
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+  "fps": 10.0,
+  "total_episodes": 90,
+  "total_frames": 90000,
+  "total_tasks": 1,
+  "total_videos": 0,
+  "total_chunks": 1,
+  "chunks_size": 1000,
+  "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",
+  "features": {
+    "observation.image": {
+      "dtype": "image",
+      "shape": [
+        224,
+        224,
+        3
+      ],
+      "names": [
+        "height",
+        "width",
+        "channel"
+      ],
+      "video_info": {
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+      }
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+    "observation.state": {
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+        "torso_quaternion_w",
+        "torso_quaternion_x",
+        "torso_quaternion_y",
+        "torso_quaternion_z",
+        "front_left_hip_angle",
+        "front_left_ankle_angle",
+        "front_right_hip_angle",
+        "front_right_ankle_angle",
+        "back_left_hip_angle",
+        "back_left_ankle_angle",
+        "back_right_hip_angle",
+        "back_right_ankle_angle",
+        "torso_x_velocity",
+        "torso_y_velocity",
+        "torso_z_velocity",
+        "torso_angular_velocity_x",
+        "torso_angular_velocity_y",
+        "torso_angular_velocity_z",
+        "front_left_hip_angular_velocity",
+        "front_left_ankle_angular_velocity",
+        "front_right_hip_angular_velocity",
+        "front_right_ankle_angular_velocity",
+        "back_left_hip_angular_velocity",
+        "back_left_ankle_angular_velocity",
+        "back_right_hip_angular_velocity",
+        "back_right_ankle_angular_velocity"
+      ]
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+        "back_right_ankle_torque",
+        "front_left_hip_torque",
+        "front_left_ankle_torque",
+        "front_right_hip_torque",
+        "front_right_ankle_torque",
+        "back_left_hip_torque",
+        "back_left_ankle_torque"
+      ]
+    },
+    "timestamp": {
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+    "frame_index": {
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+  }
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot/meta/modality.json

@@ -0,0 +1,30 @@
+{
+  "state": {
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+      "end": 27,
+      "dtype": "float32",
+      "absolute": true,
+      "original_key": "observation.state"
+    }
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+  "video": {
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+    }
+  },
+  "annotation": {
+    "human.task_description": {
+      "original_key": "task_index"
+    }
+  }
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot/meta/tasks.jsonl

@@ -0,0 +1 @@
+{"task": "Make the Ant move forward as fast as possible without falling. Predict eight continuous torques in [-1, 1] ordered as back right hip, back right ankle, front left hip, front left ankle, front right hip, front right ankle, back left hip, and back left ankle. Current action latency is 1 raw frames (100.00 ms).  The environment runs at 10 FPS and observations are emitted at 10 FPS. Choose the best next action.", "task_index": 0}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot__val/manifest.json

@@ -0,0 +1,230 @@
+{
+  "dataset_name": "lerobot__val",
+  "env_name": "ant_rgb_state",
+  "episodes": 10,
+  "frames": 10000,
+  "task_prompts": [
+    "Make the Ant move forward as fast as possible without falling. Predict eight continuous torques in [-1, 1] ordered as back right hip, back right ankle, front left hip, front left ankle, front right hip, front right ankle, back left hip, and back left ankle. Current action latency is 1 raw frames (100.00 ms).  The environment runs at 10 FPS and observations are emitted at 10 FPS. Choose the best next action."
+  ],
+  "format": "lerobot_v2_image_parquet",
+  "source": "/home/ubuntu/lzj/mean-profiling/ant/data/raw",
+  "integration_name": "gymnasium",
+  "task_name": "ant_rgb_state",
+  "action_layout": "gymnasium_continuous_v1",
+  "action_labels": [
+    "back_right_hip_torque",
+    "back_right_ankle_torque",
+    "front_left_hip_torque",
+    "front_left_ankle_torque",
+    "front_right_hip_torque",
+    "front_right_ankle_torque",
+    "back_left_hip_torque",
+    "back_left_ankle_torque"
+  ],
+  "carrier_action_labels": [
+    "back_right_hip_torque",
+    "back_right_ankle_torque",
+    "front_left_hip_torque",
+    "front_left_ankle_torque",
+    "front_right_hip_torque",
+    "front_right_ankle_torque",
+    "back_left_hip_torque",
+    "back_left_ankle_torque"
+  ],
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+  "active_action_dim": 8,
+  "action_carrier": "native",
+  "rows_unit": "decision_step",
+  "obs_fps": 10.0,
+  "obs_stride_raw_frames": 1,
+  "uses_state": true,
+  "state_dim": 27,
+  "state_labels": [
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+    "torso_quaternion_w",
+    "torso_quaternion_x",
+    "torso_quaternion_y",
+    "torso_quaternion_z",
+    "front_left_hip_angle",
+    "front_left_ankle_angle",
+    "front_right_hip_angle",
+    "front_right_ankle_angle",
+    "back_left_hip_angle",
+    "back_left_ankle_angle",
+    "back_right_hip_angle",
+    "back_right_ankle_angle",
+    "torso_x_velocity",
+    "torso_y_velocity",
+    "torso_z_velocity",
+    "torso_angular_velocity_x",
+    "torso_angular_velocity_y",
+    "torso_angular_velocity_z",
+    "front_left_hip_angular_velocity",
+    "front_left_ankle_angular_velocity",
+    "front_right_hip_angular_velocity",
+    "front_right_ankle_angular_velocity",
+    "back_left_hip_angular_velocity",
+    "back_left_ankle_angular_velocity",
+    "back_right_hip_angular_velocity",
+    "back_right_ankle_angular_velocity"
+  ],
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latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot__val/meta/episodes.jsonl

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+{"episode_index": 0, "length": 1000}
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+{"episode_index": 7, "length": 1000}
+{"episode_index": 8, "length": 1000}
+{"episode_index": 9, "length": 1000}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot__val/meta/info.json

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latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot__val/meta/modality.json

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latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/conversion/expected/lerobot__val/meta/tasks.jsonl

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latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/raw/filter_report.json

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+{
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+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/demonstrations/raw/metadata.json

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+    "continuous_tanh_scale": 0.0,
+    "decoder_mlp_layers": [],
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+      64
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+    "env_gpu_actions": false,
+    "env_gpu_observations": true,
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+    "git_repo_name": "not a git repository",
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+    "gym_env_id": "LatencyBench/AntContinuous-v0",
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+        "back_left_hip_angle",
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+        "back_left_ankle_angular_velocity",
+        "back_right_hip_angular_velocity",
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+    "flappy_ghost_trail": " You can see the past motion of the bird in the image. Use it to make your decision."
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+  "prompt_source": "sample_factory_checkpoint:gym_base_prompt",
+  "prompt_timing_suffix": " Current action latency is {latency_raw_frames} raw frames ({latency_ms:.2f} ms).  The environment runs at {env_fps:g} FPS and observations are emitted at {obs_fps:g} FPS. Choose the best next action.",
+  "replay_num_envs": 8,
+  "reward_field": "raw_reward",
+  "reward_semantics": "raw_reward",
+  "rows_unit": "decision_step",
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+  "train_episodes": 90,
+  "train_rows": 90000,
+  "val_episodes": 10,
+  "val_fraction": 0.1,
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+      "val",
+      39,
+      [
+        224,
+        224
+      ],
+      "RGB"
+    ]
+  ],
+  "converted_episode_row_counts_verified": true
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/evidence/collect_sf.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+import argparse,hashlib,json,subprocess,sys
+import pyarrow.parquet as pq
+from latency_bench.data.exporters.starvla_lerobot import convert_canonical_rollout_to_lerobot
+ROOT=Path('/home/ubuntu/lzj/mean-profiling');REPO=Path('/home/ubuntu/lzj/latency-sensitive-bench')
+BUDGETS={'flappy':(200,18000,256,256,'gpu'),'deadly_corridor':(8500,3600,16,8,'cpu'),'ant':(100,1000,12,8,'gpu')}
+p=argparse.ArgumentParser();p.add_argument('task',choices=BUDGETS);p.add_argument('--finalize-only',action='store_true');args=p.parse_args()
+task=args.task;out=ROOT/task;cfg=json.loads((out/'teacher.flat.json').read_text());ck=Path(json.loads((out/'teacher.upload.json').read_text())['local_checkpoint']);episodes,cap,probe,replay,device=BUDGETS[task];raw=out/'data/raw'
+command=[sys.executable,str(REPO/'scripts/rollout_data/export_sf_teacher_dataset.py'),'--checkpoint-root',str(ck),'--output-dir',str(raw),'--max-episodes',str(episodes),'--max-steps-per-episode',str(cap),'--num-envs',str(probe),'--replay-num-envs',str(replay),'--device',device,'--val-fraction','0.1','--split-seed','0','--context-window','1','--filter-preset','none','--deterministic','--image-writer-workers','32' if device=='gpu' else '8']
+if task=='ant':command.extend(['--episode-return-gt','2500','--export-image-size','224,224'])
+else:command.extend(['--max-attempts',str(episodes)])
+(out/'collection-command.json').write_text(json.dumps(command,indent=2)+'\n')
+if not args.finalize_only:
+ subprocess.run(command,cwd=REPO,check=True)
+ convert_canonical_rollout_to_lerobot(raw,out/'data/lerobot',action_carrier='native')
+meta=json.loads((raw/'metadata.json').read_text());manifest=json.loads((out/'data/lerobot/manifest.json').read_text());assert meta['total_episodes']==episodes and manifest['state_dim']=={'flappy':24,'deadly_corridor':120,'ant':27}[task]
+train=set(pq.read_table(raw/'train.parquet',columns=['seed']).column('seed').unique().to_pylist());val=set(pq.read_table(raw/'val.parquet',columns=['seed']).column('seed').unique().to_pylist());assert len(train)==meta['train_episodes'] and len(val)==meta['val_episodes'] and train.isdisjoint(val)
+report={'task':task,'teacher_checkpoint':str(ck),'teacher_sha256':hashlib.sha256(ck.read_bytes()).hexdigest(),'episodes':episodes,'train_episodes':meta['train_episodes'],'val_episodes':meta['val_episodes'],'train_rows':meta['train_rows'],'val_rows':meta['val_rows'],'fixed_latency_ms':cfg['fixed_latency_ms'],'action_horizon':1,'unique_seeds':len(train|val),'state_dim':manifest['state_dim']};(out/'data/collection.complete.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report),flush=True)

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/evidence/collection-command.json

@@ -0,0 +1,33 @@
+[
+  "/home/ubuntu/lzj/conda/envs/sf-data/bin/python",
+  "/home/ubuntu/lzj/latency-sensitive-bench/scripts/rollout_data/export_sf_teacher_dataset.py",
+  "--checkpoint-root",
+  "/home/ubuntu/lzj/mean-profiling/ant/teacher-publication/checkpoint.pth",
+  "--output-dir",
+  "/home/ubuntu/lzj/mean-profiling/ant/data/raw",
+  "--max-episodes",
+  "100",
+  "--max-steps-per-episode",
+  "1000",
+  "--num-envs",
+  "12",
+  "--replay-num-envs",
+  "8",
+  "--device",
+  "gpu",
+  "--val-fraction",
+  "0.1",
+  "--split-seed",
+  "0",
+  "--context-window",
+  "1",
+  "--filter-preset",
+  "none",
+  "--deterministic",
+  "--image-writer-workers",
+  "32",
+  "--episode-return-gt",
+  "2500",
+  "--export-image-size",
+  "224,224"
+]

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/evidence/gym-language-contract.patch

@@ -0,0 +1,17 @@
+diff --git a/examples/rl_games/train_files/data_registry/data_config.py b/examples/rl_games/train_files/data_registry/data_config.py
+index 2ef8b39..6d76aeb 100644
+--- a/examples/rl_games/train_files/data_registry/data_config.py
++++ b/examples/rl_games/train_files/data_registry/data_config.py
+@@ -53,10 +53,10 @@ class AtlantisDataConfig(FlappyDataConfig):
+ 
+ 
+ class GymnasiumDataConfig(FlappyDataConfig):
+-    pass
++    language_keys = ["annotation.human.task_description"]
+ 
+ 
+-class GymnasiumNativeDataConfig(FlappyDataConfig):
++class GymnasiumNativeDataConfig(GymnasiumDataConfig):
+     """Configure native Gymnasium state and action vectors."""
+ 
+     state_keys = ["state.native"]

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/evidence/profile.source.json

@@ -0,0 +1,66 @@
+{
+  "burst_model_path": "latency_burst_model.json",
+  "distribution_path": "latency_distribution.json",
+  "env_fps": 10,
+  "frame_ms": 100.0,
+  "gpu_class": "1x-rtx3090",
+  "instance_id": "instance_859cf1e47bca6046",
+  "latency_kind": "observation_to_action_latency",
+  "latency_method": "temporal",
+  "model_id": "qwenoft",
+  "n_admitted_observations": 7976,
+  "n_capacity_drops": 294,
+  "n_observation_attempts": 8270,
+  "per_slot_summary": {
+    "0": {
+      "admitted_count": 7976,
+      "mean_observation_to_action_latency_ms": 90.56460638563993,
+      "mean_worker_service_time_ms": 90.22241150451042,
+      "p95_observation_to_action_latency_ms": 92.13036412000656,
+      "p95_worker_service_time_ms": 91.75596672296524,
+      "p99_worker_service_time_ms": 110.92562991380692
+    }
+  },
+  "provenance": {
+    "base_config": "/workspace/tasks/20260911T023128Z-p-only4/ant/profile.yaml",
+    "checkpoint_kind": "best",
+    "model_artifact": {
+      "checkpoint": "checkpoints/steps_5000_pytorch_model.pt",
+      "model_config": "config.full.yaml",
+      "path_in_repo": "OpenVLA/zero-latency/ant_rgb_state_l0_return_gt3000_100ep_openvla_native_sft_5k",
+      "repo_id": "latency-sensitive-bench/extra-envs-checkpoints",
+      "source": "local"
+    },
+    "session_ids": [
+      0,
+      1,
+      2,
+      3,
+      4
+    ]
+  },
+  "sample_model_type": "hidden_regime",
+  "source_run_id": "20260911T033037730561Z",
+  "summary": {
+    "frame_ms": 100.0,
+    "max_ms": 130.29119229316711,
+    "mean_effective_frames": 0.9056460638563993,
+    "mean_ms": 90.56460638563993,
+    "min_ms": 87.98952293395996,
+    "n_samples": 7976,
+    "p50_frames": 0.8970676946640015,
+    "p50_ms": 89.70676946640015,
+    "p90_frames": 0.9074710392951966,
+    "p90_ms": 90.74710392951965,
+    "p95_frames": 0.9213036412000656,
+    "p95_ms": 92.13036412000656,
+    "p99_frames": 1.1217999053001404,
+    "p99_ms": 112.17999053001404,
+    "prob_latency_gt_1_frame": 0.03686058174523571,
+    "prob_latency_gt_2_frames": 0.0,
+    "prob_latency_gt_3_frames": 0.0,
+    "std_ms": 4.164609685850939
+  },
+  "visualization_path": "latency_profile.png",
+  "workload_id": "ant"
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/evidence/starvla_lerobot.source.py

@@ -0,0 +1,1141 @@
+"""Convert canonical rollout exports to the StarVLA LeRobot image format."""
+
+from __future__ import annotations
+
+import io
+import json
+import shutil
+from pathlib import Path
+from typing import Any, Mapping
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+from PIL import Image
+
+from latency_bench.utils.io import write_jsonl
+
+from latency_bench.data.action_layouts import (
+    ACTION_LAYOUT_GYMNASIUM_CONTINUOUS_V1,
+    ACTION_LAYOUT_GYMNASIUM_DISCRETE_V1,
+    ACTION_LAYOUT_G1_SONIC_ABSOLUTE_JOINT_TARGET_V1,
+    action_spec_for_env,
+)
+from latency_bench.data.filters import density_weights_clean_v1
+from latency_bench.data.state_normalization import min_max_normalize_state
+
+STATE_DIM = 1
+BRIDGE_ACTION_DIM = 7
+LE_ROBOT_CHUNK_SIZE = 1000
+_ROBOT_TYPES_BY_ENV = {
+    "flappy": "rl_games_flappy",
+    "demon_attack": "rl_games_demon_attack",
+    "deadly_corridor": "rl_games_deadly_corridor",
+    "atlantis": "rl_games_atlantis",
+    "asterix": "rl_games_asterix",
+}
+_HISTORY_STATE_DIMS = {"flappy": 24, "demon_attack": 88, "deadly_corridor": 120, "atlantis": 56, "asterix": 136}
+_DEADLY_MULTIBINARY_LABELS = [
+    "MOVE_FORWARD", "MOVE_BACKWARD", "MOVE_LEFT", "MOVE_RIGHT",
+    "TURN_LEFT", "TURN_RIGHT", "ATTACK",
+]
+
+
+def write_starvla_modality(
+    dataset_dir: Path | str,
+    state_dim: int,
+    action_dim: int,
+    *,
+    action_key: str = "action",
+) -> Path:
+    path = Path(dataset_dir) / "meta" / "modality.json"
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(
+            {
+                "state": {
+                    "state": {
+                        "start": 0,
+                        "end": state_dim,
+                        "dtype": "float32",
+                        "absolute": True,
+                        "original_key": "observation.state",
+                    }
+                },
+                "action": {
+                    action_key: {
+                        "start": 0,
+                        "end": action_dim,
+                        "dtype": "float32",
+                        "absolute": True,
+                        "original_key": action_key,
+                    }
+                },
+                "video": {"ego_view": {"original_key": "observation.image"}},
+                "annotation": {
+                    "human.task_description": {"original_key": "task_index"}
+                },
+            },
+            indent=2,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+    return path
+
+
+def _action_carrier_spec(
+    action_labels: list[str],
+    action_carrier: str,
+) -> tuple[list[str], int, int, list[str]]:
+    if action_carrier == "native":
+        return action_labels, len(action_labels), STATE_DIM, ["state"]
+    if action_carrier != "bridge":
+        raise ValueError(f"Unsupported action_carrier={action_carrier!r}")
+    if len(action_labels) > BRIDGE_ACTION_DIM:
+        raise ValueError(
+            f"bridge action carrier supports at most {BRIDGE_ACTION_DIM} actions, "
+            f"got {len(action_labels)}"
+        )
+    carrier_labels = [
+        *action_labels,
+        *(
+            f"BRIDGE_PAD_{index}"
+            for index in range(len(action_labels), BRIDGE_ACTION_DIM)
+        ),
+    ]
+    state_labels = [f"BRIDGE_STATE_{index}" for index in range(BRIDGE_ACTION_DIM)]
+    return carrier_labels, len(action_labels), BRIDGE_ACTION_DIM, state_labels
+
+
+def _robot_type_for_env(env_name: str, integration_name: str) -> str:
+    if integration_name == "gymnasium":
+        return "rl_games_gymnasium"
+    return _ROBOT_TYPES_BY_ENV[env_name]
+
+
+def _write_starvla_handoff_files(
+    output_dir: Path,
+    val_output_dir: Path,
+    *,
+    env_name: str,
+    integration_name: str,
+    rows: list[dict[str, Any]],
+) -> tuple[Path, Path]:
+    prompt_map = {
+        str(int(row["latency_raw_frames"])): {
+            "prompt": row["prompt"],
+            "latency_raw_frames": int(row["latency_raw_frames"]),
+            "latency_ms": float(row["latency_ms"]),
+        }
+        for row in rows
+    }
+    prompt_map_path = output_dir / "latency_prompt_map.json"
+    prompt_map_path.write_text(json.dumps(prompt_map, indent=2), encoding="utf-8")
+
+    mixture_path = output_dir.parent / "_generated_mixtures" / f"{output_dir.name}.json"
+    mixture_path.parent.mkdir(parents=True, exist_ok=True)
+    robot_type = _robot_type_for_env(env_name, integration_name)
+    mixture_path.write_text(
+        json.dumps(
+            {
+                output_dir.name: [[output_dir.name, 1.0, robot_type]],
+                val_output_dir.name: [[val_output_dir.name, 1.0, robot_type]],
+            },
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    return prompt_map_path, mixture_path
+
+
+def _png_bytes(image: Any) -> bytes:
+    """Serialize a rollout image value to PNG bytes.
+
+    Args:
+        image: Hugging Face image struct, PIL image, or array-like image.
+
+    Returns:
+        RGB PNG bytes.
+    """
+    if isinstance(image, Mapping):
+        if image["bytes"] is not None:
+            return bytes(image["bytes"])
+        with Path(image["path"]).open("rb") as handle:
+            return handle.read()
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(np.asarray(image))
+    image = image.convert("RGB")
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    return buffer.getvalue()
+
+
+def _load_rows(raw_export_dir: Path, split: str) -> list[dict[str, Any]]:
+    """Load canonical rollout rows from one parquet split.
+
+    Args:
+        raw_export_dir: Directory containing ``train.parquet`` and ``val.parquet``.
+        split: Split name to read.
+
+    Returns:
+        Rows as Python dictionaries.
+    """
+    path = raw_export_dir / f"{split}.parquet"
+    # Bound nested image buffers per Arrow batch; the converter still owns all rows.
+    return [
+        row
+        for batch in pq.ParquetFile(path).iter_batches(batch_size=1024)
+        for row in batch.to_pylist()
+    ]
+
+
+def _episode_groups(rows: list[dict[str, Any]]) -> list[list[dict[str, Any]]]:
+    """Group canonical rows by episode and order by decision step.
+
+    Args:
+        rows: Canonical rollout rows from one split.
+
+    Returns:
+        Episode row groups sorted by episode index.
+    """
+    grouped: dict[int, list[dict[str, Any]]] = {}
+    for row in rows:
+        grouped.setdefault(int(row["episode_idx"]), []).append(row)
+    return [
+        sorted(episode_rows, key=lambda row: int(row["decision_step"]))
+        for _, episode_rows in sorted(grouped.items())
+    ]
+
+
+def _legacy_row_action_vector(row: dict[str, Any], *, action_dim: int) -> list[float]:
+    if "action" in row and row["action"] is not None:
+        return [float(value) for value in row["action"]]
+    if "action_id" not in row:
+        raise KeyError(f"row is missing action fields; available columns: {sorted(row)}")
+    action_id = int(row["action_id"])
+    if action_id < 0 or action_id >= action_dim:
+        raise ValueError(
+            f"action_id={action_id} is outside action range [0, {action_dim - 1}]"
+        )
+    return [1.0 if index == action_id else 0.0 for index in range(action_dim)]
+
+
+def _write_legacy_episode(
+    path: Path,
+    rows: list[dict[str, Any]],
+    *,
+    action_dim: int,
+) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    table = pa.table(
+        {
+            "observation.image": pa.array(
+                [{"bytes": row["image_bytes"], "path": None} for row in rows],
+                type=pa.struct([("bytes", pa.binary()), ("path", pa.string())]),
+            ),
+            "observation.state": pa.array(
+                [[0.0] * STATE_DIM for _ in rows],
+                type=pa.list_(pa.float32(), STATE_DIM),
+            ),
+            "action": pa.array(
+                [row["action"] for row in rows],
+                type=pa.list_(pa.float32(), action_dim),
+            ),
+            "timestamp": pa.array(
+                [row["timestamp"] for row in rows], type=pa.float64()
+            ),
+            "episode_index": pa.array(
+                [row["episode_index"] for row in rows], type=pa.int64()
+            ),
+            "frame_index": pa.array(
+                [row["frame_index"] for row in rows], type=pa.int64()
+            ),
+            "task_index": pa.array(
+                [row["task_index"] for row in rows], type=pa.int64()
+            ),
+            "done": pa.array([row["done"] for row in rows], type=pa.bool_()),
+            "reward": pa.array([row["reward"] for row in rows], type=pa.float32()),
+            "density_weight": pa.array(
+                [row.get("density_weight", 1.0) for row in rows],
+                type=pa.float32(),
+            ),
+        }
+    )
+    pq.write_table(table, path)
+
+
+def _write_legacy_metadata(
+    dataset_dir: Path,
+    *,
+    fps: float,
+    action_labels: list[str],
+    episode_lengths: list[int],
+    task_prompts: list[str],
+) -> None:
+    meta_dir = dataset_dir / "meta"
+    meta_dir.mkdir(parents=True, exist_ok=True)
+    action_dim = len(action_labels)
+    (meta_dir / "info.json").write_text(
+        json.dumps(
+            {
+                "codebase_version": "v2.0",
+                "fps": fps,
+                "features": {
+                    "observation.image": {"dtype": "image"},
+                    "observation.state": {
+                        "dtype": "float32",
+                        "shape": [STATE_DIM],
+                    },
+                    "action": {
+                        "dtype": "float32",
+                        "shape": [action_dim],
+                        "names": action_labels,
+                    },
+                    "timestamp": {"dtype": "float64", "shape": [1]},
+                    "episode_index": {"dtype": "int64", "shape": [1]},
+                    "frame_index": {"dtype": "int64", "shape": [1]},
+                    "task_index": {"dtype": "int64", "shape": [1]},
+                },
+            },
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    write_jsonl(
+        meta_dir / "episodes.jsonl",
+        [
+            {"episode_index": index, "length": int(length)}
+            for index, length in enumerate(episode_lengths)
+        ],
+    )
+    write_jsonl(
+        meta_dir / "tasks.jsonl",
+        [
+            {"task_index": index, "task": prompt}
+            for index, prompt in enumerate(task_prompts)
+        ],
+    )
+
+
+def _convert_legacy_split(
+    rows: list[dict[str, Any]],
+    output_dir: Path,
+    *,
+    env_name: str,
+    action_labels: list[str],
+    fps: float,
+    reward_field: str,
+    density_weighting: bool,
+) -> dict[str, Any]:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    prompt_to_task_index: dict[str, int] = {}
+    task_prompts: list[str] = []
+    episode_lengths: list[int] = []
+    for new_episode_idx, episode_rows in enumerate(_episode_groups(rows)):
+        density_weights = (
+            density_weights_clean_v1(env_name, episode_rows)
+            if density_weighting
+            else [1.0] * len(episode_rows)
+        )
+        out_rows = []
+        for frame_idx, row in enumerate(episode_rows):
+            row["latency_raw_frames"]
+            prompt = str(row["prompt"])
+            if prompt not in prompt_to_task_index:
+                prompt_to_task_index[prompt] = len(task_prompts)
+                task_prompts.append(prompt)
+            out_rows.append(
+                {
+                    "image_bytes": _png_bytes(row["image"]),
+                    "action": _legacy_row_action_vector(
+                        row,
+                        action_dim=len(action_labels),
+                    ),
+                    "timestamp": frame_idx / fps,
+                    "episode_index": new_episode_idx,
+                    "frame_index": frame_idx,
+                    "task_index": prompt_to_task_index[prompt],
+                    "done": frame_idx == len(episode_rows) - 1,
+                    "reward": float(row[reward_field]),
+                    "density_weight": float(density_weights[frame_idx]),
+                }
+            )
+        episode_lengths.append(len(out_rows))
+        episode_chunk = new_episode_idx // 1000
+        _write_legacy_episode(
+            output_dir
+            / f"data/chunk-{episode_chunk:03d}/episode_{new_episode_idx:06d}.parquet",
+            out_rows,
+            action_dim=len(action_labels),
+        )
+    _write_legacy_metadata(
+        output_dir,
+        fps=fps,
+        action_labels=action_labels,
+        episode_lengths=episode_lengths,
+        task_prompts=task_prompts,
+    )
+    return {
+        "dataset_name": output_dir.name,
+        "env_name": env_name,
+        "episodes": len(episode_lengths),
+        "frames": int(sum(episode_lengths)),
+        "task_prompts": task_prompts,
+    }
+
+
+def _convert_legacy_export(
+    raw_export_dir: Path,
+    output_dir: Path,
+    *,
+    metadata: dict[str, Any],
+    force: bool,
+) -> dict[str, Any]:
+    env_name = str(metadata["env_name"])
+    action_spec = action_spec_for_env(env_name)
+    action_labels = list(metadata["action_labels"])
+    reward_field = str(metadata["reward_field"])
+    rows_unit = metadata["rows_unit"]
+    if rows_unit != "decision_step":
+        raise ValueError(f"rows_unit must be 'decision_step', got {rows_unit!r}")
+    fps = metadata["obs_fps"]
+    obs_stride = metadata["obs_stride_raw_frames"]
+    val_output_dir = output_dir.with_name(f"{output_dir.name}__val")
+    if output_dir.exists() and force:
+        shutil.rmtree(output_dir)
+    if val_output_dir.exists() and force:
+        shutil.rmtree(val_output_dir)
+    train_manifest = _convert_legacy_split(
+        _load_rows(raw_export_dir, "train"),
+        output_dir,
+        env_name=env_name,
+        action_labels=action_labels,
+        fps=fps,
+        reward_field=reward_field,
+        density_weighting=True,
+    )
+    val_manifest = _convert_legacy_split(
+        _load_rows(raw_export_dir, "val"),
+        val_output_dir,
+        env_name=env_name,
+        action_labels=action_labels,
+        fps=fps,
+        reward_field=reward_field,
+        density_weighting=False,
+    )
+    manifest = {
+        **train_manifest,
+        "format": "starvla_lerobot_v2_image_parquet",
+        "source": str(raw_export_dir),
+        "action_layout": str(metadata["action_layout"]),
+        "action_labels": action_labels,
+        "action_dim": len(action_labels),
+        "active_action_dim": len(action_spec.labels),
+        "rows_unit": rows_unit,
+        "obs_fps": fps,
+        "obs_stride_raw_frames": obs_stride,
+        "validation_dataset_name": val_output_dir.name,
+        "validation_episodes": val_manifest["episodes"],
+        "validation_frames": val_manifest["frames"],
+    }
+    (output_dir / "manifest.json").write_text(
+        json.dumps(manifest, indent=2),
+        encoding="utf-8",
+    )
+    return manifest
+
+
+def _convert_history_game_export(
+    raw_export_dir: Path,
+    output_dir: Path,
+    *,
+    metadata: dict[str, Any],
+    force: bool,
+) -> dict[str, Any]:
+    """Convert decision-v1 game rollouts with their real pre-decision state."""
+    env_name = metadata["env_name"]
+    state_dim = _HISTORY_STATE_DIMS[env_name]
+    action_labels = (
+        _DEADLY_MULTIBINARY_LABELS
+        if env_name == "deadly_corridor" else list(metadata["action_labels"])
+    )
+    state_labels = [f"decision_history_{index}" for index in range(state_dim)]
+    train_rows = _load_rows(raw_export_dir, "train")
+    val_rows = _load_rows(raw_export_dir, "val")
+    for row in [*train_rows, *val_rows]:
+        if len(row["state"]) != state_dim:
+            raise ValueError(f"{env_name} decision history has {len(row['state'])} values, expected {state_dim}")
+        if env_name == "deadly_corridor":
+            action_id = row["action_id"]
+            turn, move, strafe, attack = (
+                action_id // 18, (action_id // 6) % 3, (action_id // 2) % 3, action_id % 2
+            )
+            row["action"] = [
+                float(move == 1), float(move == 2), float(strafe == 1),
+                float(strafe == 2), float(turn == 1), float(turn == 2),
+                float(attack == 1),
+            ]
+    first_row = (train_rows or val_rows)[0]
+    with Image.open(io.BytesIO(_png_bytes(first_row["image"]))) as image:
+        image_shape = (image.height, image.width, 3)
+    val_output_dir = output_dir.with_name(f"{output_dir.name}__val")
+    if output_dir.exists() and force:
+        shutil.rmtree(output_dir)
+    if val_output_dir.exists() and force:
+        shutil.rmtree(val_output_dir)
+    common = {
+        "env_name": env_name,
+        "integration_name": env_name,
+        "action_labels": action_labels,
+        "active_action_dim": len(action_labels),
+        "state_dim": state_dim,
+        "state_labels": state_labels,
+        "context_image_count": None,
+        "image_shape": image_shape,
+        "fps": metadata["obs_fps"],
+        "reward_field": metadata["reward_field"],
+        "state_normalization": None,
+        "include_state": True,
+        "density_weighting": False,
+    }
+    train_manifest = _convert_split(train_rows, output_dir, **common)
+    val_manifest = _convert_split(val_rows, val_output_dir, **common)
+    for directory in (output_dir, val_output_dir):
+        modality_path = directory / "meta/modality.json"
+        modality = json.loads(modality_path.read_text())
+        modality["annotation"] = {
+            "human.action.task_description": {"original_key": "task_index"}
+        }
+        modality_path.write_text(json.dumps(modality, indent=2) + "\n")
+    prompt_map_path, mixture_path = _write_starvla_handoff_files(
+        output_dir, val_output_dir, env_name=env_name,
+        integration_name=env_name, rows=[*train_rows, *val_rows],
+    )
+    contract = {
+        "format": "starvla_lerobot_v2_decision_v1",
+        "source": str(raw_export_dir),
+        "robot_type": _ROBOT_TYPES_BY_ENV[env_name],
+        "action_layout": "multibinary_7" if env_name == "deadly_corridor" else metadata["action_layout"],
+        "source_action_layout": metadata["action_layout"],
+        "action_labels": action_labels,
+        "action_dim": len(action_labels),
+        "active_action_dim": len(action_labels),
+        "state_source": "decision_v1",
+        "state_dim": state_dim,
+        "state_labels": state_labels,
+        "state_normalization": {"min": [0.0] * state_dim, "max": [1.0] * state_dim},
+        "action_history_decisions": metadata["action_history_decisions"],
+        "rows_unit": metadata["rows_unit"],
+        "obs_fps": metadata["obs_fps"],
+        "obs_stride_raw_frames": metadata["obs_stride_raw_frames"],
+        "latency_prompt_map_path": str(prompt_map_path),
+        "custom_mixtures_path": str(mixture_path),
+    }
+    if env_name in {"atlantis", "asterix"}:
+        # The exported student image is current RGB; four-frame stacking belongs
+        # to the teacher and remains recorded in the raw collection metadata.
+        contract.update(integration_name="gymnasium",
+                        gymnasium_task={**metadata["gymnasium_task"], "frame_stack": 1})
+    manifest = {**train_manifest, **contract,
+                "validation_dataset_name": val_output_dir.name,
+                "validation_episodes": val_manifest["episodes"],
+                "validation_frames": val_manifest["frames"]}
+    (output_dir / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n")
+    (val_output_dir / "manifest.json").write_text(
+        json.dumps({**val_manifest, **contract, "training_dataset_name": output_dir.name}, indent=2) + "\n"
+    )
+    return manifest
+
+
+def _write_episode(
+    path: Path,
+    rows: list[dict[str, Any]],
+    *,
+    action_dim: int,
+    state_dim: int,
+    include_state: bool,
+) -> None:
+    """Write one LeRobot episode parquet file.
+
+    Args:
+        path: Destination episode parquet path.
+        rows: Converted LeRobot frame rows.
+        action_dim: Expected action vector dimension.
+        state_dim: Expected state vector dimension.
+        include_state: Whether rows carry real state vectors.
+    """
+    path.parent.mkdir(parents=True, exist_ok=True)
+    states = (
+        [row["state"] for row in rows]
+        if include_state
+        else [[0.0] * state_dim for _ in rows]
+    )
+    columns = {
+        "observation.image": pa.array(
+            [{"bytes": row["image_bytes"], "path": None} for row in rows],
+            type=pa.struct([("bytes", pa.binary()), ("path", pa.string())]),
+        ),
+        "observation.state": pa.array(
+            states,
+            type=pa.list_(pa.float32(), state_dim),
+        ),
+        "action": pa.array(
+            [row["action"] for row in rows],
+            type=pa.list_(pa.float32(), action_dim),
+        ),
+        "timestamp": pa.array([row["timestamp"] for row in rows], type=pa.float64()),
+        "episode_index": pa.array(
+            [row["episode_index"] for row in rows], type=pa.int64()
+        ),
+        "frame_index": pa.array([row["frame_index"] for row in rows], type=pa.int64()),
+        "task_index": pa.array([row["task_index"] for row in rows], type=pa.int64()),
+        "latency": pa.array(
+            [row["latency_raw_frames"] for row in rows], type=pa.int64()
+        ),
+        "latency_raw_frames": pa.array(
+            [row["latency_raw_frames"] for row in rows], type=pa.int64()
+        ),
+        "done": pa.array([row["done"] for row in rows], type=pa.bool_()),
+        "reward": pa.array([row["reward"] for row in rows], type=pa.float32()),
+        # Per-row loss weight for KV-memory scheme-B supervision (clean_v1 event
+        # balancing without deleting rows). Uniform 1.0 when density is disabled.
+        "density_weight": pa.array(
+            [row["density_weight"] for row in rows], type=pa.float32()
+        ),
+    }
+    if rows and "issued_command_chunk" in rows[0]:
+        columns["issued_command_chunk"] = pa.array(
+            [row["issued_command_chunk"] for row in rows],
+            type=pa.list_(pa.list_(pa.float32(), action_dim), len(rows[0]["issued_command_chunk"])),
+        )
+    for mask_key in ("action_prefix_mask", "action_loss_mask"):
+        if rows and mask_key in rows[0]:
+            columns[mask_key] = pa.array(
+                [row[mask_key] for row in rows],
+                type=pa.list_(pa.bool_(), len(rows[0][mask_key])),
+            )
+    if rows and "action_id" in rows[0]:
+        columns["action_id"] = pa.array(
+            [row["action_id"] for row in rows], type=pa.int64()
+        )
+    if rows and "context_image_bytes" in rows[0]:
+        image_type = pa.struct([("bytes", pa.binary()), ("path", pa.string())])
+        columns["observation.context_images"] = pa.array(
+            [
+                [{"bytes": value, "path": None} for value in row["context_image_bytes"]]
+                for row in rows
+            ],
+            type=pa.list_(image_type),
+        )
+    table = pa.table(columns)
+    pq.write_table(table, path)
+
+
+def _row_action_vector(
+    row: dict[str, Any],
+    *,
+    active_action_dim: int,
+    action_dim: int,
+) -> list[float]:
+    values = [float(value) for value in row["action"]]
+    return [*values, *([0.0] * (action_dim - active_action_dim))]
+
+
+def _write_metadata(
+    dataset_dir: Path,
+    *,
+    fps: float,
+    action_labels: list[str],
+    episode_lengths: list[int],
+    task_prompts: list[str],
+    image_shape: tuple[int, int, int],
+    state_dim: int,
+    state_labels: list[str],
+    context_image_count: int | None,
+) -> None:
+    """Write minimal LeRobot metadata files for a split dataset.
+
+    Args:
+        dataset_dir: LeRobot dataset split directory.
+        fps: Frame rate stored in metadata.
+        action_labels: Canonical action labels in vector order.
+        episode_lengths: Number of frames per converted episode.
+        task_prompts: Unique prompts used as LeRobot tasks.
+    """
+    meta_dir = dataset_dir / "meta"
+    meta_dir.mkdir(parents=True, exist_ok=True)
+    action_dim = len(action_labels)
+    height, width, channels = image_shape
+    features = {
+        "observation.image": {
+            "dtype": "image",
+            "shape": [height, width, channels],
+            "names": ["height", "width", "channel"],
+            "video_info": {"video.fps": fps},
+        },
+        "observation.state": {
+            "dtype": "float32",
+            "shape": [state_dim],
+            "names": state_labels,
+        },
+        "action": {"dtype": "float32", "shape": [action_dim], "names": action_labels},
+        "timestamp": {"dtype": "float64", "shape": [1]},
+        "episode_index": {"dtype": "int64", "shape": [1]},
+        "frame_index": {"dtype": "int64", "shape": [1]},
+        "task_index": {"dtype": "int64", "shape": [1]},
+        "latency": {"dtype": "int64", "shape": [1]},
+    }
+    if context_image_count is not None:
+        features["observation.context_images"] = {
+            "dtype": "image_sequence",
+            "shape": [context_image_count, height, width, channels],
+            "names": ["time", "height", "width", "channel"],
+            "video_info": {"video.fps": fps},
+        }
+    (meta_dir / "info.json").write_text(
+        json.dumps(
+            {
+                "codebase_version": "v2.0",
+                "fps": fps,
+                "total_episodes": len(episode_lengths),
+                "total_frames": int(sum(episode_lengths)),
+                "total_tasks": len(task_prompts),
+                "total_videos": 0,
+                "total_chunks": (len(episode_lengths) + LE_ROBOT_CHUNK_SIZE - 1)
+                // LE_ROBOT_CHUNK_SIZE,
+                "chunks_size": LE_ROBOT_CHUNK_SIZE,
+                "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"
+                ),
+                "features": features,
+            },
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    (meta_dir / "modality.json").write_text(
+        json.dumps(
+            {
+                "state": {
+                    "game_state": {
+                        "start": 0,
+                        "end": state_dim,
+                        "dtype": "float32",
+                        "absolute": True,
+                        "original_key": "observation.state",
+                    }
+                },
+                "action": {
+                    "button": {
+                        "start": 0,
+                        "end": action_dim,
+                        "dtype": "float32",
+                        "absolute": True,
+                        "original_key": "action",
+                    }
+                },
+                "video": {"image": {"original_key": "observation.image"}},
+                "annotation": {
+                    "human.task_description": {"original_key": "task_index"}
+                },
+            },
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    write_jsonl(
+        meta_dir / "episodes.jsonl",
+        [
+            {"episode_index": idx, "length": int(length)}
+            for idx, length in enumerate(episode_lengths)
+        ],
+    )
+    write_jsonl(
+        meta_dir / "tasks.jsonl",
+        [
+            {"task_index": idx, "task": prompt}
+            for idx, prompt in enumerate(task_prompts)
+        ],
+    )
+
+
+def _convert_split(
+    rows: list[dict[str, Any]],
+    output_dir: Path,
+    *,
+    env_name: str,
+    integration_name: str,
+    action_labels: list[str],
+    active_action_dim: int,
+    state_dim: int,
+    state_labels: list[str],
+    context_image_count: int | None,
+    image_shape: tuple[int, int, int],
+    fps: float,
+    reward_field: str,
+    state_normalization: dict[str, Any] | None,
+    include_state: bool,
+    density_weighting: bool = False,
+) -> dict[str, Any]:
+    """Convert one canonical rollout split into a LeRobot dataset directory.
+
+    Args:
+        rows: Canonical rollout rows from one split.
+        output_dir: Destination LeRobot split directory.
+        env_name: Canonical benchmark environment name.
+        action_labels: Canonical action labels in vector order.
+        fps: Frame rate used for timestamps.
+        reward_field: Source canonical reward column.
+        density_weighting: When True, write per-row ``density_weight`` from clean_v1's
+            event balancing (train split). When False, weights are uniform 1.0 (val
+            split keeps an unweighted held-out loss).
+
+    Returns:
+        Split manifest summary.
+    """
+    output_dir.mkdir(parents=True, exist_ok=True)
+    prompt_to_task_index: dict[str, int] = {}
+    task_prompts: list[str] = []
+    episode_lengths: list[int] = []
+    for new_episode_idx, episode_rows in enumerate(_episode_groups(rows)):
+        density_weights = (
+            density_weights_clean_v1(env_name, episode_rows)
+            if density_weighting
+            and integration_name != "gymnasium"
+            else [1.0] * len(episode_rows)
+        )
+        out_rows = []
+        for frame_idx, row in enumerate(episode_rows):
+            # Enforce the canonical rollout contract; downstream must not read the legacy ``latency`` alias.
+            row["latency_raw_frames"]
+            prompt = str(row["prompt"])
+            if prompt not in prompt_to_task_index:
+                prompt_to_task_index[prompt] = len(task_prompts)
+                task_prompts.append(prompt)
+            out_row = {
+                "image_bytes": _png_bytes(row["image"]),
+                "action": _row_action_vector(
+                    row,
+                    active_action_dim=active_action_dim,
+                    action_dim=len(action_labels),
+                ),
+                "timestamp": frame_idx / fps,
+                "episode_index": new_episode_idx,
+                "frame_index": frame_idx,
+                "task_index": prompt_to_task_index[prompt],
+                "latency_raw_frames": int(row["latency_raw_frames"]),
+                # ``done`` is generated only for the LeRobot target format, not for canonical rollout rows.
+                "done": frame_idx == len(episode_rows) - 1,
+                "reward": float(row[reward_field]),
+                "density_weight": float(density_weights[frame_idx]),
+            }
+            if include_state:
+                state = np.asarray(row["state"], dtype=np.float32)
+                if state_normalization is not None:
+                    state_min = np.asarray(state_normalization["min"], dtype=np.float32)
+                    state_max = np.asarray(state_normalization["max"], dtype=np.float32)
+                    state = min_max_normalize_state(state, state_min, state_max)
+                out_row["state"] = state.tolist()
+            if "issued_command_chunk" in row:
+                out_row["issued_command_chunk"] = row["issued_command_chunk"]
+            for mask_key in ("action_prefix_mask", "action_loss_mask"):
+                if mask_key in row:
+                    out_row[mask_key] = row[mask_key]
+            if "action_id" in row:
+                out_row["action_id"] = int(row["action_id"])
+            if "context_images" in row and row["context_images"] is not None:
+                out_row["context_image_bytes"] = [
+                    _png_bytes(image) for image in row["context_images"]
+                ]
+            out_rows.append(out_row)
+        episode_lengths.append(len(out_rows))
+        episode_chunk = new_episode_idx // LE_ROBOT_CHUNK_SIZE
+        _write_episode(
+            output_dir
+            / f"data/chunk-{episode_chunk:03d}/episode_{new_episode_idx:06d}.parquet",
+            out_rows,
+            action_dim=len(action_labels),
+            state_dim=state_dim,
+            include_state=include_state,
+        )
+    _write_metadata(
+        output_dir,
+        fps=fps,
+        action_labels=action_labels,
+        episode_lengths=episode_lengths,
+        task_prompts=task_prompts,
+        image_shape=image_shape,
+        state_dim=state_dim,
+        state_labels=state_labels,
+        context_image_count=context_image_count,
+    )
+    if rows and "issued_command_chunk" in rows[0]:
+        info_path = output_dir / "meta/info.json"
+        info = json.loads(info_path.read_text())
+        info["features"]["issued_command_chunk"] = {
+            "dtype": "float32", "shape": [len(rows[0]["issued_command_chunk"]), len(action_labels)],
+            "names": action_labels,
+        }
+        for mask_key in ("action_prefix_mask", "action_loss_mask"):
+            if mask_key in rows[0]:
+                info["features"][mask_key] = {
+                    "dtype": "bool", "shape": [len(rows[0][mask_key])], "names": None,
+                }
+        info_path.write_text(json.dumps(info, indent=2) + "\n")
+    return {
+        "dataset_name": output_dir.name,
+        "env_name": env_name,
+        "episodes": len(episode_lengths),
+        "frames": int(sum(episode_lengths)),
+        "task_prompts": task_prompts,
+    }
+
+
+def convert_canonical_rollout_to_lerobot(
+    raw_export_dir: Path | str,
+    output_dir: Path | str,
+    *,
+    force: bool = False,
+    action_carrier: str = "native",
+    normalize_state: bool = True,
+    write_starvla_handoff: bool = True,
+) -> dict[str, Any]:
+    """Convert a canonical rollout export into train/val LeRobot datasets.
+
+    Args:
+        raw_export_dir: Directory with canonical rollout parquet files and metadata.
+        output_dir: Destination LeRobot train dataset directory.
+        force: Remove existing train/val output directories before writing.
+        normalize_state: Apply the existing min-max state transform when the
+            canonical export includes a state vector. When disabled, an
+            explicit ``state`` field in canonical rows is copied as-is.
+        write_starvla_handoff: Write the existing StarVLA prompt/mixture handoff
+            files and manifest fields.
+
+    Returns:
+        Manifest describing the converted train dataset and validation companion.
+    """
+    raw_export_dir = Path(raw_export_dir)
+    output_dir = Path(output_dir)
+    metadata = json.loads(
+        (raw_export_dir / "metadata.json").read_text(encoding="utf-8")
+    )
+    env_name = str(metadata["env_name"])
+    integration_name = (
+        metadata["integration_name"] if "integration_name" in metadata else env_name
+    )
+    if metadata.get("action_history_decisions") == 8 and env_name in _HISTORY_STATE_DIMS:
+        return _convert_history_game_export(
+            raw_export_dir, output_dir, metadata=metadata, force=force
+        )
+    if integration_name != "gymnasium":
+        return _convert_legacy_export(
+            raw_export_dir,
+            output_dir,
+            metadata=metadata,
+            force=force,
+        )
+    action_layout = metadata["action_layout"]
+    active_action_labels = list(metadata["action_labels"])
+    if action_layout not in (
+        ACTION_LAYOUT_GYMNASIUM_DISCRETE_V1,
+        ACTION_LAYOUT_GYMNASIUM_CONTINUOUS_V1,
+        ACTION_LAYOUT_G1_SONIC_ABSOLUTE_JOINT_TARGET_V1,
+    ):
+        raise ValueError(f"Unsupported Gymnasium action_layout={action_layout!r}")
+    gymnasium_task = metadata["gymnasium_task"]
+    if env_name != gymnasium_task["task_name"]:
+        raise ValueError(
+            "Canonical Gymnasium env_name does not match gymnasium_task.task_name"
+        )
+    continuous = action_layout in (
+        ACTION_LAYOUT_GYMNASIUM_CONTINUOUS_V1,
+        ACTION_LAYOUT_G1_SONIC_ABSOLUTE_JOINT_TARGET_V1,
+    )
+    if continuous and action_carrier == "native":
+        gymnasium_task = {
+            **gymnasium_task,
+            "action_space": {
+                key: value
+                for key, value in gymnasium_task["action_space"].items()
+                if key not in {"openvla_carrier_dim", "openvla_padding"}
+            },
+        }
+    task_action_labels = (
+        gymnasium_task["action_space"]["labels"]
+        if continuous
+        else gymnasium_task["action_labels"]
+    )
+    if active_action_labels != task_action_labels:
+        raise ValueError(
+            "Canonical Gymnasium action_labels do not match gymnasium_task action-space labels"
+        )
+    action_labels, active_action_dim, state_dim, state_labels = _action_carrier_spec(
+        active_action_labels,
+        action_carrier,
+    )
+    train_rows = _load_rows(raw_export_dir, "train")
+    val_rows = _load_rows(raw_export_dir, "val")
+    raw_train_rows = train_rows
+    raw_val_rows = val_rows
+    admission_filtered = (
+        action_layout == ACTION_LAYOUT_G1_SONIC_ABSOLUTE_JOINT_TARGET_V1
+        and any("command_admitted" in row for row in [*train_rows, *val_rows])
+    )
+    if admission_filtered:
+        train_rows = [row for row in train_rows if row["command_admitted"]]
+        val_rows = [row for row in val_rows if row["command_admitted"]]
+    uses_state = "state_space" in gymnasium_task
+    if not normalize_state:
+        uses_state = any("state" in row for row in [*train_rows, *val_rows])
+    if uses_state:
+        if "state_space" in gymnasium_task:
+            state_labels = list(gymnasium_task["state_space"]["labels"])
+            state_dim = len(state_labels)
+        else:
+            state_row = next(row for row in [*train_rows, *val_rows] if "state" in row)
+            state_dim = len(state_row["state"])
+            state_labels = [f"state_{index}" for index in range(state_dim)]
+    reward_field = str(metadata["reward_field"])
+    rows_unit = metadata["rows_unit"]
+    if rows_unit != "decision_step":
+        raise ValueError(f"rows_unit must be 'decision_step', got {rows_unit!r}")
+    fps = metadata["obs_fps"]
+    obs_stride = metadata["obs_stride_raw_frames"]
+    val_output_dir = output_dir.with_name(f"{output_dir.name}__val")
+    if output_dir.exists() and force:
+        shutil.rmtree(output_dir)
+    if val_output_dir.exists() and force:
+        shutil.rmtree(val_output_dir)
+    state_normalization = None
+    if uses_state and normalize_state:
+        train_states = np.asarray([row["state"] for row in train_rows], dtype=np.float32)
+        state_normalization = {
+            "type": "min_max",
+            "min": train_states.min(axis=0).tolist(),
+            "max": train_states.max(axis=0).tolist(),
+        }
+    first_row = (raw_train_rows or raw_val_rows)[0]
+    image_source = first_row["image"]
+    with Image.open(io.BytesIO(_png_bytes(image_source))) as image:
+        width, height = image.size
+        image_shape = (height, width, 3)
+    context_image_count = (
+        len(first_row["context_images"])
+        if "context_images" in first_row and first_row["context_images"] is not None
+        else None
+    )
+    train_manifest = _convert_split(
+        train_rows,
+        output_dir,
+        env_name=env_name,
+        integration_name=integration_name,
+        action_labels=action_labels,
+        active_action_dim=active_action_dim,
+        state_dim=state_dim,
+        state_labels=state_labels,
+        context_image_count=context_image_count,
+        image_shape=image_shape,
+        fps=fps,
+        reward_field=reward_field,
+        state_normalization=state_normalization,
+        include_state=uses_state,
+        density_weighting=True,
+    )
+    val_manifest = _convert_split(
+        val_rows,
+        val_output_dir,
+        env_name=env_name,
+        integration_name=integration_name,
+        action_labels=action_labels,
+        active_action_dim=active_action_dim,
+        state_dim=state_dim,
+        state_labels=state_labels,
+        context_image_count=context_image_count,
+        image_shape=image_shape,
+        fps=fps,
+        reward_field=reward_field,
+        state_normalization=state_normalization,
+        include_state=uses_state,
+        density_weighting=False,
+    )
+    if "label_provenance" in metadata:
+        train_info = json.loads((output_dir / "meta/info.json").read_text())
+        for directory in (output_dir, val_output_dir):
+            info_path = directory / "meta/info.json"
+            info = json.loads(info_path.read_text())
+            info["label_provenance"] = metadata["label_provenance"]
+            for key in ("issued_command_chunk", "action_prefix_mask", "action_loss_mask"):
+                if key in train_info["features"]:
+                    info["features"][key] = train_info["features"][key]
+            info_path.write_text(json.dumps(info, indent=2) + "\n")
+    handoff_contract = {
+        "format": (
+            "lerobot_v2_image_parquet"
+            if continuous
+            else "starvla_lerobot_v2_image_parquet"
+        ),
+        "source": str(raw_export_dir),
+        "integration_name": integration_name,
+        "task_name": env_name,
+        "action_layout": str(metadata["action_layout"]),
+        "action_labels": active_action_labels,
+        "carrier_action_labels": action_labels,
+        "action_dim": len(action_labels),
+        "active_action_dim": active_action_dim,
+        "action_carrier": action_carrier,
+        "rows_unit": rows_unit,
+        "obs_fps": fps,
+        "obs_stride_raw_frames": obs_stride,
+        "uses_state": uses_state,
+        "state_dim": state_dim,
+        "state_labels": state_labels,
+        "state_normalization": state_normalization,
+    }
+    if admission_filtered:
+        handoff_contract.update(
+            {
+                "admission_filter": "command_admitted",
+                "raw_train_frames": len(raw_train_rows),
+                "raw_validation_frames": len(raw_val_rows),
+            }
+        )
+    if write_starvla_handoff:
+        handoff_contract["robot_type"] = _robot_type_for_env(env_name, integration_name)
+        prompt_map_path, mixture_path = _write_starvla_handoff_files(
+            output_dir,
+            val_output_dir,
+            env_name=env_name,
+            integration_name=integration_name,
+            rows=[*train_rows, *val_rows],
+        )
+        handoff_contract.update(
+            {
+                "latency_prompt_map_path": str(prompt_map_path),
+                "custom_mixtures_path": str(mixture_path),
+            }
+        )
+    if gymnasium_task is not None:
+        handoff_contract["gymnasium_task"] = gymnasium_task
+    manifest = {
+        **train_manifest,
+        **handoff_contract,
+        "validation_dataset_name": val_output_dir.name,
+        "validation_episodes": val_manifest["episodes"],
+        "validation_frames": val_manifest["frames"],
+    }
+    validation_manifest = {
+        **val_manifest,
+        **handoff_contract,
+        "training_dataset_name": output_dir.name,
+    }
+    (output_dir / "manifest.json").write_text(
+        json.dumps(manifest, indent=2), encoding="utf-8"
+    )
+    (val_output_dir / "manifest.json").write_text(
+        json.dumps(validation_manifest, indent=2), encoding="utf-8"
+    )
+    return manifest

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/files.sha256.json

@@ -0,0 +1,106 @@
+{
+  "README.md": {
+    "bytes": 905,
+    "sha256": "5236cf2a83cce7e46a332b354809b867549ca1d3a5e743b57c877eefe8a56f7c"
+  },
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+    "sha256": "b0cc5a2a0681575c7fd23150d5b3211f65236832b4f9c715bd0b84404d35ccdf"
+  },
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+    "bytes": 230,
+    "sha256": "4570ac62854befced86409de1c911b857d6a852202a3679fdfe94555ad84b3e7"
+  },
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+    "sha256": "4478485033db57f5cfc8dd58edfb3ec3a240a22e9821b641ae0c822d7e1035a8"
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+    "sha256": "e5aa108960398ae84ff32bbe8a60d49441e8d01ee070de948dd38d04daf6edd1"
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+  },
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+    "sha256": "07deeb7e3cf86c00ecdf0cf7a4cf4e52279e1b2177bd5eb642c1b415d094582b"
+  },
+  "evidence/collect_sf.py": {
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+    "sha256": "883cdfc33382e3cdbb628124a4eb7c736ecaa6bdb3757d9fca00a21f4586d037"
+  },
+  "evidence/collection-command.json": {
+    "bytes": 707,
+    "sha256": "378ec971f61b95b06844ee793bafef58003e4dfdaef35c45e0b8afda16a5c302"
+  },
+  "evidence/gym-language-contract.patch": {
+    "bytes": 688,
+    "sha256": "9f5dee2c5b52d5507aff27373a8cf6def4959361d2c5264d5079b416f28a4c49"
+  },
+  "evidence/profile.source.json": {
+    "bytes": 2148,
+    "sha256": "0e49f72ec05ac600a54e07bbca5af3143708444d606167f33fa21788a9fefe50"
+  },
+  "evidence/starvla_lerobot.source.py": {
+    "bytes": 42762,
+    "sha256": "c3aae215af7c7f04051d7a2731a2007727f7ac3426fbc8d2aad2f6a276ae842b"
+  },
+  "provenance.json": {
+    "bytes": 4168,
+    "sha256": "af7156185beaa8ff3e153c55c6c7b8aa426590cc309d60a82d8b55597b6ae8ee"
+  }
+}

latency-aware/ant/mean_rtx3090_s2222_100ep_v1/provenance.json

@@ -0,0 +1,152 @@
+{
+  "task": "ant",
+  "downstream_model": "QwenOFT",
+  "path_family_alias": "OpenVLA",
+  "teacher": {
+    "repo_id": "latency-sensitive-bench/benchmark-models",
+    "revision": "1c2f38f16553622eef2c62ddde86036be989ea31",
+    "path": "latency-aware/ant/small-policy/sample-factory-qwenoft-rtx3090-mean-v1",
+    "local_checkpoint": "/home/ubuntu/lzj/mean-profiling/ant/teacher-publication/checkpoint.pth",
+    "status": "complete_verified",
+    "completed_env_steps": 10006528,
+    "selected_env_steps": 9969664,
+    "selected_source_checkpoint": "/home/ubuntu/lzj/mean-profiling/ant/teacher/ant_mean_3090_s2222/checkpoint_p0/best_000019472_9969664_reward_3371.900.pth",
+    "tensor_count": 15,
+    "weights_only_export": true,
+    "tensor_equality_verified": true,
+    "hash_readback_verified": true,
+    "url": "https://huggingface.co/latency-sensitive-bench/benchmark-models/tree/1c2f38f16553622eef2c62ddde86036be989ea31/latency-aware/ant/small-policy/sample-factory-qwenoft-rtx3090-mean-v1",
+    "completed_utc": "2026-09-30T13:54:03.497277+00:00"
+  },
+  "data": {
+    "task": "ant",
+    "teacher_checkpoint": "/home/ubuntu/lzj/mean-profiling/ant/teacher-publication/checkpoint.pth",
+    "teacher_sha256": "a7899b32beb9b2cfec1bcff85b285dddc243a73f1c322fafefae599df0f67b3b",
+    "episodes": 100,
+    "train_episodes": 90,
+    "val_episodes": 10,
+    "train_rows": 90000,
+    "val_rows": 10000,
+    "fixed_latency_ms": 90.56460638563993,
+    "action_horizon": 1,
+    "unique_seeds": 100,
+    "state_dim": 27
+  },
+  "runtime": {
+    "repo": "c3c6a39365a151e9b7a5e215452fd64e957c2b29",
+    "starvla": "ccca13c5177fe3d3c884b6e2de4965d916016649",
+    "runtime_fixes": [
+      "mean-profile-preparation.patch",
+      "gym-language-contract.patch",
+      "loader-spawn-cache.patch",
+      "loader-spawn-test.patch"
+    ],
+    "pytorch3d": {
+      "revision": "33824be3cbc87a7dd1db0f6a9a9de9ac81b2d0ba",
+      "build": "transforms-only, no native render extension; QwenOFT uses transforms only"
+    },
+    "decord": {
+      "version": "0.6.0",
+      "build": "official source CPU decoder CP310",
+      "wheel_sha256": "e193b356b1e984b4eff08d23b62e482c2c9e5037a6efdc0b1af47079ae2e4c47"
+    }
+  },
+  "profile": {
+    "mean_ms": 90.56460638563993,
+    "profile": "/home/ubuntu/lzj/mean-profiling/reference/checkpoint-configs/latency-aware/ant/small-policy/sample-factory-qwenoft-rtx3090-v1/profile/profile.json",
+    "sha256": "0e49f72ec05ac600a54e07bbca5af3143708444d606167f33fa21788a9fefe50"
+  },
+  "audit": {
+    "task": "ant",
+    "episodes": 100,
+    "rows": 100000,
+    "split": [
+      90,
+      10
+    ],
+    "state_dim": 27,
+    "action_dim": 8,
+    "all_numeric_finite": true,
+    "each_episode_1000_contiguous_steps": true,
+    "unique_seeds": 100,
+    "split_seed_overlap": 0,
+    "strict_return_gt2500": true,
+    "episode_return_min_max_mean": {
+      "train": [
+        2996.093242406845,
+        4104.504939198494,
+        3921.6442338414427
+      ],
+      "val": [
+        3773.778401404619,
+        4109.358084142208,
+        3976.1818374603986
+      ]
+    },
+    "mean_nominal_ms": 90.56460638563993,
+    "effective_latency_frames": 1,
+    "effective_latency_ms": 100.0,
+    "sampled_image_checks": [
+      [
+        "train",
+        0,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ],
+      [
+        "train",
+        176,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ],
+      [
+        "train",
+        351,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ],
+      [
+        "val",
+        0,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ],
+      [
+        "val",
+        20,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ],
+      [
+        "val",
+        39,
+        [
+          224,
+          224
+        ],
+        "RGB"
+      ]
+    ],
+    "converted_episode_row_counts_verified": true
+  },
+  "condition": "fixed_mean_from_RTX3090_profile",
+  "raw_format": "canonical embeddedPNG parquet schema2",
+  "training_data_format": "converted LeRobot v2",
+  "vla_training_status": "not started",
+  "closed_loop_evaluation_status": "pending"
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/README.md

@@ -0,0 +1,5 @@
+# Deadly Corridor QwenOFT mean-profile demonstrations
+
+8500 unique-seed episodes,7650training/850validation,313925/34465rows. State120 binary decision-history H8, seven native binary buttons, original160x120RGB image,H1. Fixed mean73.69250777493353ms from RTX3090 profile, quantized3raw35Hzframes (85.7142857ms),8.75Hz observations. Original budget/cap3600 and16probe/8replay preserved. Native Doom environments recreated perseed; replay consistency verified after seed-reuse repair.
+
+Raw-only embeddedPNG Parquet distribution with original conversion recipe/expected metadata/audit/source code. Training-host LeRobot conversion complete. Teacher trained25006080frames under original25Mbudget, selectedbest22239232. This publication does not establish completed VLA training or closed-loop benchmark acceptance.

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/README.md

@@ -0,0 +1 @@
+Use latency-sensitive-bench@c3c6a39365a151e9b7a5e215452fd64e957c2b29 plus published preparation patches. Run convert_to_lerobot.py demonstrations/raw DEST/lerobot. The converter also creates DEST/lerobot__val and DEST/_generated_mixtures. Expected metadata and audit are included; raw image/state/action values are retained. LeRobot data remain on training host, following this repository raw-only distribution.

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/convert_to_lerobot.py

@@ -0,0 +1,4 @@
+import sys
+from pathlib import Path
+from latency_bench.data.exporters.starvla_lerobot import convert_canonical_rollout_to_lerobot
+convert_canonical_rollout_to_lerobot(Path(sys.argv[1]), Path(sys.argv[2]), action_carrier="native")

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/expected/_generated_mixtures/lerobot.json

@@ -0,0 +1,16 @@
+{
+  "lerobot": [
+    [
+      "lerobot",
+      1.0,
+      "rl_games_deadly_corridor"
+    ]
+  ],
+  "lerobot__val": [
+    [
+      "lerobot__val",
+      1.0,
+      "rl_games_deadly_corridor"
+    ]
+  ]
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/expected/lerobot/manifest.json

@@ -0,0 +1,404 @@
+{
+  "dataset_name": "lerobot",
+  "env_name": "deadly_corridor",
+  "episodes": 7650,
+  "frames": 313925,
+  "task_prompts": [
+    "You are playing Deadly Corridor in VizDoom. Your goal is to survive the corridor, fight enemies, and reach the green armor vest at the far end. The available actions are: MOVE_FORWARD, MOVE_BACKWARD, MOVE_LEFT, MOVE_RIGHT, TURN_LEFT, TURN_RIGHT, and ATTACK. Choose the best next action from the current visual observation."
+  ],
+  "format": "starvla_lerobot_v2_decision_v1",
+  "source": "/home/ubuntu/lzj/mean-profiling/deadly_corridor/data/raw",
+  "robot_type": "rl_games_deadly_corridor",
+  "action_layout": "multibinary_7",
+  "source_action_layout": "deadly_corridor_joint_54",
+  "action_labels": [
+    "MOVE_FORWARD",
+    "MOVE_BACKWARD",
+    "MOVE_LEFT",
+    "MOVE_RIGHT",
+    "TURN_LEFT",
+    "TURN_RIGHT",
+    "ATTACK"
+  ],
+  "action_dim": 7,
+  "active_action_dim": 7,
+  "state_source": "decision_v1",
+  "state_dim": 120,
+  "state_labels": [
+    "decision_history_0",
+    "decision_history_1",
+    "decision_history_2",
+    "decision_history_3",
+    "decision_history_4",
+    "decision_history_5",
+    "decision_history_6",
+    "decision_history_7",
+    "decision_history_8",
+    "decision_history_9",
+    "decision_history_10",
+    "decision_history_11",
+    "decision_history_12",
+    "decision_history_13",
+    "decision_history_14",
+    "decision_history_15",
+    "decision_history_16",
+    "decision_history_17",
+    "decision_history_18",
+    "decision_history_19",
+    "decision_history_20",
+    "decision_history_21",
+    "decision_history_22",
+    "decision_history_23",
+    "decision_history_24",
+    "decision_history_25",
+    "decision_history_26",
+    "decision_history_27",
+    "decision_history_28",
+    "decision_history_29",
+    "decision_history_30",
+    "decision_history_31",
+    "decision_history_32",
+    "decision_history_33",
+    "decision_history_34",
+    "decision_history_35",
+    "decision_history_36",
+    "decision_history_37",
+    "decision_history_38",
+    "decision_history_39",
+    "decision_history_40",
+    "decision_history_41",
+    "decision_history_42",
+    "decision_history_43",
+    "decision_history_44",
+    "decision_history_45",
+    "decision_history_46",
+    "decision_history_47",
+    "decision_history_48",
+    "decision_history_49",
+    "decision_history_50",
+    "decision_history_51",
+    "decision_history_52",
+    "decision_history_53",
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+        "decision_history_116",
+        "decision_history_117",
+        "decision_history_118",
+        "decision_history_119"
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+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/expected/lerobot__val/meta/modality.json

@@ -0,0 +1,30 @@
+{
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+      "end": 120,
+      "dtype": "float32",
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+    }
+  }
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/conversion/expected/lerobot__val/meta/tasks.jsonl

@@ -0,0 +1 @@
+{"task": "You are playing Deadly Corridor in VizDoom. Your goal is to survive the corridor, fight enemies, and reach the green armor vest at the far end. The available actions are: MOVE_FORWARD, MOVE_BACKWARD, MOVE_LEFT, MOVE_RIGHT, TURN_LEFT, TURN_RIGHT, and ATTACK. Choose the best next action from the current visual observation.", "task_index": 0}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/raw/filter_report.json

@@ -0,0 +1,12 @@
+{
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+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/raw/metadata.json

@@ -0,0 +1,395 @@
+{
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+  "latency_source": "checkpoint",
+  "max_episodes": 8500,
+  "max_steps_per_episode": 3600,
+  "max_steps_unit": "raw_frame",
+  "num_envs": 16,
+  "obs_fps": 8.75,
+  "obs_stride_raw_frames": 4,
+  "prompt_source": "sample_factory_checkpoint:base_prompt",
+  "replay_num_envs": 8,
+  "reward_field": "raw_reward",
+  "reward_semantics": "raw_reward",
+  "rows_unit": "decision_step",
+  "schema_version": 2,
+  "split_seed": 0,
+  "total_episodes": 8500,
+  "total_rows": 348390,
+  "train_episodes": 7650,
+  "train_rows": 313925,
+  "val_episodes": 850,
+  "val_fraction": 0.1,
+  "val_rows": 34465
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/raw/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e3473cf7eeece0b3e107f516869b136159fd197ff4a8667a47e128cf336b9e2
+size 4950850571

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/raw/val.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68456699331b2362acf90f859b9d5b379e844a6d38d6047c9f67d2cceb79b269
+size 543289255

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/validation/collection.complete.json

@@ -0,0 +1,14 @@
+{
+  "task": "deadly_corridor",
+  "teacher_checkpoint": "/home/ubuntu/lzj/mean-profiling/deadly_corridor/teacher-publication/checkpoint.pth",
+  "teacher_sha256": "e631e9017634f799860c581761b5fa48601109393c11d20a35187c6f75d14b88",
+  "episodes": 8500,
+  "train_episodes": 7650,
+  "val_episodes": 850,
+  "train_rows": 313925,
+  "val_rows": 34465,
+  "fixed_latency_ms": 73.69250777493353,
+  "action_horizon": 1,
+  "unique_seeds": 8500,
+  "state_dim": 120
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/demonstrations/validation/dataset.audit.json

@@ -0,0 +1,36 @@
+{
+  "task": "deadly_corridor",
+  "episodes": 8500,
+  "train_episodes": 7650,
+  "val_episodes": 850,
+  "unique_seeds": 8500,
+  "seed_range": [
+    42,
+    8541
+  ],
+  "split_intersection": 0,
+  "train_rows": 313925,
+  "val_rows": 34465,
+  "rows": 348390,
+  "state_dim": 120,
+  "history_decisions": 8,
+  "state_binary": true,
+  "action_dim": 7,
+  "source_joint_actions": 54,
+  "converted_native_binary_actions": true,
+  "finite_numeric": true,
+  "image_shape": [
+    120,
+    160,
+    3
+  ],
+  "decoded_image_samples": 6,
+  "mean_ms": 73.69250777493353,
+  "effective_latency_raw_frames": 3,
+  "effective_latency_ms": 85.71428571428571,
+  "env_fps": 35,
+  "obs_fps": 8.75,
+  "raw_max_steps": 3600,
+  "raw_parquet_bytes": 5494139826,
+  "converted_all_episode_row_counts_verified": true
+}

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/evidence/collect_sf.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+import argparse,hashlib,json,subprocess,sys
+import pyarrow.parquet as pq
+from latency_bench.data.exporters.starvla_lerobot import convert_canonical_rollout_to_lerobot
+ROOT=Path('/home/ubuntu/lzj/mean-profiling');REPO=Path('/home/ubuntu/lzj/latency-sensitive-bench')
+BUDGETS={'flappy':(200,18000,256,256,'gpu'),'deadly_corridor':(8500,3600,16,8,'cpu'),'ant':(100,1000,12,8,'gpu')}
+p=argparse.ArgumentParser();p.add_argument('task',choices=BUDGETS);p.add_argument('--finalize-only',action='store_true');args=p.parse_args()
+task=args.task;out=ROOT/task;cfg=json.loads((out/'teacher.flat.json').read_text());ck=Path(json.loads((out/'teacher.upload.json').read_text())['local_checkpoint']);episodes,cap,probe,replay,device=BUDGETS[task];raw=out/'data/raw'
+command=[sys.executable,str(REPO/'scripts/rollout_data/export_sf_teacher_dataset.py'),'--checkpoint-root',str(ck),'--output-dir',str(raw),'--max-episodes',str(episodes),'--max-steps-per-episode',str(cap),'--num-envs',str(probe),'--replay-num-envs',str(replay),'--device',device,'--val-fraction','0.1','--split-seed','0','--context-window','1','--filter-preset','none','--deterministic','--image-writer-workers','32' if device=='gpu' else '8']
+if task=='ant':command.extend(['--episode-return-gt','2500','--export-image-size','224,224'])
+else:command.extend(['--max-attempts',str(episodes)])
+(out/'collection-command.json').write_text(json.dumps(command,indent=2)+'\n')
+if not args.finalize_only:
+ subprocess.run(command,cwd=REPO,check=True)
+ convert_canonical_rollout_to_lerobot(raw,out/'data/lerobot',action_carrier='native')
+meta=json.loads((raw/'metadata.json').read_text());manifest=json.loads((out/'data/lerobot/manifest.json').read_text());assert meta['total_episodes']==episodes and manifest['state_dim']=={'flappy':24,'deadly_corridor':120,'ant':27}[task]
+train=set(pq.read_table(raw/'train.parquet',columns=['seed']).column('seed').unique().to_pylist());val=set(pq.read_table(raw/'val.parquet',columns=['seed']).column('seed').unique().to_pylist());assert len(train)==meta['train_episodes'] and len(val)==meta['val_episodes'] and train.isdisjoint(val)
+report={'task':task,'teacher_checkpoint':str(ck),'teacher_sha256':hashlib.sha256(ck.read_bytes()).hexdigest(),'episodes':episodes,'train_episodes':meta['train_episodes'],'val_episodes':meta['val_episodes'],'train_rows':meta['train_rows'],'val_rows':meta['val_rows'],'fixed_latency_ms':cfg['fixed_latency_ms'],'action_horizon':1,'unique_seeds':len(train|val),'state_dim':manifest['state_dim']};(out/'data/collection.complete.json').write_text(json.dumps(report,indent=2)+'\n');print(json.dumps(report),flush=True)

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/evidence/collection-command.json

@@ -0,0 +1,31 @@
+[
+  "/home/ubuntu/lzj/conda/envs/sf-data/bin/python",
+  "/home/ubuntu/lzj/latency-sensitive-bench/scripts/rollout_data/export_sf_teacher_dataset.py",
+  "--checkpoint-root",
+  "/home/ubuntu/lzj/mean-profiling/deadly_corridor/teacher-publication/checkpoint.pth",
+  "--output-dir",
+  "/home/ubuntu/lzj/mean-profiling/deadly_corridor/data/raw",
+  "--max-episodes",
+  "8500",
+  "--max-steps-per-episode",
+  "3600",
+  "--num-envs",
+  "16",
+  "--replay-num-envs",
+  "8",
+  "--device",
+  "cpu",
+  "--val-fraction",
+  "0.1",
+  "--split-seed",
+  "0",
+  "--context-window",
+  "1",
+  "--filter-preset",
+  "none",
+  "--deterministic",
+  "--image-writer-workers",
+  "8",
+  "--max-attempts",
+  "8500"
+]

latency-aware/deadly-corridor/mean_rtx3090_s42_8500ep_v1/evidence/doom-mean-reset.patch

@@ -0,0 +1,192 @@
+diff --git a/latency_bench/data/sf_teacher_rollout.py b/latency_bench/data/sf_teacher_rollout.py
+index ef45d95b..e32b942e 100644
+--- a/latency_bench/data/sf_teacher_rollout.py
++++ b/latency_bench/data/sf_teacher_rollout.py
+@@ -964,6 +964,11 @@ def _replay_accepted_specs(
+         row_buffer_size=int(row_buffer_size),
+         export_image_size=export_image_size,
+     )
++    stream_current_images = (
++        image_transform_config.normalized_transform() == "raw_rgb"
++        and int(context_window) <= 1
++        and filter_preset == "none"
++    )
+     image_resolution: list[int] | None = None
+ 
+     def assign_specs_to_slots(slots: list[int]) -> dict[int, int]:
+@@ -1051,7 +1056,10 @@ def _replay_accepted_specs(
+                         next_raw_frame=next_decision_step * obs_stride_raw_frames,
+                         max_steps_per_episode=int(max_steps_per_episode),
+                     )
+-                    active_episode_frames[slot].append(np.array(image_frame, copy=True))
++                    if stream_current_images:
++                        image_writer.write(image_frame, image_path)
++                    else:
++                        active_episode_frames[slot].append(np.array(image_frame, copy=True))
+                     row = teacher_dataset_row(
+                         env_name=env_name,
+                         spec=spec,
+@@ -1104,7 +1112,9 @@ def _replay_accepted_specs(
+                             )
+                         else:
+                             shard_rows = [dict(row) for row in final_rows]
+-                        if _is_demon_ghost_trail:
++                        if stream_current_images:
++                            image_writer.flush()
++                        elif _is_demon_ghost_trail:
+                             # Frames are pre-built ghost trail images; map directly.
+                             ep_steps = [int(row["decision_step"]) for row in final_rows]
+                             frames_by_decision_step = {
+@@ -1118,12 +1128,13 @@ def _replay_accepted_specs(
+                                 frames=active_episode_frames[slot],
+                                 config=image_transform_config,
+                             )
+-                        shard_rows = _write_filtered_episode_images(
+-                            image_writer,
+-                            shard_rows,
+-                            frames_by_decision_step,
+-                            context_window=int(context_window),
+-                        )
++                        if not stream_current_images:
++                            shard_rows = _write_filtered_episode_images(
++                                image_writer,
++                                shard_rows,
++                                frames_by_decision_step,
++                                context_window=int(context_window),
++                            )
+                         write_episode_row_shard(output_dir, shard_rows)
+                         progress.update(1)
+                         replayed += 1
+@@ -1272,9 +1283,8 @@ def _build_rollout_driver_runtime(
+         driver = BatchedTeacherRolloutDriver(runtime["envs"][0], int(num_envs))
+     else:
+         recreate_env = None
+-        if integration_name == "deadly_corridor" and (
+-            "export_zero_action_history" in config or getattr(cfg, "rollout_profile_seed_by_reset", False)
+-        ):
++        # ViZDoom applies reset seeds only while creating its native game.
++        if integration_name == "deadly_corridor":
+             from sample_factory.algo.utils.make_env import make_env_func_batched
+             from sample_factory.utils.attr_dict import AttrDict
+ 
+diff --git a/tests/data/test_sf_teacher_dataset_export.py b/tests/data/test_sf_teacher_dataset_export.py
+index e6b25233..61a58118 100644
+--- a/tests/data/test_sf_teacher_dataset_export.py
++++ b/tests/data/test_sf_teacher_dataset_export.py
+@@ -498,8 +498,11 @@ def test_serial_rollout_driver_continues_life_loss_with_seedless_reset(monkeypat
+     assert info == {"reset_seed": None}
+ 
+ 
+-def test_serial_teacher_replay_starts_each_seed_in_a_fresh_environment(monkeypatch):
+-    sf_teacher_drivers = _load_sf_teacher_drivers_for_direct_test(monkeypatch)
++@pytest.mark.parametrize("latency_type", ["fixed", "profile_sample"])
++def test_serial_teacher_replay_starts_each_seed_in_a_fresh_environment(monkeypatch, tmp_path, latency_type):
++    from contextlib import nullcontext
++    from latency_bench.data import sf_teacher_rollout
++    from sample_factory.algo.utils import make_env
+ 
+     class StatefulEnv:
+         action_space = object()
+@@ -514,13 +517,17 @@ def test_serial_teacher_replay_starts_each_seed_in_a_fresh_environment(monkeypat
+         def close(self):
+             return None
+ 
+-    driver = sf_teacher_drivers.SerialTeacherRolloutDriver(
+-        [StatefulEnv()], recreate_env=lambda _slot: StatefulEnv()
++    cfg = SimpleNamespace(frame_stack=4, action_history_decisions=8, latency_type=latency_type)
++    monkeypatch.setattr(sf_teacher_rollout, "sample_factory_init_log", lambda _: nullcontext())
++    monkeypatch.setattr(sf_teacher_rollout, "build_sample_factory_rollout_cfg", lambda _: (object(), cfg))
++    monkeypatch.setattr(sf_teacher_rollout, "build_env_and_policy", lambda *args: {"envs": [StatefulEnv()]})
++    monkeypatch.setattr(make_env, "make_env_func_batched", lambda *args, **kwargs: StatefulEnv())
++    _, _, driver, _ = sf_teacher_rollout._build_rollout_driver_runtime(
++        {"env_name": "deadly_corridor", "integration_name": "deadly_corridor"},
++        output_dir=tmp_path, num_envs=1, export_env_raw_rgb_frames=False,
+     )
+-
+     driver.reset_slots({0: 42})
+     observation, _ = driver.reset_slots({0: 43})[0]
+-
+     assert observation["obs"].tolist() == [43, 1]
+ 
+ 
+@@ -670,19 +677,22 @@ def test_collect_teacher_dataset_probe_retries_until_episode_is_accepted(monkeyp
+         def action_distribution(self):
+             return object()
+ 
++    reset_seeds = []
++
+     class FakeEnv:
+         def __init__(self):
+             self.action_space = object()
+-            self.reset_seeds: list[int] = []
++            self.reset_seeds = reset_seeds
+ 
+         def reset(self, seed=None):
+             self.reset_seeds.append(seed)
++            self.current_seed = seed
+             return np.zeros((3, 2, 2), dtype=np.uint8), [
+                 {"env_raw_rgb_frame_stack": _env_raw_rgb_frame_stack(5)}
+             ]
+ 
+         def step(self, action):
+-            reward = 3000.0 if len(self.reset_seeds) == 1 else 3000.001
++            reward = 3000.0 if self.current_seed == 11 else 3000.001
+             return (
+                 np.zeros((3, 2, 2), dtype=np.uint8),
+                 np.float32(reward),
+@@ -702,6 +712,8 @@ def test_collect_teacher_dataset_probe_retries_until_episode_is_accepted(monkeyp
+ 
+     fake_env = FakeEnv()
+     fake_actor = FakeActorCritic()
++    from sample_factory.algo.utils import make_env
++    monkeypatch.setattr(make_env, "make_env_func_batched", lambda *args, **kwargs: FakeEnv())
+     fake_cfg = SimpleNamespace(frame_stack=1, env_fps=35.0, obs_fps=17.5, seed=11)
+ 
+     monkeypatch.setattr(exporter, "build_sample_factory_rollout_cfg", lambda config: (object(), fake_cfg))
+@@ -1273,7 +1285,8 @@ def test_collect_teacher_dataset_saves_demon_attack_latest_raw_rgb_image(monkeyp
+ 
+ 
+ @pytest.mark.parametrize("export_history", [False, True])
+-def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch, tmp_path, export_history):
++@pytest.mark.parametrize("filter_preset", ["clean_v1", "none"])
++def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch, tmp_path, export_history, filter_preset):
+     import torch
+     from gymnasium.spaces import Discrete
+     from PIL import Image
+@@ -1307,6 +1320,9 @@ def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch,
+     class FakeEnv:
+         action_space = Discrete(2)
+ 
++        def __init__(self, exporting):
++            self.exporting = exporting
++
+         def reset(self, seed=None):
+             self.steps = 0
+             obs = {"obs": np.zeros((1, 12, 2, 2), dtype=np.uint8)}
+@@ -1323,6 +1339,9 @@ def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch,
+ 
+         def step(self, action):
+             self.steps += 1
++            if self.exporting and self.steps == 2 and filter_preset == "none":
++                image_path = output_dir / "images/train/episode_000000/decision_step_000000.png"
++                assert image_path.is_file(), "First frame must be on disk before the episode ends"
+             obs = {"obs": np.ones((1, 12, 2, 2), dtype=np.uint8)}
+             next_stack = np.stack([np.full((6, 5, 3), 50 + idx, dtype=np.uint8) for idx in range(4)], axis=0)
+             return (
+@@ -1354,7 +1373,7 @@ def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch,
+         exporter,
+         "build_env_and_policy",
+         lambda module, cfg, config, num_envs: {
+-            "envs": [FakeEnv()],
++            "envs": [FakeEnv(cfg.export_env_raw_rgb_frames)],
+             "env_info": object(),
+             "actor_critic": FakeActorCritic(),
+             "device": torch.device("cpu"),
+@@ -1382,6 +1401,7 @@ def test_collect_teacher_dataset_saves_flappy_latest_raw_rgb_image(monkeypatch,
+         export_image_size=None,
+         image_writer_workers=0,
+         row_buffer_size=1,
++        filter_preset=filter_preset,
+     )
+     train_rows = _read_shard_rows(output_dir, "train")
+     val_rows = _read_shard_rows(output_dir, "val")