diff --git a/.gitattributes b/.gitattributes
index 2abca71ea85c31a04bbb4a1b38489a6958d1290c..6cf2937b112d992a0e5ce692c15fcc1ec685ef19 100644
--- a/.gitattributes
+++ b/.gitattributes
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 experiments/robotwin-native/kinex-1/combined.mp4 filter=lfs diff=lfs merge=lfs -text
+experiments/robotwin-native/kinex_policy-1/combined.mp4 filter=lfs diff=lfs merge=lfs -text
+experiments/robotwin-native/standalone-2/combined.mp4 filter=lfs diff=lfs merge=lfs -text
+experiments/robotwin-native/kinex-2/combined.mp4 filter=lfs diff=lfs merge=lfs -text
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index 26b9a43130005746e779261ded1046bca7ee2354..2a2f6123318a5d159fc978a179b99034987251c6 100644
--- a/data.json
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index 0bced2e7d9f2da04ab45e2b8e463875e8f418059..310918b643aabcdd6ad9fb235e80ca33c2ed99d0 100644
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@@ -1,6 +1,6 @@
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diff --git a/experiments/robotwin-native/kinex-2/effective-model.json b/experiments/robotwin-native/kinex-2/effective-model.json
new file mode 100644
index 0000000000000000000000000000000000000000..c234a9c7e798741950c6c23501ac97ee69d9c71c
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/effective-model.json
@@ -0,0 +1,16 @@
+{
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+  "kinex_version": "1.1.0",
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+  "effort": "high",
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+  "model_recording": false,
+  "compaction": "native",
+  "transport": "sse",
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+  "max_request_retries": 50,
+  "usage_accounting": "reported-responses"
+}
\ No newline at end of file
diff --git a/experiments/robotwin-native/kinex-2/episode-report.json b/experiments/robotwin-native/kinex-2/episode-report.json
new file mode 100644
index 0000000000000000000000000000000000000000..c39fdfa7533b353c6de61096f17f3b19096b159c
--- /dev/null
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diff --git a/experiments/robotwin-native/kinex-2/episode.json b/experiments/robotwin-native/kinex-2/episode.json
new file mode 100644
index 0000000000000000000000000000000000000000..51566a1b7c966fb0353324e2ca738d437d18faeb
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/episode.json
@@ -0,0 +1,15 @@
+{
+  "episode": 1,
+  "initialization": {
+    "expert_plan_success": false,
+    "requested_seed": 0
+  },
+  "native_reward": 1.0,
+  "planned_episodes": 1,
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+  "simulation_time_s": 13.48,
+  "step_limit": 600,
+  "steps": 337,
+  "success": true,
+  "valid": true
+}
diff --git a/experiments/robotwin-native/kinex-2/evidence.tar.gz b/experiments/robotwin-native/kinex-2/evidence.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..45933964ab8023400163046943eea840e6852ba9
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/evidence.tar.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3b03d2f1aa3b6282dfaf35097503948c5943ddcf5ed58450795bb5f870dc7ca
+size 5165037
diff --git a/experiments/robotwin-native/kinex-2/instruction.json b/experiments/robotwin-native/kinex-2/instruction.json
new file mode 100644
index 0000000000000000000000000000000000000000..117bff786713490f127c6af684bb93646cd98d4d
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/instruction.json
@@ -0,0 +1,14 @@
+{
+  "binding_paths": {},
+  "bindings": {},
+  "context": {},
+  "instruction": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open",
+  "native_instruction": "",
+  "policy": "modified",
+  "prompt": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "prompt_template": "<!-- rlebench:resolved-episode-instruction -->\n# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "schema": "rlebench/episode-instruction/3",
+  "source": "catalog",
+  "task_id": "robotwin/place-dual-shoes-aloha-agilex",
+  "template": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open"
+}
diff --git a/experiments/robotwin-native/kinex-2/media.json b/experiments/robotwin-native/kinex-2/media.json
new file mode 100644
index 0000000000000000000000000000000000000000..81c901cf1accc50ae0259e58456f6a30d98cb7c8
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/media.json
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+    "task": "robotwin/place-dual-shoes-aloha-agilex",
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+    "role": "user",
+    "name": null,
+    "text": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:27.076Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \"harness.md\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:29.943Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:29.951Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: ls\n{\"path\": \".kinex\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:32.445Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "ls",
+    "text": "devices/\nkinex.json\nmemos/\npython/\nskills/\ntools/\nworkspace.yaml",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:32.449Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: bash\n{\"command\": \"find .kinex/devices .kinex/tools .kinex/skills .kinex/memos -maxdepth 3 -type f\", \"timeout\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:35.253Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "bash",
+    "text": ".kinex/devices/left_arm/links.py\n.kinex/devices/left_arm/arm.urdf\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/links.py\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/left_gripper/gripper.urdf\n.kinex/devices/right_arm/links.py\n.kinex/devices/right_arm/arm.urdf\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/links.py\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/right_gripper/gripper.urdf\n.kinex/devices/world/profile.yaml\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:35.269Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \".kinex/devices/world/profile.yaml\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:37.160Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:37.164Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \".kinex/devices/left_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/head_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/workspace.yaml\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.459Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.465Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.465Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.465Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.466Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.466Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.466Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"python\": \"from kinex import runtime,finish\\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})\", \"description\": \"inspect operation contracts\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:45.671Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:46.600Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"world\", \"action\": \"load\"}, {\"deviceID\": \"left_arm\", \"action\": \"load\"}, {\"deviceID\": \"right_arm\", \"action\": \"load\"}, {\"deviceID\": \"left_gripper\", \"action\": \"load\"}, {\"deviceID\": \"right_gripper\", \"action\": \"load\"}, {\"deviceID\": \"head_camera\", \"action\": \"load\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:51.042Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:56.432Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: device\n{\"deviceID\": \"head_camera\", \"action\": \"image\"}\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/gripper.urdf\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:00.667Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "device",
+    "text": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-s12ft6c7.png\"\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/07deeeee-1.png"
+    ],
+    "timestamp": "2026-10-28T21:39:00.867Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:00.872Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:00.874Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Inspecting shoe-box kinematics**\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py\", \"offset\": 1, \"limit\": 300}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:06.893Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:06.897Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"python\": \"import asyncio\\nfrom kinex import runtime, helpers, communication,finish\\nasync def main():\\n async with communication() as io:\\n  data={}\\n  for d in ['left_arm','right_arm']:\\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\\n   data[d]={'q':m.position,'fk':helpers.joint_model(d).fk(m.position),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\\n  f=await helpers.camera_frame(io,'head_camera')\\n  import numpy as np\\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\\n  finish(data)\\nasyncio.run(main())\", \"description\": \"inspect measured state and schemas\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:19.714Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223561.071991926] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 14, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 8, in main\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n    q = joint_target(positions, self.joints, check_limits=False)\\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n    vector(value, len(joints), \\\"joint target\\\")\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n    raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:21.475Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"python\": \"import asyncio\\nfrom kinex import runtime,helpers,communication,finish\\nasync def main():\\n async with communication() as io:\\n  data={}\\n  for d in ['left_arm','right_arm']:\\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\\n   q=list(m.position)\\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(q),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\\n  f=await helpers.camera_frame(io,'head_camera')\\n  import numpy as np\\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\\n  finish(data)\\nasyncio.run(main())\", \"description\": \"measure initial scene\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:26.606Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223568.172439105] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 15, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 12, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:29.070Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py\", \"offset\": 1, \"limit\": 220}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:31.726Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:31.730Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"python\": \"import asyncio\\nfrom kinex import runtime,helpers,communication,finish\\nasync def main():\\n async with communication() as io:\\n  data={}\\n  for d in ['left_arm','right_arm']:\\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(list(m.position)),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\\n  im,dep=await helpers.camera_frame(io,'head_camera')\\n  import numpy as np\\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\\n  finish(data)\\nasyncio.run(main())\", \"description\": \"measure initial scene\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:37.269Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223578.614148509] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\"fk\":[0.1056935554023861,-0.0011901315627094447,0.15999839199046592,-0.000007715996484952103,0.0000021542621098628648,-2.4666643061721644e-7,0.9999999999678809],\"tcp\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0}},\n    \"right_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\"fk\":[0.10569355567032844,-0.0011901318683733137,0.15999839080293787,-0.000007716210574917094,0.0000021565576609891396,-2.4864112524367e-7,0.9999999999678738],\"tcp\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}},\n    \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"right_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"right_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:39.212Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Estimating shoe 3D points**\nTool call: exec\n{\"python\": \"import numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import finish\\nf=np.load('/tmp/head.npz');d=f['depth']*.001\\nrot=R.from_quat([-.948683301192,0,0,.316227769682]).as_matrix();t=np.array([-.032,-.45,1.35])\\nv,u=np.indices(d.shape);p=np.stack([(u-159.5)*d/358.64218,(v-119.5)*d/358.64218,d],axis=-1)@rot.T+t\\nnp.save('/tmp/points.npy',p)\\nfinish({str((u,v)):p[v,u].tolist() for u,v in [(65,124),(70,137),(78,156),(83,176),(258,109),(260,127),(264,149),(261,166),(120,145),(235,145),(111,214),(241,214),(175,195),(175,170),(45,198)]})\", \"description\": \"project shoe and box points\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:51.107Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(65, 124)\": [-0.24701096162197103,0.03140910949709719,0.6910568320592497],\n    \"(70, 137)\": [-0.21991292201045629,-0.027594196989208564,0.7255543520299529],\n    \"(78, 156)\": [-0.20947912417886824,-0.044987609248771654,0.6775092925698912],\n    \"(83, 176)\": [-0.183232908521803,-0.11395591142723871,0.7157830657360426],\n    \"(258, 109)\": [0.19705560076620107,0.06993367883975005,0.6974502592814215],\n    \"(260, 127)\": [0.1815303772690653,-0.005548079149456486,0.7308389405389644],\n    \"(264, 149)\": [0.20284967663312783,-0.01943781830802399,0.6654216358573333],\n    \"(261, 166)\": [0.18054192688656984,-0.07729713643164932,0.6907771470716708],\n    \"(120, 145)\": [-0.11416267255569326,-0.0448333771340384,0.721374966820128],\n    \"(235, 145)\": [0.12504510830265422,-0.0448333771340384,0.721374966820128],\n    \"(111, 214)\": [-0.1258512028897438,-0.1798917686242506,0.6850811723963807],\n    \"(241, 214)\": [0.1259359683794026,-0.17950256367990514,0.6841230761030037],\n    \"(175, 195)\": [0.001018982875912705,-0.12026751685270726,0.6422993613618274],\n    \"(175, 170)\": [0.002574851178966185,-0.060117673106498126,0.6424117444706855],\n    \"(45, 198)\": [-0.2752759024607758,-0.12622992376337028,0.6403275561418671]\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:53.041Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Planning shoe grasp**\n\n**Aligning gripper for shoe**\nTool call: exec\n{\"python\": \"import numpy as np\\nfrom kinex import finish\\np=np.load('/tmp/points.npy');v,u=np.indices(p.shape[:2]);out={}\\nfor name,mask in [('left',(u>45)&(u<106)&(v>105)&(v<194)),('right',(u>239)&(u<287)&(v>90)&(v<180))]:\\n sel=p[mask&(p[:,:,2]>.653)&(p[:,:,2]<.78)]\\n xy=sel[:,:2];cov=np.cov(xy.T);w,V=np.linalg.eigh(cov);axis=V[:,-1];axis*=np.sign(axis[1]);proj=xy@axis;perp=np.array([axis[1],-axis[0]])\\n out[name]={'bounds':np.percentile(sel,[0,5,50,95,100],axis=0),'axis':axis,'along':np.percentile(proj,[0,50,100]),'across':np.percentile(xy@perp,[0,50,100])}\\nfinish(out)\", \"description\": \"estimate shoe geometry\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:13.207Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": {\"bounds\":[[-0.27703949870034816,-0.11849672931365157,0.6530756384513174],[-0.26232817835314304,-0.11589681011362746,0.6648956826818977],[-0.2084795206185731,-0.048453607301866264,0.6989171661594558],[-0.16517033707524315,0.04587652017197147,0.7280738549115844],[-0.15957980670316027,0.0673668820840671,0.7397426331669105]],\"axis\":[-0.4348054796080431,0.9005243999486185],\"along\":[-0.02822664134255312,0.04733788568632173,0.17347461851403906],\"across\":[-0.2438903331252665,-0.20888409446241507,-0.17148698275945262]},\n    \"right\": {\"bounds\":[[0.12579376536245684,-0.12617690826397737,0.6530134103302274],[0.16420628560756578,-0.07877877371633266,0.6623519701729963],[0.19117578484493938,-0.005752951117641736,0.69816639561117],[0.22156610312819316,0.09690986291317003,0.7299211106280531],[0.23969001705265122,0.12021601950746491,0.7417706810964337]],\"axis\":[0.16462025977367772,0.9863570195786345],\"along\":[-0.10348059217440107,0.02641075351907217,0.15416704938916012],\"across\":[0.13832065240589805,0.18958687182277106,0.2274015727704394]}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:14.556Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Refining the box mask**\nI’ll grasp each shoe from above, rotate the tips left, and place the initially left shoe in the nearer row of the box.\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\", \"offset\": 1, \"limit\": 240}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:24.192Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n    \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n    Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in <device>/base; link\n    defaults to the model's tip_link.\n    \"\"\"\n\n    def __init__(self, profile, directory=None):\n        if profile.get(\"type\") != \"joint\":\n            raise ValueError(\"expected a joint device profile\")\n        model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n        if self.tree is None:\n            if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n                raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n            self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n        self.joints = urdf.commanded(self.tree)\n        self.names = [joint[\"name\"] for joint in self.joints]\n        self.tip = model[\"tip_link\"]\n\n    def chain(self, link):\n        link = link or self.tip\n        if not link:\n            raise ValueError(\"name a link; the model declares no tip_link\")\n        return Chain(self.tree, link)\n\n    def fk(self, positions, link=None):\n        \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n        q = joint_target(positions, self.joints, check_limits=False)\n        return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n    def ik(self, target, seed, link=None, **options):\n        \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n        Joints off the link's path keep their seed values. options override the\n        solver tolerances, damping, max_iterations and search budget.\n        \"\"\"\n        unknown = options.keys() - {*DEFAULTS, \"budget\"}\n        if unknown:\n            raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n        values = dict(zip(self.names, project_seed(seed, self.joints)))\n        values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n        return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n    \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n    home = profile.get(\"home\")\n    if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n        return 0.0\n    return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n    \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n    joint_target(actions[-1], joints)\n    previous = None\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n        for i, (q, joint) in enumerate(zip(target, joints)):\n            limits = joint[\"limits\"]\n            if limits is None:\n                continue\n            margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n            low, high = limits[\"min\"], limits[\"max\"]\n            if not low - margin <= q <= high + margin:\n                raise ValueError(\"target outside joint limits recovery tolerance\")\n            if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n                raise ValueError(\"joint limits recovery must move inward\")\n        previous = target\n\n\ndef command(profile, actions, joints):\n    \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n    if not isinstance(actions, list) or not actions:\n        raise ValueError(\"actions must be a nonempty sequence\")\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n    if profile[\"limits\"] == \"strict\":\n        recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n    return actions\n\n\ndef project_seed(value, joints):\n    \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n    values = vector(value, len(joints), \"IK seed\")\n    return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n            for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n    \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n    if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n        raise ValueError(\"joint state requires unique names and matching positions\")\n    positions = dict(zip(message.name, message.position))\n    names = [joint[\"name\"] for joint in joints]\n    if any(name not in positions for name in names):\n        raise ValueError(\"joint state is missing modeled joints\")\n    return joint_target([positions[name] for name in names], joints, check_limits=False)\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:24.196Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Creating staged motion script**\nTool call: write\n{\"path\": \".kinex/tools/dual_shoes.py\", \"content\": \"\\\"\\\"\\\"Measured Cartesian dual-arm moves through public Kinex ROS operations.\\nInvoke with exec file and args: targets {left/right: [x,y,z,qx,qy,qz,qw]},\\nduration, settle; optional grips {left/right: opening}. World coordinates.\\nA dry_run solves without publishing; each path uses measured start FK.\\n\\\"\\\"\\\"\\nimport asyncio\\nimport numpy as np\\nfrom scipy.spatial.transform import Rotation, Slerp\\nfrom kinex import communication, helpers, finish, args\\n\\nasync def state(io, device):\\n    async with io.subscribe(device+'.joint_state') as stream:\\n        return await stream.next(timeout_s=10)\\n\\ndef matrix(p):\\n    t=np.eye(4);t[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();t[:3,3]=p[:3];return t\\n\\ndef pose(t):\\n    return [*t[:3,3],*Rotation.from_matrix(t[:3,:3]).as_quat()]\\n\\nasync def main():\\n    a=args(); duration=a.get('duration',1.2); plans={}; report={}\\n    async with communication() as io:\\n        for side,target in a.get('targets',{}).items():\\n            dev=side+'_arm'; model=helpers.joint_model(dev)\\n            measured=await state(io,dev); q=list(measured.position)\\n            base=matrix((await helpers.relative_pose(io,{'frame':dev+':base'},{'frame':'world:base'}))['pose'])\\n            tcp=np.eye(4);tcp[0,3]=.12\\n            start=base@matrix(model.fk(q))@tcp; end=matrix(target)\\n            interp=Slerp([0,1],Rotation.from_matrix(np.stack([start[:3,:3],end[:3,:3]])))\\n            points=[]; max_jump=0\\n            for i in range(1,a.get('segments',20)+1):\\n                f=i/a.get('segments',20);t=np.eye(4);t[:3,3]=start[:3,3]*(1-f)+end[:3,3]*f;t[:3,:3]=interp(f).as_matrix()\\n                goal=pose(np.linalg.inv(base)@t@np.linalg.inv(tcp))\\n                qnew=model.ik(goal,q);max_jump=max(max_jump,float(np.max(np.abs(np.array(qnew)-q))));q=qnew\\n                ns=round(duration*f*1e9)\\n                points.append({'positions':q,'time_from_start':{'sec':ns//10**9,'nanosec':ns%10**9}})\\n            if max_jump>1.0: raise ValueError(f'{side}: IK discontinuity {max_jump}')\\n            plans[dev]={'joint_names':model.names,'points':points}\\n            report[side]={'target_q':q,'max_segment_joint_change':max_jump}\\n        if not a.get('dry_run',False):\\n            for dev,payload in plans.items(): await io.publish(dev+'.joint_target',payload)\\n            for side,opening in a.get('grips',{}).items(): await io.publish(side+'_gripper.target',{'data':opening})\\n            report['wait']=await io.request('world.wait',duration_s=duration+a.get('settle',.2))\\n            for side in ['left','right']:\\n                m=await state(io,side+'_arm');g=await state(io,side+'_gripper')\\n                report[side]={**report.get(side,{}),'measured_q':list(m.position),'grip':list(g.position),'tcp':await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})}\\n            im,dep=await helpers.camera_frame(io,'head_camera')\\n            np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\\n            image=helpers.save_image(im)\\n        else: image=None\\n    finish(report,file=image)\\n\\nif __name__=='__main__': asyncio.run(main())\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:49.414Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/tools/dual_shoes.py",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:49.418Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"python\": \"import numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import finish\\nout={}\\nfor s,axis in [('left',[-.435,.9005,0]),('right',[.10,.995,0]),('placed',[-1,0,0])]:\\n x=np.array([0,0,-1]);z=-np.array(axis);z=z/np.linalg.norm(z);y=np.cross(z,x)\\n out[s]=R.from_matrix(np.column_stack([x,y,z])).as_quat()\\nfinish(out)\", \"description\": \"construct grasp orientations\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:54.106Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": [0.3758414690119471,0.5989517427731067,-0.3758414690119471,0.5989517427731066],\n    \"right\": [0.5244041261335186,0.47434197842288933,-0.5244041261335186,0.47434197842288933],\n    \"placed\": [0,0.7071067811865475,0,0.7071067811865475]\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:55.933Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [-0.218, -0.025, 0.84, 0.37584147, 0.59895174, -0.37584147, 0.59895174], \"right\": [0.195, 0.016, 0.84, 0.52440413, 0.47434198, -0.52440413, 0.47434198]}, \"grips\": {\"left\": 1, \"right\": 1}, \"duration\": 2, \"settle\": 0.2}, \"description\": \"approach both shoes\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:00.359Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223662.312752592] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22774460233231092,2.0844783296165446,1.6334247882592328,-1.119696352368475,0.002011036180461048,2.49168103259622],\"max_segment_joint_change\":0.22250770540570428,\"measured_q\":[-0.19359128177165985,2.0299289226531982,1.565936803817749,-1.0698388814926147,0.0637018159031868,2.444208860397339],\"grip\":[0.9992598511955955],\"tcp\":{\"pose\":[-0.22299369029360194,-0.02944199607878123,0.8439472305082514,0.3711196237801609,0.5970864937471737,-0.3314537937041148,0.6292029294823159],\"timestamp\":2.2}},\n    \"right\": {\"target_q\":[0.22891849509608184,2.2770167296021664,1.946258985399993,-1.2399737668878201,0.0038143270660531453,-2.7921574517349774],\"max_segment_joint_change\":0.2557527373667139,\"measured_q\":[0.19502022862434387,2.2170157432556152,1.8614673614501953,-1.1837477684020996,-0.06620503216981888,-2.7387287616729736],\"grip\":[1.0035958859053524],\"tcp\":{\"pose\":[0.20106956124805897,0.01130916671671306,0.8441301058365255,-0.5273385980969397,-0.4719980791625396,0.5607531031936284,-0.4297531541277429],\"timestamp\":2.2}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.2,\"waited_s\":2.2},\n    \"file\": \"/tmp/kinex-observation-vuwcfag0.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/8d9f2c0d-1.png"
+    ],
+    "timestamp": "2026-10-28T21:41:15.320Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Preparing descent parameters**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [-0.218, -0.025, 0.695, 0.37584147, 0.59895174, -0.37584147, 0.59895174], \"right\": [0.195, 0.016, 0.695, 0.52440413, 0.47434198, -0.52440413, 0.47434198]}, \"duration\": 1.3, \"settle\": 0.4}, \"description\": \"lower to shoe grasp height\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:20.723Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223682.804429596] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22587400015586576,2.3274100314457447,1.4029364137932692,-0.6462568482762985,0.004351146139304931,2.4966583272771543],\"max_segment_joint_change\":0.023721534514010267,\"measured_q\":[-0.2192988246679306,2.315551280975342,1.404956340789795,-0.6554389595985413,0.006320989690721035,2.4952962398529053],\"grip\":[1.000401415608146],\"tcp\":{\"pose\":[-0.22011327724616148,-0.023654303271224808,0.6998869872945025,0.3753037660380011,0.6003254223692802,-0.37166967146596513,0.6005148838848378],\"timestamp\":3.92}},\n    \"right\": {\"target_q\":[0.23063784230102252,2.4714411321064156,1.6872846072585452,-0.7865804977321514,0.006038718669629817,-2.7882938737519463],\"max_segment_joint_change\":0.023468032426248397,\"measured_q\":[0.22381655871868134,2.4601662158966064,1.689003586769104,-0.7951595187187195,0.0037091674748808146,-2.786982297897339],\"grip\":[1.000115448778326],\"tcp\":{\"pose\":[0.19746808623661757,0.017369893443527185,0.7001516902630236,0.526383385692651,0.47392899178612075,-0.526300156869011,0.470446582399094],\"timestamp\":3.92}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":3.92,\"waited_s\":1.7199999999999998},\n    \"file\": \"/tmp/kinex-observation-49lcysgb.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/3c8e0d38-1.png"
+    ],
+    "timestamp": "2026-10-28T21:41:34.431Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"grips\": {\"left\": 0, \"right\": 0}, \"duration\": 0.6, \"settle\": 0}, \"description\": \"grasp both shoes\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:37.263Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223699.319257784] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":4.5200000000000005,\"waited_s\":0.6000000000000005},\n    \"left\": {\"measured_q\":[-0.2207689732313156,2.3197131156921387,1.4022727012634277,-0.646481454372406,0.004202340263873339,2.4976632595062256],\"grip\":[0.8248065384951505],\"tcp\":{\"pose\":[-0.21981260859167018,-0.023525114497031374,0.6977895497680353,0.3768516763506197,0.600604329088065,-0.37237554199764294,0.5988269446462514],\"timestamp\":4.52}},\n    \"right\": {\"measured_q\":[0.2260061502456665,2.4656732082366943,1.6879534721374512,-0.7873106598854065,0.006010189186781645,-2.786764621734619],\"grip\":[0.8215297054160725],\"tcp\":{\"pose\":[0.19675509976374933,0.01755103700765198,0.6975930297370944,0.5268113232251944,0.4745009262840679,-0.5249098951839438,0.47094405465539396],\"timestamp\":4.52}},\n    \"file\": \"/tmp/kinex-observation-eib75mac.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/551566c6-1.png"
+    ],
+    "timestamp": "2026-10-28T21:41:44.844Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Preparing to lift shoe**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [-0.218, -0.025, 0.865, 0.37584147, 0.59895174, -0.37584147, 0.59895174], \"right\": [0.195, 0.016, 0.865, 0.52440413, 0.47434198, -0.52440413, 0.47434198]}, \"duration\": 1.4, \"settle\": 0.3}, \"description\": \"lift and verify both grasps\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:51.410Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223713.308781947] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22844013591941273,2.0678736313216377,1.7085401660146515,-1.2114074372635,0.0019312142797508374,2.4907547833771906],\"max_segment_joint_change\":0.031346100891458395,\"measured_q\":[-0.2239367812871933,2.0769543647766113,1.692858338356018,-1.19147789478302,0.0024510447401553392,2.492839813232422],\"grip\":[0.8405653969808059],\"tcp\":{\"pose\":[-0.2196101692368914,-0.02448676045971626,0.8576266700337278,0.3735575027731245,0.598660183425509,-0.3766380207818991,0.6001704576237794],\"timestamp\":6.24}},\n    \"right\": {\"target_q\":[0.22832395150517062,2.271398028168711,2.0359137374683787,-1.334673049706994,0.003974610351548502,-2.7930490273624735],\"max_segment_joint_change\":0.03253001686157586,\"measured_q\":[0.22686776518821716,2.278221845626831,2.0181984901428223,-1.3149802684783936,0.004381020087748766,-2.79101824760437],\"grip\":[0.8270359554074027],\"tcp\":{\"pose\":[0.19568852418996996,0.016476706520842377,0.857661751844948,-0.5246346404832504,-0.4720684139275534,0.5257000886763412,-0.474920333679894],\"timestamp\":6.24}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.24,\"waited_s\":1.7199999999999998},\n    \"file\": \"/tmp/kinex-observation-chiiyo5r.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/c734630c-1.png"
+    ],
+    "timestamp": "2026-10-28T21:42:07.067Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Rotating left shoe**\n\n**Measuring box interior**\nTool call: exec\n{\"python\": \"import numpy as np\\nfrom kinex import finish\\np=np.load('/tmp/points.npy');finish({str((u,v)):p[v,u].tolist() for u,v in [(175,140),(175,145),(175,155),(175,163),(175,210),(175,218),(175,221),(175,225),(108,200),(245,200)]})\", \"description\": \"measure box rim and interior\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:28.145Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(175, 140)\": [-0.00040722973521965106,-0.044827186153967746,0.7401296101250818],\n    \"(175, 145)\": [0.00024104872438596853,-0.0448333771340384,0.721374966820128],\n    \"(175, 155)\": [0.001624042771544619,-0.04480792020862767,0.6813940593653645],\n    \"(175, 163)\": [0.0027909439988347273,-0.04511127723001751,0.6474165414712326],\n    \"(175, 210)\": [0.00015461159643855027,-0.1537931500053472,0.6421551363302781],\n    \"(175, 218)\": [-0.00027757404329852714,-0.17087271682999766,0.6418454611257971],\n    \"(175, 221)\": [-0.004858741824511546,-0.21538516883598618,0.7409611222694521],\n    \"(175, 225)\": [-0.004426556184774468,-0.2173418458088874,0.7269936144780211],\n    \"(108, 200)\": [-0.12591254536764196,-0.17503626728740773,0.7387227986229724],\n    \"(245, 200)\": [0.12582025415973105,-0.17167279655086232,0.731245401664232]\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:29.520Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Positioning shoes in box**\n\n**Moving left shoe**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [0.003, -0.173, 0.83, 0, 0.70710678, 0, 0.70710678], \"right\": [0.2, 0.016, 0.94, 0, 0.70710678, 0, 0.70710678]}, \"duration\": 2, \"settle\": 0.4}, \"description\": \"rotate tips left and position near shoe\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:54.336Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223776.430757245] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.9123295133525193,2.040620894932573,1.526702598322709,-1.0568256105508256,0.0017302909673467626,0.6856940754923477],\"max_segment_joint_change\":0.09190104728563075,\"measured_q\":[-0.8980924487113953,2.0321245193481445,1.51948082447052,-1.0570282936096191,0.0017319483449682593,0.7115294933319092],\"grip\":[0.8391735607927496],\"tcp\":{\"pose\":[-0.0018207208108431128,-0.16987044001734503,0.8313141069326292,0.004367543172538346,0.7067911316088494,-0.003909370937005834,0.7073980051319024],\"timestamp\":8.64}},\n    \"right\": {\"target_q\":[0.2149396947228276,2.3495298995565355,2.472670536500331,-1.6938670996952807,0.004595068148560996,-4.476247787375465],\"max_segment_joint_change\":0.0844328656620017,\"measured_q\":[0.22082558274269104,2.3440868854522705,2.461483955383301,-1.6856166124343872,0.004535673651844263,-4.451779842376709],\"grip\":[0.8248719681393016],\"tcp\":{\"pose\":[0.197473170754076,0.015326894137119296,0.9395929366625656,0.007442272502197977,0.7072963359824993,-0.005721082827813494,0.7068548471199813],\"timestamp\":8.64}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.64,\"waited_s\":2.4000000000000004},\n    \"file\": \"/tmp/kinex-observation-h5dm5cs9.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/95050dd7-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:08.598Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Lowering left shoe tip**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [0.003, -0.171, 0.795, 0, 0.70710678, 0, 0.70710678]}, \"duration\": 0.6, \"settle\": 0.24}, \"description\": \"lower near shoe above box rim\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:14.865Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223796.860208708] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.9076583925397403,2.089445255422863,1.4546758564746218,-0.9359729029341021,0.0020983747438904758,0.6910483067882552],\"max_segment_joint_change\":0.006180449019134837,\"measured_q\":[-0.901189923286438,2.0784759521484375,1.4630359411239624,-0.9493597149848938,0.0016712871147319674,0.6950069069862366],\"grip\":[0.8396634703332728],\"tcp\":{\"pose\":[0.0018037855152258642,-0.1686042508031495,0.8009633382598002,0.0005767295511718286,0.7055685951862317,0.002302956350375791,0.7086376515977687],\"timestamp\":9.48}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":9.48,\"waited_s\":0.8399999999999999},\n    \"right\": {\"measured_q\":[0.21868762373924255,2.349149227142334,2.472313165664673,-1.6932917833328247,0.004566576331853867,-4.473906993865967],\"grip\":[0.8240381804379551],\"tcp\":{\"pose\":[0.19837489371624997,0.015575779624339592,0.9400232677332943,-0.0002924959106377356,0.7071658500472698,0.0007037309396101165,0.7070472966752855],\"timestamp\":9.48}},\n    \"file\": \"/tmp/kinex-observation-w3fhvu6z.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/dd8205a3-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:21.456Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Opening shoe sole**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"grips\": {\"left\": 1}, \"duration\": 0.52, \"settle\": 0}, \"description\": \"release near shoe into box\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:28.127Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223810.138928558] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10,\"waited_s\":0.5199999999999996},\n    \"left\": {\"measured_q\":[-0.9030988812446594,2.0846312046051025,1.4563316106796265,-0.9375838041305542,0.002041139407083392,0.6914576292037964],\"grip\":[0.9781321043317969],\"tcp\":{\"pose\":[0.0023022107816283444,-0.16925432379288402,0.7971472161026872,-0.00036430205739246274,0.7057867511512136,0.0025707515284416164,0.7084195934755086],\"timestamp\":10}},\n    \"right\": {\"measured_q\":[0.21778197586536407,2.349522113800049,2.472609519958496,-1.6934796571731567,0.004572504665702581,-4.474327564239502],\"grip\":[0.8236210156570781],\"tcp\":{\"pose\":[0.19876386776936317,0.015692001397513644,0.9399359967789964,-0.00021207173898674109,0.7071431483194491,0.0004420190870238846,0.7070702422175338],\"timestamp\":10}},\n    \"file\": \"/tmp/kinex-observation-mqaw_xsm.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/015664b9-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:33.861Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Repositioning both wrists**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"left\": [-0.23, -0.18, 0.94, 0, 0.70710678, 0, 0.70710678], \"right\": [0.003, -0.087, 0.88, 0, 0.70710678, 0, 0.70710678]}, \"duration\": 1.6, \"settle\": 0.3}, \"description\": \"clear near row and position far shoe\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:43.208Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223825.237700211] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.3029406615483511,1.4601317643672989,1.2774506423510985,-1.38814618012482,0.00008826971323923154,1.2861114749667022],\"max_segment_joint_change\":0.04320701833293622,\"measured_q\":[-0.3268199861049652,1.473667860031128,1.268896222114563,-1.3711458444595337,0.00013764412142336369,1.2655565738677979],\"grip\":[0.9954985949126157],\"tcp\":{\"pose\":[-0.2234420230825827,-0.1791360265468177,0.9347732029075545,-0.000600390655715932,0.70762774173887,-0.002856605292725736,0.7065794070441124],\"timestamp\":11.92}},\n    \"right\": {\"target_q\":[0.7180670816966506,2.2785383810171047,2.1069806150836756,-1.3991631957307018,0.0038320742290281293,-3.973956566069662],\"max_segment_joint_change\":0.06328158398588979,\"measured_q\":[0.7010597586631775,2.268319606781006,2.102823495864868,-1.402452826499939,0.004190298728644848,-3.985568046569824],\"grip\":[0.822635371576656],\"tcp\":{\"pose\":[0.00977399022646136,-0.08302936425841839,0.8832660165004471,0.0027850239077577012,0.7076324371141649,-0.0010922535121931497,0.7065744012968245],\"timestamp\":11.92}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.92,\"waited_s\":1.92},\n    \"file\": \"/tmp/kinex-observation-5145i7tt.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/95a00c6e-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:54.730Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Lowering over the shoe**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"targets\": {\"right\": [0.003, -0.087, 0.795, 0, 0.70710678, 0, 0.70710678]}, \"duration\": 0.9, \"settle\": 0.32}, \"description\": \"lower far shoe above box rim\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:00.730Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223842.720896783] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"right\": {\"target_q\":[0.7200100387934371,2.314264594631272,1.8292255773152175,-1.0856937116394345,0.004208342230618559,-3.971584752701272],\"max_segment_joint_change\":0.019064546089732737,\"measured_q\":[0.712832510471344,2.305619955062866,1.8402382135391235,-1.099536657333374,0.004314719699323177,-3.9705166816711426],\"grip\":[0.8249491155147552],\"tcp\":{\"pose\":[0.004880994000601269,-0.08428218124142661,0.8013795046666288,0.004495685880999968,0.7084282284060708,-0.001358558475059154,0.7057672338135041],\"timestamp\":13.16}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.16,\"waited_s\":1.2400000000000002},\n    \"left\": {\"measured_q\":[-0.3054666519165039,1.4618515968322754,1.2769138813018799,-1.3879971504211426,0.00009789218893274665,1.2860099077224731],\"grip\":[0.9978405155918815],\"tcp\":{\"pose\":[-0.22938359630191002,-0.1801734683142392,0.9394668852690865,0.00013833433721333058,0.7073163702745995,-0.0015805167780718991,0.7068953495192136],\"timestamp\":13.16}},\n    \"file\": \"/tmp/kinex-observation-qgiw6y15.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/8698e793-1.png"
+    ],
+    "timestamp": "2026-10-28T21:44:08.851Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes.py\", \"args\": {\"grips\": {\"right\": 1, \"left\": 1}, \"duration\": 0.56, \"settle\": 0}, \"description\": \"release far shoe\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:11.910Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223853.744106609] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"episode ended during wait\",\n  \"code\": \"operation_rejected\",\n  \"details\": {\n    \"operation\": \"world.wait\",\n    \"type\": \"kinex_interfaces/srv/Wait\",\n    \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":13.48,\"waited_s\":0.3200000000000003}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:15.622Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Stopping the device**\nTool call: device_stop\n{\"reason\": \"World reported episode ended during release wait; hold and inspect final measurements.\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:21.811Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "device_stop",
+    "text": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:21.914Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"head_camera\", \"action\": \"image\"}, {\"deviceID\": \"left_gripper\", \"action\": \"joint_state\"}, {\"deviceID\": \"right_gripper\", \"action\": \"joint_state\"}, {\"deviceID\": \"world\", \"action\": \"time\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:25.100Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":13.48,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-hilw3rij.png\"},\n    {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9982522617686879],\"velocities\":[0.00007679601284590635],\"joint_names\":[\"left_gripper\"],\"timestamp\":13.48}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9529129727320238],\"velocities\":[0.17699392681772058],\"joint_names\":[\"right_gripper\"],\"timestamp\":13.48}},\n    {\"deviceID\":\"world\",\"action\":\"time\",\"ok\":true,\"data\":{\"clock\":{\"sec\":13,\"nanosec\":480000000}}}\n  ]\n}",
+    "images": [
+      "experiments/robotwin-native/kinex-2/observations/20d111e0-1.png"
+    ],
+    "timestamp": "2026-10-28T21:44:25.261Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Refining resource memo estimates**\n\n**Recording gripper state**\nTool call: write\n{\"path\": \".kinex/memos/dual_shoes.md\", \"content\": \"# RoboTwin dual shoes / ALOHA AgileX\\n\\nPublic interface: read harness.md and refreshed profiles each episode; load world, both arms/grippers, head_camera. Native period 0.04 s, stepped world.wait only. Use typed kinex.communication operations. Do not reset or inspect private simulator state.\\n\\nReusable tool: `.kinex/tools/dual_shoes.py` accepts world-frame TCP targets, duration/settle, normalized grip targets, and dry_run. Uses measured joint state, calibrated base transforms, 0.12 m flange-to-TCP translation, Cartesian interpolation/Slerp and continuous seeded IK. Publishes both trajectories before one sequential wait. Returns measured TCP/grips and camera. Read schemas before using. JointModel.fk/ik require list, not ROS array; camera_frame returns (image,depth).\\n\\nPerception: paired head depth mm -> optical coordinates using profile intrinsics -> world via camera mount. In observed scene table z=.640, box bottom .642, rim .740, inner x about [-.125,.130], y[-.215,-.045]. Shoes length ~.22 m, width .07 m. Re-measure positions each scene. Initial left shoe pointed roughly [-.435,.9005,0], right [.10,.995,0]. Grasp across width at center: downward TCP x axis; local z opposite shoe toe direction; local y=cross(z,x). Grasp TCP z=.695 worked, lift to .865. Both measured grips stalled ~.82-.84 and visual lift confirmed shoes attached (stall alone is not evidence).\\n\\nPlace tips world -X using quaternion xyzw [0,.70710678,0,.70710678]. Near row for initially left shoe: observed target TCP [.003,-.171,.795]. Far row: [.003,-.087,.795]. Grasp offsets kept soles roughly .055 below TCP. Release just above rim: gripper fingers cannot descend to floor in narrow box without contacting walls. Shoe falls flat into box. Place sequentially; keep other held shoe high/outside, then withdraw released arm up/outward while bringing other shoe above far row. Example left clearance [-.23,-.18,.94]. Waits .3-.4 s after trajectories reduce tracking error; verify actual state. Near shoe was visibly flat, toe left, before placing far shoe.\\n\\nWorld can terminate during a release wait before requested duration. A rejected/interrupted wait is NOT proof of success. Stop and inspect final images/state; do not keep advancing terminal world. Observed terminal at t=13.48 during second release (337 frames): image showed both shoes flat inside box with tips left and initially left shoe nearer robot. Open commands had been sent to both; final openings left .9983 and right .9529. Native verdict is owner-only; do not claim it. Tool currently surfaces rejected wait as error, so final observation requires separate device reads.\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:45:05.154Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/memos/dual_shoes.md",
+    "images": [],
+    "timestamp": "2026-10-28T21:45:05.157Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Both shoes appear flat in the box, tips pointing left, with the initially left shoe nearer the robot. Both grippers were commanded open. The episode ended during final release; the native verdict remains unconfirmed.",
+    "images": [],
+    "timestamp": "2026-10-28T21:45:09.896Z"
+  }
+]
diff --git a/experiments/robotwin-native/kinex-2/session.jsonl b/experiments/robotwin-native/kinex-2/session.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1d4184d2caf00fbb461c3fe30970853317468e0
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/session.jsonl
@@ -0,0 +1,79 @@
+{"type":"session","version":3,"id":"01a18473-31ef-7247-8e38-e8b2337b6833","timestamp":"2026-10-28T21:38:24.368Z","cwd":"/workspace"}
+{"type":"model_change","id":"380c1db1","parentId":null,"timestamp":"2026-10-28T21:38:26.857Z","provider":"openai-codex","modelId":"gpt-6-astra"}
+{"type":"thinking_level_change","id":"078adb23","parentId":"380c1db1","timestamp":"2026-10-28T21:38:26.857Z","thinkingLevel":"high"}
+{"type":"message","id":"2624d288","parentId":"078adb23","timestamp":"2026-10-28T21:38:27.076Z","message":{"role":"system","content":"","sections":{"preamble":"You are an expert coding assistant operating inside pi, a coding agent harness. You help users by reading files, executing commands, editing code, and writing new files.","tools":"<tools>\n- read: Read file contents\n- write: Create or overwrite files\n- edit: Make precise file edits with exact text replacement, including multiple disjoint edits in one call\n- bash: Execute bash commands (ls, grep, find, etc.)\n- grep: Search file contents for patterns (respects .gitignore)\n- find: Find files by glob pattern (respects .gitignore)\n- ls: List directory contents\n\nIn addition to the tools above, you may have access to other custom tools depending on the project.\n</tools>","rules":"<rules>\n- Use read to examine files instead of cat or sed.\n- Use write only for new files or complete rewrites.\n- Use edit for precise changes (edits[].oldText must match exactly)\n- When changing multiple separate locations in one file, use one edit call with multiple entries in edits[] instead of multiple edit calls\n- Each edits[].oldText is matched against the original file, not after earlier edits are applied. Do not emit overlapping or nested edits. Merge nearby changes into one edit.\n- Keep edits[].oldText as small as possible while still being unique in the file. Do not pad with large unchanged regions.\n- You can inspect PI_* environment variables for current model and session details.\n- Be concise in your responses\n- Show file paths clearly when working with files\n</rules>","docs":"<docs>\nPi documentation (read only when the user asks about pi itself, its SDK, extensions, themes, skills, or TUI):\n- Main documentation: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/README.md\n- Additional docs: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/docs\n- Examples: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/examples (extensions, custom tools, SDK)\n- When reading pi docs or examples, resolve docs/... under Additional docs and examples/... under Examples, not the current working directory\n- When asked about: extensions (docs/extensions.md, examples/extensions/), themes (docs/themes.md), skills (docs/skills.md), prompt templates (docs/prompt-templates.md), TUI components (docs/tui.md), keybindings (docs/keybindings.md), SDK integrations (docs/sdk.md), custom providers (docs/custom-provider.md), adding models (docs/models.md), pi packages (docs/packages.md), environment variables (docs/environment-variables.md), MCP servers (docs/mcp.md), codemode scripts and non-LLM models such as classifiers and image models (docs/codemode.md)\n- When working on pi topics, read the docs and examples, and follow .md cross-references before implementing\n- Always read pi .md files completely and follow links to related docs (e.g., tui.md for TUI API details)\n</docs>","skills":"<skills>\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>btw</name>\n    <description>Helps you use the /btw side-conversation workflow effectively. Use when you want to think in parallel, ask side questions without interrupting ongoing work, or inject a side thread back into the main agent.</description>\n    <location>/opt/kinex/node_modules/pi-btw/skills/btw/SKILL.md</location>\n  </skill>\n</available_skills>\n</skills>","cwd":"<cwd>\n/workspace\n</cwd>","kinex":"<kinex>\nYou are Kinex, a coding agent for robotics work. Use the coding tools normally and use Kinex tools for robot/device access. Direct camera reads include the image; no separate file read is needed.\n\nRuntime facts arrive in kinex_context messages; they are host state, not user requests, and the latest value of each section applies. Tools and skills written during a task become available after the current tool batch.\n\nAsk the user through the question tool.\n\n\n# Robotics\n\n## Workspace\n\nThe nearest `.kinex/` (here or an ancestor) holds the robot:\n- `workspace.yaml`: identity and safety limits.\n- `devices/<name>/`: `profile.yaml` plus node sources (`node.py`, URDF, ...).\n- `device/`, `tools/`, `skills/`: workspace guides, tools and skills; override builtins.\n- `lib/`: Python shared by exec, tools and nodes.\n- `kinex.json`, `python/`: config and the Kinex Python environment.\n- `recordings/`: recordings.\nWrite `profile.yaml` and `workspace.yaml` only with profile_write.\n\n## Devices\n\nA device is a profile plus the ROS topics and services its nodes serve. Its\ndescriptions are its contract; operation names come from the profile, not the type.\nOnly nodes touch SDKs, simulators and model services, and hold persistent resources.\nThe world device owns time and waits. Use the device tool to load, unload, read and command.\n\n## Python\n\nexec runs Kinex Python, separate from device and simulator environments; never patch sys.path.\nDiscover devices with `kinex.runtime` and return only what you need via `finish()`.\nROS resources a script creates end with it. Install missing ROS packages through setup.\n\n## Principles\n\nFollow profile units, limits and safety; never assume missing facts. Avoid unintended\ncollisions along the whole path. A sent command or arrival is not success; verify with\nmeasurements. On faults, unexpected contact or lost feedback, call device_stop first.\nWork efficiently: skip checks that add nothing, combine tool calls if necessary\n(e.g. perceive and return the results inside the same exec for motion),\nbut never at the cost of soundness and task completion.\n\n## Tools and the user\n\nTool descriptions define execution semantics; skills give procedures. Don't repeat\npermission prompts as questions. Ask only for missing facts, choices or physical\nfeedback.\n\n\n# Task-scoped setup\n\nOnboarding prepares an embodiment through an agent-authored DAG. Use setup_plan to\ninspect/save nodes and select only the current task's target nodes and prerequisites.\nMissing plans and incomplete profiles do not by themselves prevent task execution;\nexisting operation-specific validation and host permissions still apply. Global profile\ncompleteness is descriptive. Return to the task as soon as its required facts exist.\n\nReuse verified factory, model, simulator and calibration facts before measuring.\nKeep unknowns as ???. Split broad objectives into independently useful facts so a\npartial calibration can satisfy a narrower task. Optional work stays deferred.\nBind procedures to actual devices and frames; morphology does not imply an interface.\nThe frame graph and setup dependency DAG are different graphs. Plans live in\n`.kinex/setup/plan.json`, calibration attempts in `.kinex/calib/<run>/`.\n\nAvoid adding new tests unless necessary. Skip python tests during task execution or setup attempts.\n\nPrepare or repair devices with core's setup-device skill; keep existing working\nintegrations unless replacement is requested.\n\n</kinex>"},"timestamp":1793223507074,"toolsAdded":[{"name":"read","description":"Read the contents of a file. Supports text files and images (jpg, png, gif, webp, bmp). Images are sent as attachments. For text files, output is truncated to 2000 lines or 50KB (whichever is hit first). Use offset/limit for large files. When you need the full file, continue with offset until complete.","parameters":{"type":"object","required":["path"],"properties":{"path":{"type":"string","description":"Path to the file to read (relative or absolute)"},"offset":{"type":"number","description":"Line number to start reading from (1-indexed)"},"limit":{"type":"number","description":"Maximum number of lines to read"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"write","description":"Write content to a file. Creates the file if it doesn't exist, overwrites if it does. Automatically creates parent directories.","parameters":{"type":"object","required":["path","content"],"properties":{"path":{"type":"string","description":"Path to the file to write (relative or absolute)"},"content":{"type":"string","description":"Content to write to the file"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"edit","description":"Edit a single file using exact text replacement. Every edits[].oldText must match a unique, non-overlapping region of the original file. If two changes affect the same block or nearby lines, merge them into one edit instead of emitting overlapping edits. Do not include large unchanged regions just to connect distant changes.","parameters":{"type":"object","required":["path","edits"],"properties":{"path":{"type":"string","description":"Path to the file to edit (relative or absolute)"},"edits":{"type":"array","items":{"type":"object","required":["oldText","newText"],"properties":{"oldText":{"type":"string","description":"Exact text for one targeted replacement. It must be unique in the original file and must not overlap with any other edits[].oldText in the same call."},"newText":{"type":"string","description":"Replacement text for this targeted edit."}}},"description":"One or more targeted replacements. Each edit is matched against the original file, not incrementally. Do not include overlapping or nested edits. If two changes touch the same block or nearby lines, merge them into one edit instead."}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"bash","description":"Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last 2000 lines or 50KB (whichever is hit first). If truncated, full output is saved to a temp file. Optionally provide a timeout in seconds.","parameters":{"type":"object","required":["command"],"properties":{"command":{"type":"string","description":"Shell command to execute"},"timeout":{"type":"number","description":"Timeout in seconds (optional, no default timeout)"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"grep","description":"Search file contents for a pattern. Returns matching lines with file paths and line numbers. Respects .gitignore. Output is truncated to 100 matches or 50KB (whichever is hit first). Long lines are truncated to 500 chars.","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Search pattern (regex or literal string)"},"path":{"type":"string","description":"Directory or file to search (default: current directory)"},"glob":{"type":"string","description":"Filter files by glob pattern, e.g. '*.ts' or '**/*.spec.ts'"},"ignoreCase":{"type":"boolean","description":"Case-insensitive search (default: false)"},"literal":{"type":"boolean","description":"Treat pattern as literal string instead of regex (default: false)"},"context":{"type":"number","description":"Number of lines to show before and after each match (default: 0)"},"limit":{"type":"number","description":"Maximum number of matches to return (default: 100)"}}}},{"name":"find","description":"Search for files by glob pattern. Returns matching file paths relative to the search directory. Respects .gitignore. Output is truncated to 1000 results or 50KB (whichever is hit first).","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Glob pattern to match files, e.g. '*.ts', '**/*.json', or 'src/**/*.spec.ts'"},"path":{"type":"string","description":"Directory to search in (default: current directory)"},"limit":{"type":"number","description":"Maximum number of results (default: 1000)"}}}},{"name":"ls","description":"List directory contents. Returns entries sorted alphabetically, with '/' suffix for directories. Includes dotfiles. Output is truncated to 500 entries or 50KB (whichever is hit first).","parameters":{"type":"object","properties":{"path":{"type":"string","description":"Directory to list (default: current directory)"},"limit":{"type":"number","description":"Maximum number of entries to return (default: 500)"}}}},{"name":"web_search","description":"Search the web using the current supported provider (Google Gemini, xAI Grok, OpenAI, Anthropic, DeepSeek, Ollama, or OpenCode Zen/Go). Optionally include URLs to analyze alongside search results.","parameters":{"type":"object","required":["query"],"properties":{"query":{"type":"string","description":"The search query or question to answer"},"urls":{"type":"array","items":{"type":"string"},"description":"Additional URLs to analyze along with search (up to 20)","maxItems":20}}}},{"name":"calib_compute","description":"Run supported offline calibration/introspection using archived evidence. No device\nmotion or profile writes. Use an existing run ID, a plan node, or a procedure name\nwith concrete device-role bindings. First use creates an isolated attempt. Use the\nprocedure descriptor returned by setup_plan full:true for roles and computations.\nCustom procedure code runs through existing host tools, not this offline registry.\n\nResults distinguish computation, quality and exact resolved fields.\nValid computed results that miss accuracy targets may still be accepted within\nthe user's accuracy/time budget. Do not repeat calibration solely to meet those\ntargets when the user accepts the result. If the user explicitly keeps a result\nthat exceeds a limit, honor it without repeated confirmation or forced recollection;\nwhen requested, persist its computed fields with profile_write from the archived\nsolver data even if failed quality leaves proposedChanges empty, using the\nprocedure's frame conventions, and record the errors, limits and user decision in\nevidence. Always preserve measured errors and the original quality assessment,\nnever mark a failed-quality node done, and never treat missing or invalid results\nas approximate estimates.\nApply accepted proposedChanges with core profile_write, preserving unrelated facts.\nProposed patches never clear calibration requests automatically. A pivot solve\nmeasures translation only; RGB-D color calibration does not measure depth facts.\nURDF introspection of the bound arm or target is offline and does not require\nactivation. Base verification uses explicit measured checks and tolerances,\nwithout controller tuning. Sensor bias averages calib_sample readings of a static,\nunloaded imu or force_torque target and proposes the current bias plus each axis's\nmean residual; specific-force accelerometers need gravity_m_s2, the expected\nstatic reading in the IMU frame.\n\nArchive manual/factory facts or external solver results as evidence for setup_plan\nrecord. Reuse valid evidence and stop as soon as the current task is supported.\n\nBase measurement files contain required_checks:[name,...] and checks:[{name,\nexpected,measured,reference,...}]. reference names the archived source or independent\nmeasurement. Numerical expected/measured values need explicit tolerance and units.\nOther assertions compare exact values. Optional timestamps must contain at least\ntwo distinct increasing sample times. Only required_checks determine coverage;\nmissing checks fail quality. Do not claim untested axes or operating conditions.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","oneOf":[{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"urdf-introspect"}},"required":["operation"],"additionalProperties":false},{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"fit-mount"},"points":{"type":"string","minLength":1,"description":"Input file path, relative to invocation directory or absolute"},"parent_frame":{"type":"string","minLength":1}},"required":["operation","points","parent_frame"],"additionalProperties":false},{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"fit-dual"},"points":{"type":"string","minLength":1,"description":"Input file path, relative to invocation directory or 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file path, relative to invocation directory or absolute"},"gravity_m_s2":{"description":"expected static specific force in the IMU frame, e.g. [0, 0, 9.80665] when level with +z up","type":"array","prefixItems":[{"type":"number"},{"type":"number"},{"type":"number"}],"items":false,"minItems":3,"maxItems":3}},"required":["operation","samples"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"calib_sample","description":"Read one sample into an isolated .kinex/calib/<run>/samples.json. Start with a plan\nnode or procedure plus bindings; subsequent samples use the returned run ID. The\ndevice must be one of the bound participants. Inputs and participants cannot change\nwithin an attempt. Returns the new sample, run ID and count; camera attachments are\ncopied into immutable attempt artifacts, including both RGB and depth files for\nRGBD readings. Both copies must succeed before a sample is appended.\nUse note to identify the collection step. Samples\ncontain measured state and timestamps; this tool cannot command or load devices.\n\nOptional settle waits for measured velocities to remain below the threshold over\ndistinct samples. Unsupported or malformed feedback is an error. Cached readings\ndo not establish stability. A timeout after a reading saves that reading with\nsettled:false; cancellation saves nothing. Without settle, the sample makes no\nclaim of stationary motion. Settling supports joint devices whose joint states\nmeasure every commanded joint's velocity, and locomotion devices whose odometry\nmeasures their declared velocity axes; their runtime.observation must name the\nodometry operation.\nThis blocking sampler does not advance simulator time; arrange world waits\noutside the sampler when collecting simulation evidence.\n\nFor joint devices, optional frame names a URDF link; FK from that exact sample's\npositions stores kinematics with frame and pose in the device base alongside the\nmeasured data. Use it for TCP calibration; joint-device reads contain no link\nposes. FK and artifact capture still run after a settle timeout, subject to caller\ncancellation.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","description":"device to sample, e.g. arm"},"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"args":{"description":"arguments for the observation operation","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"frame":{"description":"joint-device URDF link whose pose to compute through FK from this sample","type":"string","minLength":1},"settle":{"description":"wait for the device to stop moving before recording; omit to record immediately","type":"object","properties":{"velocity_threshold":{"default":0.005,"description":"max |joint velocity| that counts as settled","type":"number","minimum":0},"stable_reads":{"default":3,"description":"consecutive settled reads required","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"timeout_s":{"default":30,"type":"number","exclusiveMinimum":0}}},"note":{"description":"label stored with the sample, e.g. 'touch 3, rolled +45deg'","type":"string"}},"required":["deviceID"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device","description":"Call a named device's declared operation with action and args. Inspect the compact operation catalog for names, topic/service types and payload contracts. Prefer exec with async communication helpers for streams and composed operations so intermediate data stays outside model context.\nload/unload accept no arguments; status observes readiness without starting or restarting resources. stop invokes the declared stopping behavior and cancels running scripts that commanded the device. Runtime definition changes require unload, validation and explicit reload. Never terminate borrowed services.\nImage observations return selected image attachments and acquisition timestamps. A sent command is not measured arrival. The world device owns time and wait semantics; reads never advance time. relative_pose uses tf2 and ROS frame IDs.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"compact":{"default":false,"description":"single-line JSON output — use in polling/sampling loops","type":"boolean"}},"required":["deviceID","action"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device_stop","description":"Stop the named device, or every loaded device with a stop operation when deviceID\nis omitted, and cancel running scripts that commanded a stopped device. Stops use\neach node's declared behavior and affected-device scope. No approval is required.\nResults report each device's acknowledgment or failure; inspect failures before resuming.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"description":"device to stop; omit to stop every loaded device with a stop operation","type":"string"},"reason":{"description":"why the stop was triggered","type":"string"}},"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"devices","description":"Call devices concurrently with calls: [{deviceID, action, args?}]; action is an explicit declared operation. Returns {results: [...]} in input order, with deviceID, action, ok and\ndata/file or error per entry.\nImage observations may carry attachments; attached images follow call order. Permissions are\nchecked before dispatch. Sampling is not simultaneous and effects are not atomic.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"calls":{"minItems":1,"type":"array","items":{"type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}}},"required":["deviceID","action"],"additionalProperties":false}}},"required":["calls"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"exec","description":"Run Python source or a script in the workspace's Kinex Python. Device environments and the world clock never choose this interpreter. Python cannot load/unload devices or control recording; lifecycle remains host-controlled. One execution approval covers composed operations.\nDiscover interfaces on demand, without starting devices:\n  from kinex import runtime, finish\n  devices = runtime.devices()\n  # runtime.operations(device_name) lists declared operations, even if unloaded.\n  # runtime.describe(operation) resolves ROS payload schemas when needed.\n  finish({\"devices\": [{\"name\": d[\"name\"], \"loaded\": d[\"loaded\"]} for d in devices]})\nUse actual operation names from these queries, not names inferred from device types. Catalogs remain in Python until you explicitly return selected fields. Load required devices through the host before communication.\nFor async protocol work, import communication, args, finish from kinex. For example, where args supplies a discovered command topic and its ROS payload:\n  async def main():\n      a = args()\n      async with communication() as io:\n          await io.publish(a[\"operation\"], a[\"payload\"])\n      finish({\"sent\": True})\n  # Execute with asyncio.run(main()). Sending is not measured arrival.\nUse io.subscribe(operation, depth=1) and await stream.next(timeout_s=...) for output topics (with or async with closes the stream early); await io.request(operation, **fields) for services. World wait operations own scheduling, one wait at a time. Observations and tf2 queries never advance world time. Bound work, inspect fresh measured feedback, and stop on terminal task status. Service rejections raise kinex.OperationError with operation, response and structured details; inspect the response and relevant feedback before deciding how to recover.\nImport helpers with from kinex import helpers. Read kinex/helpers/helpers.txt under the Python library directory in runtime context for available functions and implementation paths. Builtin Python tools are interfaces to these helpers; call them directly to share this invocation's communication and cleanup. Put workspace-specific helpers in .kinex/lib, which exec can import.\nFailure, timeout or cancellation stops every device this invocation commanded; success leaves their motion running. finish(value, file=paths) returns selected summaries as JSON, converting ROS messages and NumPy values, and images only through file paths, after leaving the communication scope. Do not print entire catalogs or raw streams unless requested.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"description":{"description":"Short lowercase description of the task, for display","type":"string"},"python":{"description":"Python source, including multiline scripts; provide exactly one of python or file","type":"string","minLength":1},"file":{"description":"Script path, relative to the working directory or absolute","type":"string","minLength":1},"args":{"default":{},"description":"Values available through kinex.args()","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"argv":{"default":[],"description":"Script command-line arguments","type":"array","items":{"type":"string"}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":2147483}},"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"locate_object_3d","description":"[Python tool] Locate an object in the camera optical frame (+X right, +Y down, +Z forward) using a detection or segmentation device with RGBD or explicit optical depth. Samples only inside a supplied mask; otherwise uses the central half of the box. Returns a representative point in metres, pixel, detection_score, depth_samples, and reference (visible_surface or box_region). Box estimates may include background. rotation_xyzw is null. No mount calibration is needed; convert frames with relative_pose after constructing a full local pose with an explicit orientation.\n\nDevice input camera: rgb | rgbd (checked against the selected device).\nDevice input model: model (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"camera":{"type":"string","description":"rgb or rgbd camera with calibrated color intrinsics"},"model":{"type":"string","description":"model device whose Json detect takes an image path, prompt, min_score and max_results and returns results with box, score, label and an optional mask PNG path"},"prompt":{"type":"string","minLength":1,"description":"object description, e.g. red mug"},"depth":{"type":"number","exclusiveMinimum":0,"description":"optional optical Z in metres at the representative region pixel; otherwise use the camera's paired RGBD depth"},"min_score":{"type":"number","minimum":0,"maximum":1,"default":0.25,"description":"minimum detection score"},"instance":{"type":"integer","minimum":0,"default":0,"description":"zero-based index after filtering and sorting by descending score; ties keep model order"}},"required":["camera","model","prompt"]}},{"name":"profile_write","description":"Persist profile facts in the workspace:\n- robot: partial manifest; identity requires id, name and kind initially. identity.kind describes the whole robot's morphology and must be arm, mobile, quadruped, humanoid or other. Use other when no category fits; ??? is not accepted for this field.\n- device: partial profile; name is required, type and description on first write, plus frames.base for spatial devices. Use setup-device and the selected guide for new devices.\n- needed: {item,note?}; queue the reason without changing existing provenance.\n\nPatches deep-merge. To switch a strict variant, supply the complete top-level section and list it in replace (e.g. [\"model\"]). Arrays replace wholesale. null removes an optional section only when listed in replace. Changing device type replaces its profile. Invalid updates are not saved.\n\nOutstanding requests persist across reads and replacements. Clear a resolved request with top-level needs_calibration:null for profile:<device> (or in access for access:<device>); the store rejects clearing incomplete facts. Unknown required facts remain ???. Set source to the origin of the data; keep diagnostics in calibration evidence.\n\nNew devices use loaded:false; activation uses the device tool (action=\"load\") or /load, not profile edits. Profiles live at .kinex/devices/<name>/profile.yaml beside node sources, using the nearest .kinex/ in the current directory or its ancestors (the current directory's .kinex/ if none exists). Validation errors identify fields and the guide to consult.\n\nDevice description gives general identity, capabilities and limitations. Each operation requires its own description for payload conventions and behavior; both descriptions are required strings and may be empty. Typed templates guide setup, while actual operation names are device-specific.\n\nPhysical facts (model, limits, color, frames, etc.) are top-level. Every frame requires parent: device:frame or null for an independent root; there is no implicit parent. Root transform components are ignored. Non-root null components mean unknown, never identity. Mounts are frames.base.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"kind":{"type":"string","enum":["robot","device","needed"]},"replace":{"default":[],"description":"Supplied top-level sections to replace instead of merge; null removes optional sections","type":"array","items":{"type":"string"}},"data":{"type":"object","propertyNames":{"type":"string"},"additionalProperties":{},"description":"partial workspace manifest patch (identity required on first write) for kind=robot, partial device patch (must include name; type on first write) for kind=device, or {item, note?} for kind=needed"}},"required":["kind","data"],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"relative_pose","description":"[Python tool] Transform a frame-attached pose through tf2. Use device:frame references or ROS frame IDs and an optional world timestamp. Missing frame relationships and unavailable timestamps are errors.","parameters":{"type":"object","additionalProperties":false,"properties":{"child":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"parent":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"timestamp":{"type":"number","minimum":0}},"required":["child","parent"]}},{"name":"run_policy","description":"[Python tool] Infer once and submit the shared valid action prefix concurrently. Returns actions_submitted, duration_s and ik_failure. Use max_actions to cap the execution horizon for receding horizon control. IK rejection returns normally, including an empty prefix, with diagnostic ik_failure (device, zero-based action_index, error), or null. Joint-only submission is asynchronous. Chunks containing gripper or suction devices publish persistent targets paced by world waits before returning; the final target remains active.\n\nDevice input model: model (checked against the selected device).\nDevice input command[].device: joint | gripper | suction (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"model":{"type":"string","description":"model device serving an \"infer\" action (obs -> action)"},"observation":{"type":"object","description":"Observation object matching the model's infer obs contract."},"inference_args":{"type":"object","description":"Additional infer arguments validated by the model operation; cannot override obs or task."},"command":{"type":"array","minItems":1,"description":"Devices in flat-vector split order; named action maps use device names. Cartesian chunks are converted through IK before any write.","items":{"type":"object","additionalProperties":false,"properties":{"device":{"type":"string","description":"joint","gripper or suction device":null},"space":{"type":"string","enum":["device","pose","pose_delta"],"default":"device","description":"device: absolute joint values in URDF document order; gripper and suction values in [-1, 1] release at -1 and grip at +1: grippers interpolate from open to closed, proportional suction takes the vacuum level (v + 1) / 2, binary suction switches on above 0. pose: xyz + quaternion xyzw (7). pose_delta: translation metres + axis-angle rotation radians (6), accumulated from the starting pose. Cartesian modes solve URDF IK and require a joint device with model.tip_link."},"frame":{"type":"string","description":"Cartesian reference frame, frozen at planning start; defaults to the device canonical frame. Rotation deltas left-compose in these axes."},"target_frame":{"type":"string","description":"Controlled frame, rigidly attached to the arm's tip_link; defaults to that link."}},"required":["device"]}},"task":{"type":"string","description":"task or goal handed to the model, e.g. a language instruction"},"world":{"type":"string","default":"world","description":"World device providing relative wait for end-effector action pacing."},"action_hz":{"type":"number","exclusiveMinimum":0,"default":10,"description":"actions replayed per second of the world clock"},"max_actions":{"type":"integer","minimum":1,"description":"Execution horizon: submit at most the first N inferred actions, discarding the remainder before validation and IK. Omit to use the whole chunk. Each call infers anew from the supplied observation. IK rejection may shorten this prefix further."}},"required":["model","observation","command"]}},{"name":"setup_plan","description":"Inspect or maintain the agent-authored embodiment DAG. This is task-scoped planning,\nnever a gate on device operations. Read with full:true for the complete graph and\navailable procedure descriptors; the default shows only the current task's gaps.\nUse the returned revision for save/select/record; conflicting edits must be reread.\n\nSave version:1 nodes with IDs, kind prepare/calibrate/verify, skill, concrete device\nrole bindings, dependsOn IDs, input facts and expected output facts. A profile fact\nuses kind:profile, device (omit for workspace manifest), and JSON pointer; optional\nvalue requires an exact match. Use narrow profile pointers for both inputs and\noutputs, tracking only facts relevant to the node's conclusion. Keep volatile\nloaded/meta fields and live poses out of persistent calibration dependencies and\noutputs. File facts use paths relative to .kinex. Select a\ntask with objective, target node IDs and optional direct facts. Only the targets'\ndependency closure is required now; unrelated work stays deferred. task:null\ndefers all calibration. An empty targets list can describe a task requiring no\ncalibration. Skill descriptors list their exact roles, requirements and coverage.\n\nRecord done only after verifying persisted outputs and archiving supporting\nevidence files (paths relative to .kinex). Existing factory/model/calibration facts\ncan satisfy a node without new measurements. Partial results may satisfy a narrower\ntask: split broad nodes into independently useful outputs. Never mark deferred work\ndone or clear incomplete profile requests. Verification receipts describe evidence,\nnot safety certification. Revisions and stale evidence never prevent unrelated\noperations. Replan only relevant gaps and return to the task as soon as satisfied.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","oneOf":[{"type":"object","properties":{"action":{"type":"string","const":"read"},"full":{"default":false,"type":"boolean"}},"required":["action"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"save"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"plan":{"type":"object","properties":{"version":{"type":"number","const":1},"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"title":{"type":"string","minLength":1},"kind":{"type":"string","enum":["prepare","calibrate","verify"]},"skill":{"type":"string","minLength":1},"bindings":{"default":{},"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"dependsOn":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"outputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"optional":{"default":false,"type":"boolean"}},"required":["id","title","kind","skill"],"additionalProperties":false}},"task":{"default":null,"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["version","nodes"],"additionalProperties":false}},"required":["action","revision","plan"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"select"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"task":{"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["action","revision","task"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"record"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"outcome":{"type":"object","properties":{"status":{"type":"string","enum":["done","partial","failed","needs-input"]},"note":{"type":"string","minLength":1},"evidence":{"minItems":1,"type":"array","items":{"type":"string","minLength":1}}},"required":["status","note","evidence"],"additionalProperties":false}},"required":["action","revision","node","outcome"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"skill","description":"Loads a skill by name and returns its full instructions. 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+{"type":"message","id":"0493caf2","parentId":"2624d288","timestamp":"2026-10-28T21:38:27.076Z","message":{"role":"user","content":[{"type":"text","text":"# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}],"timestamp":1793223507073}}
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[resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>\n\nproblems:\n(none)\n</kinex_context>","display":false,"details":{"core.guides":"Device guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-bd0a44110fcf49c58277037f711e7975/config/device, /opt/kinex/packages/core/assets/builtin/device.","core.python":"{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}","core.recording":"","core.skills":"<available_skills>\nLoad a skill with the skill tool when its description matches the task.\n  - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n  - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n  - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n  - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n  - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n  - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n  - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n  - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n  - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n  - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>","problems":""},"id":"6f184821","parentId":"0493caf2","timestamp":"2026-10-28T21:38:27.077Z"}
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+{"type":"message","id":"fff103a2","parentId":"8539a895","timestamp":"2026-10-28T21:38:41.465Z","message":{"role":"toolResult","toolCallId":"call_4cceb880d6784989bd7c305d425d4dfa|fc_0e05b35c94578fcb016ae26b5f6d248191a72c4e3a2ad19716","toolName":"read","content":[{"type":"text","text":"name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n"}],"isError":false,"durationMs":4,"timestamp":1793223521465}}
+{"type":"message","id":"c87f235c","parentId":"fff103a2","timestamp":"2026-10-28T21:38:41.465Z","message":{"role":"toolResult","toolCallId":"call_0ac1df91f68e4f75a427082873c0a21e|fc_0e05b35c94578fcb016ae26b615f4c8191b9d12716aaffa541","toolName":"read","content":[{"type":"text","text":"name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n"}],"isError":false,"durationMs":4,"timestamp":1793223521465}}
+{"type":"message","id":"8eb24d71","parentId":"c87f235c","timestamp":"2026-10-28T21:38:41.465Z","message":{"role":"toolResult","toolCallId":"call_e23d1a1ce4e3491598e87028dbbe8b60|fc_0e05b35c94578fcb016ae26b615f4c8191b6a76c2b250df9eb","toolName":"read","content":[{"type":"text","text":"name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n"}],"isError":false,"durationMs":4,"timestamp":1793223521465}}
+{"type":"message","id":"b73b5d41","parentId":"8eb24d71","timestamp":"2026-10-28T21:38:41.466Z","message":{"role":"toolResult","toolCallId":"call_57217fff6fb842a2ba2ebf0be8d60587|fc_0e05b35c94578fcb016ae26b615f4c81918138bbfb49b2064e","toolName":"read","content":[{"type":"text","text":"name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n"}],"isError":false,"durationMs":4,"timestamp":1793223521465}}
+{"type":"message","id":"19b57d67","parentId":"b73b5d41","timestamp":"2026-10-28T21:38:41.466Z","message":{"role":"toolResult","toolCallId":"call_cea94425e2fa4e5f85a2ed68057a9bdd|fc_0e05b35c94578fcb016ae26b615f4c8191a3b10ce3816ee991","toolName":"read","content":[{"type":"text","text":"name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n"}],"isError":false,"durationMs":4,"timestamp":1793223521466}}
+{"type":"message","id":"0fa84f2c","parentId":"19b57d67","timestamp":"2026-10-28T21:38:41.466Z","message":{"role":"toolResult","toolCallId":"call_e6b73f775a6747bd9a935378ace8ee6f|fc_0e05b35c94578fcb016ae26b615f4c8191b0a29903d0af9be0","toolName":"read","content":[{"type":"text","text":"identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n"}],"isError":false,"durationMs":4,"timestamp":1793223521466}}
+{"type":"message","id":"d85504a9","parentId":"0fa84f2c","timestamp":"2026-10-28T21:38:45.671Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_400056f3564b4e9c9ea3a21b816e7625|fc_0e05b35c94578fcb016ae26b63222481918bcd825a71e9a64e","name":"exec","arguments":{"python":"from kinex import runtime,finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})","description":"inspect operation contracts"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":4161,"output":55,"cacheRead":12288,"cacheWrite":0,"reasoning":0,"totalTokens":16504,"cost":{"input":0.04161,"output":0.0027500000000000003,"cacheRead":0.012288,"cacheWrite":0,"total":0.056648000000000004}},"stopReason":"toolUse","timestamp":1793223521497,"responseId":"resp_0e05b35c94578fcb016ae26b61d278819180e1b59c88c664a5","rawStopReason":"completed","durationMs":4174,"thinkingLevel":"high"}}
+{"type":"message","id":"ac5dc29d","parentId":"d85504a9","timestamp":"2026-10-28T21:38:46.600Z","message":{"role":"toolResult","toolCallId":"call_400056f3564b4e9c9ea3a21b816e7625|fc_0e05b35c94578fcb016ae26b63222481918bcd825a71e9a64e","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"\",\n  \"result\": {\n    \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. 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"}],"details":{"kinex":{"title":"image on head_camera"}},"isError":false,"durationMs":126,"timestamp":1793223540867}}
+{"type":"message","id":"cd6142f6","parentId":"07deeeee","timestamp":"2026-10-28T21:39:00.872Z","message":{"role":"toolResult","toolCallId":"call_4f112267e1f54522ac8278187813134c|fc_0e05b35c94578fcb016ae26b748d7481918c67e322c71a25f4","toolName":"read","content":[{"type":"text","text":"Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n"}],"isError":false,"durationMs":3,"timestamp":1793223540872}}
+{"type":"message","id":"9385ed05","parentId":"cd6142f6","timestamp":"2026-10-28T21:39:00.874Z","message":{"role":"toolResult","toolCallId":"call_eadc0a081774488eb0bb83932c05dca2|fc_0e05b35c94578fcb016ae26b748d748191a4cfaf6b3fee5d5f","toolName":"read","content":[{"type":"text","text":"<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n"}],"isError":false,"durationMs":1,"timestamp":1793223540873}}
+{"type":"message","id":"36b3b3e8","parentId":"9385ed05","timestamp":"2026-10-28T21:39:06.893Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting shoe-box kinematics**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0e05b35c94578fcb016ae26b776a6c8191a11f9f6e5ce53643\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting shoe-box kinematics**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mt6vnYRDQBwuQJIVH78P8WOrs8Lkjm2f3UMxpeSOs03nIaFBa86aGG-POQCENWnik760a9SjNLRP22DW7_GWqPOJuQ0RsSBErV122y0oY99XwTN0AAkE1R5wMNOkv5SoKrNcDh5ooUc_RaHM6bSI_zLKmCtiUszHvMu6wDru4Xay4pvzH3ERBgHnpJzTMDwitpnWvuTB05RWxfEzrhh8CiLvbPT8aaudh0NcOSb97pmKob0iutdMWQLfJ-dt4LB5hE75rl4HrYjwvEObRy88lFgWb4VCKa8-w6rcFF6q2HLlzisHq3xvTWo5UsoTjVl_3fuY_O7vIvZOEOVKyxIU4kxCOZ264Yp5WlIvB3GEL74BZsL34QleTkz-PKlSLLG3s2JgrpCISgtW7uiqrEbolcpvERDnrKQqX9FsEIkg9clqTWryXrgaDet3wM0GUtvxQ3ANBMHhUQDeXS7gRwdK5rDmY5O6ajm8TZu8LO92soahplmYu28fEhdCvNO7A1WUOnu5csYXLJXTqXCTaEEt-RvjOWqk9U6XUBcXMHOBpnYNw_Z8zlJtSFs7LLb2zLCioDqevJ4t8b0v_iVigjROJRKKgULpdz8B8VrxzzIaKGUF1Y_xhVt3WZOXx7Omu75SGBuSwCtHaL7jVBVvyKO5ev_m7IZxD2i1rIFoOzSXm9Ymmx0phBB-ESKNTZO3agbxTDvn07HhQrXjYWpQpCaSZkT0RrUt9dbitYr3pbcibcmfTZnV_v1Yk8nnb8i-Q-vzfoUMSTAsdux4SxKyBK74AJN_PPiIh7DDKAiUREX0u-psWaWKxewQSVoBV8DeDhR3H8fhFqM14gjsFLZHMDB7yPNKzF_n7mCnbPKxmHMhJxeIJCHLtWbO_KhdORg7w0TxFGG94jROVbvgqVENVN5dD53QrQJNhPMCqZZ4CR4hKnZJerMoAZU1Zq4rmDgYkXs8snxuAfhbFthkwp0FJ02l8nmK3FPgOCwdoxkEb94r2NDv9edwhBiLi4kksEScdfrYKP3xqik7u_8Ft7dFhV7I_Q41PDuxpplmSuybvGSFjJNBR2SshmHhiO2bqKwThZ3cxVBa5bFrs7p5K-6I5fKCWSp_FP8waxE6YOzMQiX8ZO0HXkyh2Ujyh6Dr0sIEHQ7xP3VAtAqzYibR4AIYwdweAK0zhDPNF8l4xzDOIa_4JX8GIC4Lt_PTZlW3fvi8-E4hGa8NesYxMwF_ao0T6EQys8T21d7zvUESRPT-LZbwJLizypsrVKW8nz0uJkXGQVVkWnrVxsV-KlVQ80pxXYi9yj-LzzUcP6iQ0Lczp2ulgsWBorQDI7H30XlS_RT8Cx6tnECQwWzUpMoFngJunTCcih0JXGZAq67kj4XRKm_ZdKMl9p7m3Ys9sITq0PouFsrrNUEWifOl7-PBbSPScpCcqAW_THxWEwa_dm347GcXVmBy-0XYz-62Hbczzer7IFLr06gk2LHKjoz7pg7xcvNtUZjO0dtDsJX3JS43qOjzQEpyYgm9FX8ECijfUumvL2dp1pmX4qASvNeP9kTJNLmFawcM8IoWOpMsceK0FpsHufIQsDBFjbktlwKnAAziFQxhVK1POUs0T8ytJYZoFdK-4FbpOn0yrt-cpSMa5a8fVK_utJwIxDubJ006nHzX44N2ETq6A87CJUK9R53XTqYvW85ZtsMv-RfHU-sUQ57xUSHva9l-myQcKPvw0_Gd5B3yID57HVWSQGO_jKh6FjfFCLLkWJi1rvN3hv01_RyvD88jyheSx6Ys0DIGjHupVQvg49pNs76FMHPZdjzQsCbPki73m2i4IQF6nbKGkq9KGDmYbpAsJirVYUmhxJH3kd0WkPAYA3CAeRHNp93Xv6eGDWPWeQm8SGHiUL7S-D4rmi3l4q2_ic9-I55_GtxwLzlgrES7096T0o2cEyNWdrtFhz1a55AbhyCbPV0lsiPjqpS-mU=\"}"},{"type":"toolCall","id":"call_33489e3730f749d496ec9b7b34d2fca5|fc_0e05b35c94578fcb016ae26b7ac83481919db7af8831435ab4","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py","offset":1,"limit":300}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1207,"output":84,"cacheRead":17920,"cacheWrite":0,"reasoning":40,"totalTokens":19211,"cost":{"input":0.01207,"output":0.004200000000000001,"cacheRead":0.01792,"cacheWrite":0,"total":0.03419}},"stopReason":"toolUse","timestamp":1793223540899,"responseId":"resp_0e05b35c94578fcb016ae26b753eb881919afe64f026f4f3b8","rawStopReason":"completed","durationMs":5994,"thinkingLevel":"high"}}
+{"type":"message","id":"ed5f7847","parentId":"36b3b3e8","timestamp":"2026-10-28T21:39:06.897Z","message":{"role":"toolResult","toolCallId":"call_33489e3730f749d496ec9b7b34d2fca5|fc_0e05b35c94578fcb016ae26b7ac83481919db7af8831435ab4","toolName":"read","content":[{"type":"text","text":"\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n"}],"isError":false,"durationMs":3,"timestamp":1793223546897}}
+{"type":"message","id":"9d8cbba9","parentId":"ed5f7847","timestamp":"2026-10-28T21:39:19.714Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_dd0ab53fe66841fd9221dc3616c18876|fc_0e05b35c94578fcb016ae26b82fea881919b755b5ff018d7d5","name":"exec","arguments":{"python":"import asyncio\nfrom kinex import runtime, helpers, communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'q':m.position,'fk':helpers.joint_model(d).fk(m.position),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())","description":"inspect measured state and schemas"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":349,"output":253,"cacheRead":19072,"cacheWrite":0,"reasoning":0,"totalTokens":19674,"cost":{"input":0.0034900000000000005,"output":0.01265,"cacheRead":0.019072,"cacheWrite":0,"total":0.035212}},"stopReason":"toolUse","timestamp":1793223546926,"responseId":"resp_0e05b35c94578fcb016ae26b7b60c48191b48ca1dbf5dbce1a","rawStopReason":"completed","durationMs":12788,"thinkingLevel":"high"}}
+{"type":"message","id":"7e43e20c","parentId":"9d8cbba9","timestamp":"2026-10-28T21:39:21.475Z","message":{"role":"toolResult","toolCallId":"call_dd0ab53fe66841fd9221dc3616c18876|fc_0e05b35c94578fcb016ae26b82fea881919b755b5ff018d7d5","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223561.071991926] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 14, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 8, in main\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n    q = joint_target(positions, self.joints, check_limits=False)\\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n    vector(value, len(joints), \\\"joint target\\\")\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n    raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":1760,"timestamp":1793223561475}}
+{"type":"message","id":"6b962d6d","parentId":"7e43e20c","timestamp":"2026-10-28T21:39:26.606Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ebc53b54c9144398add4c4510616f6fe|fc_0e05b35c94578fcb016ae26b8ba19881918312f9fe01c977d2","name":"exec","arguments":{"python":"import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   q=list(m.position)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(q),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())","description":"measure initial scene"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":753,"output":256,"cacheRead":19456,"cacheWrite":0,"reasoning":0,"totalTokens":20465,"cost":{"input":0.00753,"output":0.0128,"cacheRead":0.019455999999999998,"cacheWrite":0,"total":0.039786}},"stopReason":"toolUse","timestamp":1793223561505,"responseId":"resp_0e05b35c94578fcb016ae26b89dc04819192eafeda09a7f20e","rawStopReason":"completed","durationMs":5100,"thinkingLevel":"high"}}
+{"type":"message","id":"175a61e9","parentId":"6b962d6d","timestamp":"2026-10-28T21:39:29.070Z","message":{"role":"toolResult","toolCallId":"call_ebc53b54c9144398add4c4510616f6fe|fc_0e05b35c94578fcb016ae26b8ba19881918312f9fe01c977d2","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223568.172439105] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 15, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 12, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2464,"timestamp":1793223569070}}
+{"type":"message","id":"1573d9c6","parentId":"175a61e9","timestamp":"2026-10-28T21:39:31.726Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_c2b8d5d34932451c817efe51bdba851d|fc_0e05b35c94578fcb016ae26b9318f08191b9765db7a8ffc3d7","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":509,"output":39,"cacheRead":20352,"cacheWrite":0,"reasoning":0,"totalTokens":20900,"cost":{"input":0.005090000000000001,"output":0.0019500000000000001,"cacheRead":0.020352,"cacheWrite":0,"total":0.027392}},"stopReason":"toolUse","timestamp":1793223569092,"responseId":"resp_0e05b35c94578fcb016ae26b916d90819187f5621a17edd5eb","rawStopReason":"completed","durationMs":2634,"thinkingLevel":"high"}}
+{"type":"message","id":"74e73206","parentId":"1573d9c6","timestamp":"2026-10-28T21:39:31.730Z","message":{"role":"toolResult","toolCallId":"call_c2b8d5d34932451c817efe51bdba851d|fc_0e05b35c94578fcb016ae26b9318f08191b9765db7a8ffc3d7","toolName":"read","content":[{"type":"text","text":"\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n"}],"isError":false,"durationMs":2,"timestamp":1793223571729}}
+{"type":"message","id":"d7788a05","parentId":"74e73206","timestamp":"2026-10-28T21:39:37.269Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_90dc0d2cd0e74d118298a624ddad2d76|fc_0e05b35c94578fcb016ae26b967e8c81919e4629fbd557a833","name":"exec","arguments":{"python":"import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(list(m.position)),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  im,dep=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())","description":"measure initial scene"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":396,"output":250,"cacheRead":20736,"cacheWrite":0,"reasoning":0,"totalTokens":21382,"cost":{"input":0.00396,"output":0.0125,"cacheRead":0.020735999999999997,"cacheWrite":0,"total":0.037196}},"stopReason":"toolUse","timestamp":1793223571753,"responseId":"resp_0e05b35c94578fcb016ae26b942af88191a23b4618467d8798","rawStopReason":"completed","durationMs":5516,"thinkingLevel":"high"}}
+{"type":"message","id":"20d81030","parentId":"d7788a05","timestamp":"2026-10-28T21:39:39.212Z","message":{"role":"toolResult","toolCallId":"call_90dc0d2cd0e74d118298a624ddad2d76|fc_0e05b35c94578fcb016ae26b967e8c81919e4629fbd557a833","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223578.614148509] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\"fk\":[0.1056935554023861,-0.0011901315627094447,0.15999839199046592,-0.000007715996484952103,0.0000021542621098628648,-2.4666643061721644e-7,0.9999999999678809],\"tcp\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0}},\n    \"right_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\"fk\":[0.10569355567032844,-0.0011901318683733137,0.15999839080293787,-0.000007716210574917094,0.0000021565576609891396,-2.4864112524367e-7,0.9999999999678738],\"tcp\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}},\n    \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"right_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"right_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}}\n  }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":1942,"timestamp":1793223579212}}
+{"type":"message","id":"0c9864a9","parentId":"20d81030","timestamp":"2026-10-28T21:39:51.107Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Estimating shoe 3D points**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0e05b35c94578fcb016ae26b9df59881919df1efdf584f4c66\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Estimating shoe 3D points**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mukxVEV_WwTGJOtSMONVrD6qERYmMQEzEB-28fe46bXYqR_7gsWTWnaI8oaOA7bCRzznxqoSkmskmkpbRo1l2Dw2AkaOzyqwysL-ACcApzeIocD9kacb5kn1Rlbe26pSkapeIIkXaL--4FVj0PFqR2_LRlmPgh52H4HfJEovCp-RYdPM0vSF0lYI0aQkFrp4Cii9BK7ytE0uRD7j8fyYvV-32VPutRzlCzsUCapdIJ2HrGUwNi08k0aFodRElW9Q-gplnCvwM1QVeovtdOPB3U5qmxX3KPwy4SDpNkNrXtXG846n-owte_XtMIq992GEEBLs6tphdHVc9PJHBherbp-b-TGCG2uUIUrrG1UY6P226tPkr2bW98Ezgn_W2Kl56K5UPK21J5NN-CaB9oCCOtJN8MX2Jtgfaqq32k4IT0bHPkB_zMqsurqhg3RCaplaPTAeurP1BvrNmXBtheYJVFmWoTDV4fTXN2TZsxgdb5Pl55Ys0hliIMRbNWjPyLNEfQbiqJ-v56YVergFNaC53wW-tMLRNL7r6Xf9qr_Oi12LgMGvQZvu5ZUfhs881F2HKQcuPrnA50FbLMhcbzYUxt9qbnW7jirXeKl9vXUnt_QIIHvMPTgFrMzmSk2wyPxnlmTG6B2YnNU09qQL9sFahtm60Kn4jxAZj_YUgxNMkcwoBp6zirBjfQb5W3hKyLAq92xBxQnUG7NK0K_WUFXWffrnerO0yI5d70By4kR7EjA2kLhblFjdreBMfkRpsaS45i0qXLVPCoXaCAwaJOm0oso-YMqL7DRr1Y2KZKac3OyLZtWxG7WJwdWxEBDPOVzZYYX1HkW--08a0cvpwzNPh___xbP3-NwRyLGbhCtv-Yr7WqxTZVI_UwlBXonsBo91dE5nxd9jWwbAITIUZ3gNXMGJfZRMevxOsHj_e2Lw3QT1_7hHlM6CImbI3riDvXYyzzkdwgFsjRGEx4DUDq2JZof0L6zwzbN9p-oKFw7Sm8G26FFPK5G3i1CcbXYpbi4qxhzAsOTtk5ExFd1U5YBlWILxHMiEdZTBqfKvKDSGzkN2yl1Vf_r32OTRjWsq78Z8pAdMAmLqV8KM_PouhuaGgK_OWotb_9m2NGXgWWHyFk7ApwnTVjmAMNvmPjD6JBmxMzFY6xf1bjaiwwZMk0knsOyzdDXNYYLRrmCOFUtGl-sMlvtVEfRMVndC_stPg0KtCFmZ3Hk914TMNn06uws3yocIrNCT6tRCWdHddleZSoelFTGopj_h2IFLO_MSRvcm9mSj8OVTZZL0eEXrzo40M1lvcu2zTQa7tHZrtfV5SS4s2y4Z2oj6efjyz3xCLzGAxST327xJUFeatjl3Wguwi6leVOia1vBK3xKdItAqedkHZ3YwRMMWVqaRGIB8amxgAAPtDcqHrCQpMWHI4zR-YWJdviflZeG4XgvzsZJwrZH8F-ChwIc1Ag8tWjKOPIreK8wcxRCPNB5PADyKGbZa70l0fxNY1wPwDF29Wr4Kow_HI4F61CDJN74MvsRc-XJ5poadMzkytg2ACymWHZdVw_MeRtAXIRtSBPWrwO6lKS7pITEFdbcF2OeklNC13CMON9g8DfJ0oWBoRQNHhoEVMMkmODG5wRRbLoaTDvTKAmGLpc4_Dwu90wpyOe7FQfc2LQPPeJPDvvJ0nYKS3qr87xA8SYYkGHlaReMAK022g_6muvP0NSbQPXFXF5byzGkiiSiJKNZ_GbdecXevaC-UioxUZmKb41w7CNsMR9HoiQ8l0AHLVzqdLJuAQq1e_BrPz-g5BTGFUCi9VvpU6vAMQc366IXu7etBnSZRPH3lwb1sUOnfy9Xaz6H8PpHms3_NKxHmc2d5IN6unCqKQQexegqZNSyRVJup_vcRzqoJyilLUwbbpiOVli8TCyw3ZeJxsqIlOTeQmJyL-S2Ie-_R2QCRY4uXCTE9fodabd7ulWDR_t7R4a8DPe7fo46L0Anvdw2kLoi9dEey7aA2KQ3c-5WTVN9iAeuTba6nZDZJzX1oLO4aXss498Y4YNX8_WTacTqz7ExXq5zYETEWDeLP4Eliz-7q22YkOqUhXEhzWHM4bAeArtK0jlUj-Pn9cdL1fd5Buu-Tx66dglfs-0JGCAy_mf1F1qA73YKFwGFtZdvett78KEuwk2pomNMO-Ebg2L_MC2BAMG7j0aZpb2te8Ezbmh0L71AjhmfNdxWtxDoF4YAGTO1fpFWZLY0XvH9eGtdQLsNiHRn-vBkliXm0MaaXNe2B45VW9TnXiU3seeofmsFRh8rceVDBlsIhp-3Iit8p1K4Qh9D6bUhy_QCWXooZCKK_LmW8ZZDKODRaqhey3w9Ei99zM4mXPK4Uw5R7AvcnZMchsKxaLMhUrEsjtcGKB6lG9LZ82WQ3BBe6fTHQ6_rHpzTsXKkygqWAWC8t6Y2GMZTU6THkCJvo_1zjY6XsnxDETa9bOw2xIDhcn9T0_5FD9NUwXUN35rh-kZxnCOtHqqyeRRhAOMQJYTrSy5DIA==\"}"},{"type":"toolCall","id":"call_d3bf3218a40449379a6d81f7d8ad6ce3|fc_0e05b35c94578fcb016ae26ba2b72081919637c5b5562ccdd2","name":"exec","arguments":{"python":"import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nf=np.load('/tmp/head.npz');d=f['depth']*.001\nrot=R.from_quat([-.948683301192,0,0,.316227769682]).as_matrix();t=np.array([-.032,-.45,1.35])\nv,u=np.indices(d.shape);p=np.stack([(u-159.5)*d/358.64218,(v-119.5)*d/358.64218,d],axis=-1)@rot.T+t\nnp.save('/tmp/points.npy',p)\nfinish({str((u,v)):p[v,u].tolist() for u,v in [(65,124),(70,137),(78,156),(83,176),(258,109),(260,127),(264,149),(261,166),(120,145),(235,145),(111,214),(241,214),(175,195),(175,170),(45,198)]})","description":"project shoe and box points"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1854,"output":339,"cacheRead":21248,"cacheWrite":0,"reasoning":98,"totalTokens":23441,"cost":{"input":0.01854,"output":0.01695,"cacheRead":0.021248,"cacheWrite":0,"total":0.056738}},"stopReason":"toolUse","timestamp":1793223579238,"responseId":"resp_0e05b35c94578fcb016ae26b9bb61081918bbd81c0b2f29a55","rawStopReason":"completed","durationMs":11869,"thinkingLevel":"high"}}
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+{"type":"message","id":"c22adbd2","parentId":"0f2b928c","timestamp":"2026-10-28T21:40:24.192Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Refining the box mask**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0e05b35c94578fcb016ae26bc06d088191a4802d85f5e70b70\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Refining the box mask**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mvIwEo3ph_jd_Q_v9uUFbhzXOJpfKvgjNyykePedbhXad6hG9wta6_g0JUWF3T2zmI_w5xus36nbkNac3a85WhyNl3BGLDKb-jOp33QhEM6amY9JcLRrxN0vY0P26HDeqxRIHzveGPN1BRdBISV6FCVznsIHu1NKnpuv5OCFRYrZdwHtPwqHi8CyZofqGglZBZUoKBOmm9hjokbX-U83_DmjrOuLWs4ANiF9_hUuX5q1lRJvllzjlCTzMPZBGDiQZ_tG1tDwCkkVrAahAKvNJ9JTq_8ux1yt6pLa9udeThPmsBl178Ip234iJRVuExukrGsba8BuOGCpLY5IHcd4WtDij3N1whcGfKQ-u-pW-gG9lK8-gOj8ALiyMxh9-wTezZnkxNHMRYvlGqALJ9VTy3PY_3mZfF2vYXt0PPKof2pjkfRLug2WMlkHkpWzjNX3yr8q7Z_lkXQo7o0nGGopOM259FKOdDoKRkpKgGd3j1mhgamgf7IZhH6vGGXBta7loyS1n63bkj8fS1yhtiwWDRPDknAPCizsxpqLtXgCue1g3HDvb1jG_abVWryySWXAkHc_iEphZ4AV7awokQCmvKNBzECWjG77OHvsiczif-Wszm299rgk8W_6iCwIAwfp1t3DpfSe-lxRK7nTWgQjrjf3ugkZnSz9q_pjrbSRRtVhcO6H4c8_r8SMxNhqK70uENw2qH_lmyPhXOqGlbjPkUvTYEJPN2CPjFD0Uv3NJvSea9jNXxaSGpiyRl2c20BhwRrvlCaNZj1dD_sRvpdxmk8CpXRnOypn8-C-tLsrgeUjeUoQDEiKLbkzsQhY5Qo4U-hV38yOseNvbUxrhZJVaqMwpeHahCXAvRSteI5Qj7iKeVkWmBheaK9rEvWbizUUaPqIjpPkPspnYoqZ3Z5xZs7WeDWzgKAGsnX8hNGCkjznV33PEf61hcyBj8rGUkCQSwJgokxxVwIdFhsZNRWrhH-2c4PqGA1fNg2dQFp001pV1v7iXXlVGkCfRfwf6ruULbqUV0LlafJnEDU5yk9ibT2Qb1GkzUAQlQxwd2k1k50fD21e3afpmHrUhDP8POoCKjh8GH56WjigbOcLM4zYHUAW2P6_rECG5ArWNeWQrQZPFLH9C87trouvF3QnXkHuvsft6kYlZ5jezrnNOmoX3dF7tiJmuur514Sp4hX7Bplz-v8qgIj2U8CEWySp59LMMcuPxZyM_d2mER-yCR2RJpi_FG4LJGyVLGkLWmz329FdhyY5c2HVHL-z5Er6f2TZwGlh3ES_NFGBVONXzO2Qb492upm7_W0wzC7nvW2Mdt2FV90XK0PFRxPT47h5HsgG2oi56hybxUQpqESs4wGdZVbUE-7MdsKkyQAwzXQCyEt-b8nXSq4Z0_PdBaw0X_V_-9rM3o76niPRq1oTQL65386C8r9ki5GBk8stCz-BenKbHcY8zkFlTjeyl5EfeL_xT39Rw2d55M4QuSaARBuCEgdj7vjC1AcOWsv2NYlJt7r4qDOv5phy7hOJL68LMi57hg5Zv0UnyPFqqsQVMAqeff06JDo10Rn1gpAoKV4FmVylAa9xdSRb-TqrdbgoC89-cLq9xkgpdACXsnjZjD9fDz6GFqn2htrXEgavTD7FyHNytCk0OscmE5NuVheZOUTR-Kg61gpzSYzyRzFVGVMdpUDv_yULyPQXAPljLbSdNOnh0l70dsP_D1wckjjiNixz9S5R0WHGkQV2j9sLLlVG7XHwa9EEp59kZgi4HHTXWluGQg5vztYxehFVdG77luxAYdUVSkOY-58ssmViY7mt5Ne4SAvSiAPDw3AOh3x0YDRml-xYd16SwlkXzUBKQOvPVeibqGU11wUIoHrKaWnds1Kn8WRnaEB85TzPOAxxUxMnZSb8XcMZuPJsjacqM3PKMGsxkqd0ECoY-Gz3uSQtOMyoiJfvXL5GTT6B9rSVHRTy31vXV0Hb8AKME7CnEnqZMROGS9Mg93VNzdKvFfdEJ7HhYIPYb0G9MC-g0_lbygy-jNfL6QFFSCMt_gt1gRb4dswGj2w5-cJmT2cnvgMs3z4yFScd9k_ukxPCEJAfDjCbIcErM7oNwzcB7zeOnrtGESIOAgoV39LCwxsSLkgtCiM8NFs5zM4iBkbnITJyn8cs_FCTeRbGVNfJPYbPQV_cXyJDVxc3nzFxBzFyiRf83BsyHxwkD_3gdDoRxQ9pQnoGVR7cUPjXBB86XRUCtCDmhD3UZTE3AbVFWprzVrBjrkLxWPwus9D5WKKDvT7Y714rLZIKdCN2RwbmTtoHx2B4_D3ih0a4qpw3y-q7XivZ8gvl3QNDnjSRU9jzsVoec4KqE4tKLa4DrBeZYCBaA9v6nMsrPbwFTA90VDJC1J-ZtYkvRGcF4tahReH1ich9YGTpher8TGuwrxrO2wDMnurMwMxEifxZgkVgvMvreDZVAo7pJLpIs4BpOw9COzsfNgjRUSceB6clpjGB8-u9XinMSMzZheYkfxhuWmZRIxQOLq63tqw7azeCGUKyDrh7LP2CrOvltI05Kk5z_OVBg4vYFCp31Pmy6DyXO0z45WVFHLmpDk8-IobT6J8jco-FzR_TBMXY3aj14njcLU1Zh3u4YGXWJhDryn54ESIB4oeBqW6_axsyaT1WcWTlgv8IgT-0vUJweaGNp9zm5b9lkVcOSMS_U9yJmTU9q9ZzWciGfaTTx4pYQCiE9K9RhEeI9wmyx6r95rWsC7iBRYwzp9ckZhDXijbCaws-5zFWPn3clRaBjJobG-l2nxbsoXnE4NgigDANCt7KM9frm5amoDLoSywH-nhSe3j51a1jEuh23Z3YBlHIckkJ4qC5sB6WI0k5PIyjKuKVwFEK-CD1juKOZ5GaavwWFazgaf02F1uFSK6Wx_-E9EfLv9Y2EOqf7EBduCBtb8ZS1Cwf6ul_U8TYz57igmYpm_V7gq7dQ0odLnRPBh1bfaKXmQbueNDzgXGmCb3iHEgdrpiV-t8lJFpFSdFGbte67Uqsuj9cA4uosEnUgFqB1gjeZoHyqvFfCT1tflCFrJRkrZDWDKxp6tPmlcXHp6dc2fSMMWHsSdNWuVdzvQ8dGyPgXJAV-Kwhgp930xo96AdTWKlh1brXgWziwUle0wnrSVXgTtNXl1hPhjWzCnlcbCLdOWjRz4A3sB-ziCmjK2CDHrailgIqqGo_RGKLWi5rMVAy0DgyOpGMdUxa3wCM_bS4z1FZvVmpFjyRrtYgMBhroTTq79qc3PljQpc\"}"},{"type":"text","text":"I’ll grasp each shoe from above, rotate the tips left, and place the initially left shoe in the nearer row of the box.","textSignature":"{\"v\":1,\"id\":\"msg_0e05b35c94578fcb016ae26bc653408191883718d5ab5020b1\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_74b06e15e6d5437b8dbf59b863ebf7bc|fc_0e05b35c94578fcb016ae26bc810f88191bbc88266170ed223","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py","offset":1,"limit":240}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":656,"output":236,"cacheRead":24320,"cacheWrite":0,"reasoning":160,"totalTokens":25212,"cost":{"input":0.006560000000000001,"output":0.0118,"cacheRead":0.024319999999999998,"cacheWrite":0,"total":0.042679999999999996}},"stopReason":"toolUse","timestamp":1793223614575,"responseId":"resp_0e05b35c94578fcb016ae26bbeea808191bfe174322fef6669","rawStopReason":"completed","durationMs":9617,"thinkingLevel":"high"}}
+{"type":"message","id":"86de06f4","parentId":"c22adbd2","timestamp":"2026-10-28T21:40:24.196Z","message":{"role":"toolResult","toolCallId":"call_74b06e15e6d5437b8dbf59b863ebf7bc|fc_0e05b35c94578fcb016ae26bc810f88191bbc88266170ed223","toolName":"read","content":[{"type":"text","text":"\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n    \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n    Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in <device>/base; link\n    defaults to the model's tip_link.\n    \"\"\"\n\n    def __init__(self, profile, directory=None):\n        if profile.get(\"type\") != \"joint\":\n            raise ValueError(\"expected a joint device profile\")\n        model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n        if self.tree is None:\n            if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n                raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n            self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n        self.joints = urdf.commanded(self.tree)\n        self.names = [joint[\"name\"] for joint in self.joints]\n        self.tip = model[\"tip_link\"]\n\n    def chain(self, link):\n        link = link or self.tip\n        if not link:\n            raise ValueError(\"name a link; the model declares no tip_link\")\n        return Chain(self.tree, link)\n\n    def fk(self, positions, link=None):\n        \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n        q = joint_target(positions, self.joints, check_limits=False)\n        return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n    def ik(self, target, seed, link=None, **options):\n        \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n        Joints off the link's path keep their seed values. options override the\n        solver tolerances, damping, max_iterations and search budget.\n        \"\"\"\n        unknown = options.keys() - {*DEFAULTS, \"budget\"}\n        if unknown:\n            raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n        values = dict(zip(self.names, project_seed(seed, self.joints)))\n        values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n        return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n    \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n    home = profile.get(\"home\")\n    if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n        return 0.0\n    return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n    \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n    joint_target(actions[-1], joints)\n    previous = None\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n        for i, (q, joint) in enumerate(zip(target, joints)):\n            limits = joint[\"limits\"]\n            if limits is None:\n                continue\n            margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n            low, high = limits[\"min\"], limits[\"max\"]\n            if not low - margin <= q <= high + margin:\n                raise ValueError(\"target outside joint limits recovery tolerance\")\n            if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n                raise ValueError(\"joint limits recovery must move inward\")\n        previous = target\n\n\ndef command(profile, actions, joints):\n    \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n    if not isinstance(actions, list) or not actions:\n        raise ValueError(\"actions must be a nonempty sequence\")\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n    if profile[\"limits\"] == \"strict\":\n        recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n    return actions\n\n\ndef project_seed(value, joints):\n    \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n    values = vector(value, len(joints), \"IK seed\")\n    return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n            for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n    \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n    if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n        raise ValueError(\"joint state requires unique names and matching positions\")\n    positions = dict(zip(message.name, message.position))\n    names = [joint[\"name\"] for joint in joints]\n    if any(name not in positions for name in names):\n        raise ValueError(\"joint state is missing modeled joints\")\n    return joint_target([positions[name] for name in names], joints, check_limits=False)\n"}],"isError":false,"durationMs":3,"timestamp":1793223624196}}
+{"type":"message","id":"bc292a92","parentId":"86de06f4","timestamp":"2026-10-28T21:40:49.414Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Creating staged motion script**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0e05b35c94578fcb016ae26bca9d408191862210e4fa5ad8a0\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Creating staged motion script**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mvS6fqLdZmMTyYsJhATvVwebiYLckDoBzo7ScLk4Z64pqQy5f9lqbQBt5nTeP-q_VoDhwq4bPEUGFlqIoC6IUwo_WJBhwaO44XHmXOpAmTqVmvBJs3hha_fRModQGdQrTysE7WTr30aw1GsYj-hIIEVviZDseg9oj7rK8b9As_1NWFFxAuJG9EQHAN_hsWr2iwOTW5BlOUQbGwcvhkvNyRpF0nxtE2-HbHoFed0MmIn64mgvqPTq_RvGuEcd_gjAv7ggLUP32S74nrqXGg19GZ84d8iF5kmA2FjoJ7D2tJhdjhiXC8iQMzCMVektrcM2PxkDPMLWd4DjHCuv2jL4grSKbuNoXUB5RMqL__7c36p3JoWIKQgp6UDemEvMJL4B2eaHoQpMXj7uClvPDKD_z8e61A9iOxh1if0xqUUpFlthf2hLo0swvdUK2vaNlLWpuVXAYxTM61y8ioViC2HqDjMyE2cHTbz1js50tnYinOSuY6F8NuNwS49jwkI4vPnJlk2CUzc0WN08AO0ivywj9ua2s6l0rJMTTDlTnlwytqn3lBiaXGDREwlhnQk5tjE1ear9pm9kCQ21hp5malw7OOb8vNhZcCacJCdTZGr3iShQ_AEdxleeQB3w9wRzUmx1vRa4XBziTU8B59EUdsMg3ikE6F7mCodp1LKCHT5C-IV-0bsRM8Na85qG-uJDi3PLvNdrFXeGjbb0VuGtfzSte8ClmNTkpTDc23-D4j7JzPVOg3SU6iuHG8BOSnlwdgreZsEsU7J6RmtxlltJeOVXmwZXCkSowVdCnlPPT27aUYchvXXz2nkkcQ5fvFsgkC7Ix2LPgreFrQ4WrqIeRzb5RkJDn6zPWstthTIUJz29JTbHI-7aJ08j9sp4IYNVVL4hogzsBv-DDLK2Z3Z_2gkKfHkLzTEA336x32MZkdiep8MGCmjaj-JHU9f4wcax_ZkyivUDgAKe7Lbq7nOhXj2eJvnSyoufUJem5UGNlgushXWvxeJgs4kj0bUl1usj9-SO_ZoOa9piIO7LQCzx9Car_fuu_ZAUTzN2UKkNOO2wRJvkjEPhjxuXtfW_ZFysslWdH_1nnM2R8UdyB7_ylJ6WxBQ5zfGyyHquaCGU3Y_FqCs57zfL7sBfnzQ6RMFd6yeVD2iUzZuhI-8BvW3T3R_lwgBdJyF6JWFO957TiMPjF-EU3nxpIEj4Bc3Sn7NtWwQaJf2ErjtuXbtIDNy9BgM420c3zQqhvuWP5uTFwZwu7QME61CGSD3qqvWeDyMnOdIQcFsR2eCkRGR0ksRYjMuqTA-Vy9VMVLzINE1JqvcvSBNCuwH63NcGiZzT8KX_OpVyV1rJbQ3qE9LOII_R-9-b0t-4eAYIgHTIpEnJhwjLQgk5yiyE9LOxZ-_KFJAQQxGqfU8lU2gyIBXVoM-9x48eWu2J6RYHu8_ksWJ8OUP20vl6j2MHN_Z2tKOMjItlAe-b6erVpQIU147g-swgO4jUfBhJ883tCogwJesUzQE8xzmIk3fw6ObmZPobrT1ZSpMEeaLKtJxl9MR3Uvr4bHTBOOdFbu3hSgfk4RPAM2Gfq-72K9Le12rGwZPLmN8vT9QQC_Vkol_k7gis1yFd0PVNgwnKicB2_HiTisJvjpCocfiErdKuXv4xqHCWuHq3XefnK-dxOuEGps4FcnG51C_qKrqPX15vlQY7WidwhtbNexYh5FhMn3eUWBDCzaQOIPkAyFPlugAWZVQx7nUW6EoAyVk_8J_MKtZrXOkLxSQvnym4ONZ7HGIRYWL5cDNoIEd8v0zVVu9N0dNHLDs4PsA8wIQ_8a8kz1TaTsZmlT0YO4fDrZrb1pv-lfVblNXyaagvZOeoQFVWpxAr_8oVhyAQA7hZZHSnqRlDxJv-NKrxwJYplmCaTjjWVb_3BU03OoKp7xeLyVFowSYNmW1bCYkwt1kFSV-eA-i4deKZR1VfYwcmphE2oRY4o7vHunl0Ip1wWEVLlZpAY_oll0ZkY35-cXL8o3Gx_z03D1VnrfGzl3KjcvDYfji1Ir5_oRUeU3r1Hj-DU1deEw_jqxDGz21VnIipan0lA0oY06SxYy-X-N0-Bolup87k_QjsCJoH9MM0B3IFtRNukhTqSGrGbl3mckUYXmVwB-7x7YEQx9_UFm239MvmOAVqFloKpt_KdS8KJHfM6iaZYwqQkXw3qiYqHXvCD23nZH5Ul66gd_jWwGtUDxOVrS3w_i-wAF6EaCDPgjcD_pVnb2qwvC6lJS3-XWa9BWCgKVsiHzHleyWYWSb81stadUFaUcuZ8cY07XxpSsCFBHtGF_pFAKZM0NYEo5QBZoeafjmPJOLg6Sq4O3dKJkn4IFVa0Oe5WgpIUg--wH8RCofj39IBp4oOUDpTmVpHb5sAa3ru4RSdD1i1R016ORjzpO_pRtaaGlqgu-298Ce0DS5dGTf2YkoYO3HsYRWfevm7pW6W8Z6C_qBC328gyJwxKygflrkjt24X5x4JvPgZVUSLBxeNysGbd-X8pnDr1S4ar-s-mrqhch9YhiBmlVFMYBQcyWZn2kkiPkPRcOiq_ucoNGMRA9d8ylHlQMBUECOsP3M11waFcPc8viRDfzYtZOvpUZAugiM-tqTcvl7YbicZ6o6GcfbjdiY7-kQfjYMhs7BbpcGuxvcSvsYWhre7mqlj2MKxcR22nLdAl5_y6KptPUiSqt40uI8C9xKjghKlcWvb-0eXMcguYdPKPnPrzZtSpzpLRjbSvUkbOru6dJWZeTQNZ1hjqP3pjeRZLnJ9VwPAOwg5sNOx8lHlJyfGyy-QJ2KSlMDCUIPQwexyFXQ45dugANnRq4ADgh53t2DY9cEOazjIHv3dkHW3Twd0pqXBXo9bdL-oe8QG509rHqZD79RYbUTLLb6Q-qCjoK09UZPPmDUr-IdmhwbYH3xrRZvSO0DpoaOR9fX_0C1t3M5cBtXjmrp-I3ql3AhGn26MIvp7RZXVWdtjy-mBak_KqQR_LEjl6i90jfUH7O02IfxoJC-qZ8FpuezWrRjFw==\"}"},{"type":"toolCall","id":"call_0868a220b62b474994731df198d503cc|fc_0e05b35c94578fcb016ae26bd23c40819191af04b6129b3ae0","name":"write","arguments":{"path":".kinex/tools/dual_shoes.py","content":"\"\"\"Measured Cartesian dual-arm moves through public Kinex ROS operations.\nInvoke with exec file and args: targets {left/right: [x,y,z,qx,qy,qz,qw]},\nduration, settle; optional grips {left/right: opening}. World coordinates.\nA dry_run solves without publishing; each path uses measured start FK.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, finish, args\n\nasync def state(io, device):\n    async with io.subscribe(device+'.joint_state') as stream:\n        return await stream.next(timeout_s=10)\n\ndef matrix(p):\n    t=np.eye(4);t[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();t[:3,3]=p[:3];return t\n\ndef pose(t):\n    return [*t[:3,3],*Rotation.from_matrix(t[:3,:3]).as_quat()]\n\nasync def main():\n    a=args(); duration=a.get('duration',1.2); plans={}; report={}\n    async with communication() as io:\n        for side,target in a.get('targets',{}).items():\n            dev=side+'_arm'; model=helpers.joint_model(dev)\n            measured=await state(io,dev); q=list(measured.position)\n            base=matrix((await helpers.relative_pose(io,{'frame':dev+':base'},{'frame':'world:base'}))['pose'])\n            tcp=np.eye(4);tcp[0,3]=.12\n            start=base@matrix(model.fk(q))@tcp; end=matrix(target)\n            interp=Slerp([0,1],Rotation.from_matrix(np.stack([start[:3,:3],end[:3,:3]])))\n            points=[]; max_jump=0\n            for i in range(1,a.get('segments',20)+1):\n                f=i/a.get('segments',20);t=np.eye(4);t[:3,3]=start[:3,3]*(1-f)+end[:3,3]*f;t[:3,:3]=interp(f).as_matrix()\n                goal=pose(np.linalg.inv(base)@t@np.linalg.inv(tcp))\n                qnew=model.ik(goal,q);max_jump=max(max_jump,float(np.max(np.abs(np.array(qnew)-q))));q=qnew\n                ns=round(duration*f*1e9)\n                points.append({'positions':q,'time_from_start':{'sec':ns//10**9,'nanosec':ns%10**9}})\n            if max_jump>1.0: raise ValueError(f'{side}: IK discontinuity {max_jump}')\n            plans[dev]={'joint_names':model.names,'points':points}\n            report[side]={'target_q':q,'max_segment_joint_change':max_jump}\n        if not a.get('dry_run',False):\n            for dev,payload in plans.items(): await io.publish(dev+'.joint_target',payload)\n            for side,opening in a.get('grips',{}).items(): await io.publish(side+'_gripper.target',{'data':opening})\n            report['wait']=await io.request('world.wait',duration_s=duration+a.get('settle',.2))\n            for side in ['left','right']:\n                m=await state(io,side+'_arm');g=await state(io,side+'_gripper')\n                report[side]={**report.get(side,{}),'measured_q':list(m.position),'grip':list(g.position),'tcp':await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})}\n            im,dep=await helpers.camera_frame(io,'head_camera')\n            np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n            image=helpers.save_image(im)\n        else: image=None\n    finish(report,file=image)\n\nif __name__=='__main__': asyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1303,"output":1054,"cacheRead":25088,"cacheWrite":0,"reasoning":118,"totalTokens":27445,"cost":{"input":0.013030000000000002,"output":0.052700000000000004,"cacheRead":0.025088,"cacheWrite":0,"total":0.09081800000000001}},"stopReason":"toolUse","timestamp":1793223624231,"responseId":"resp_0e05b35c94578fcb016ae26bc894348191a6de9b05c309c71a","rawStopReason":"completed","durationMs":25183,"thinkingLevel":"high"}}
+{"type":"message","id":"ffb9c778","parentId":"bc292a92","timestamp":"2026-10-28T21:40:49.418Z","message":{"role":"toolResult","toolCallId":"call_0868a220b62b474994731df198d503cc|fc_0e05b35c94578fcb016ae26bd23c40819191af04b6129b3ae0","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/tools/dual_shoes.py"}],"isError":false,"durationMs":3,"timestamp":1793223649418}}
+{"type":"message","id":"f9c690f4","parentId":"ffb9c778","timestamp":"2026-10-28T21:40:54.106Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ae9204374e2d4a3cbfbeb1673bf19662|fc_0e05b35c94578fcb016ae26be3d0a88191ad5024466f984815","name":"exec","arguments":{"python":"import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nout={}\nfor s,axis in [('left',[-.435,.9005,0]),('right',[.10,.995,0]),('placed',[-1,0,0])]:\n x=np.array([0,0,-1]);z=-np.array(axis);z=z/np.linalg.norm(z);y=np.cross(z,x)\n out[s]=R.from_matrix(np.column_stack([x,y,z])).as_quat()\nfinish(out)","description":"construct grasp orientations"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":203,"output":140,"cacheRead":27264,"cacheWrite":0,"reasoning":0,"totalTokens":27607,"cost":{"input":0.00203,"output":0.007,"cacheRead":0.027264,"cacheWrite":0,"total":0.036294}},"stopReason":"toolUse","timestamp":1793223649445,"responseId":"resp_0e05b35c94578fcb016ae26be1ee308191a26a1e3c3f9d7792","rawStopReason":"completed","durationMs":4661,"thinkingLevel":"high"}}
+{"type":"message","id":"ecd0a698","parentId":"f9c690f4","timestamp":"2026-10-28T21:40:55.933Z","message":{"role":"toolResult","toolCallId":"call_ae9204374e2d4a3cbfbeb1673bf19662|fc_0e05b35c94578fcb016ae26be3d0a88191ad5024466f984815","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": [0.3758414690119471,0.5989517427731067,-0.3758414690119471,0.5989517427731066],\n    \"right\": [0.5244041261335186,0.47434197842288933,-0.5244041261335186,0.47434197842288933],\n    \"placed\": [0,0.7071067811865475,0,0.7071067811865475]\n  }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":1827,"timestamp":1793223655933}}
+{"type":"message","id":"cef1d2dd","parentId":"ecd0a698","timestamp":"2026-10-28T21:41:00.359Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_2b7ac2f9914d4c2da36300af3a602d5e|fc_0e05b35c94578fcb016ae26be9d248819188a0d3ee362a810a","name":"exec","arguments":{"file":".kinex/tools/dual_shoes.py","args":{"targets":{"left":[-0.218,-0.025,0.84,0.37584147,0.59895174,-0.37584147,0.59895174],"right":[0.195,0.016,0.84,0.52440413,0.47434198,-0.52440413,0.47434198]},"grips":{"left":1,"right":1},"duration":2,"settle":0.2},"description":"approach both shoes"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":348,"output":136,"cacheRead":27392,"cacheWrite":0,"reasoning":0,"totalTokens":27876,"cost":{"input":0.0034800000000000005,"output":0.0068000000000000005,"cacheRead":0.027392,"cacheWrite":0,"total":0.037672}},"stopReason":"toolUse","timestamp":1793223655966,"responseId":"resp_0e05b35c94578fcb016ae26be84bbc8191a1923e78fe3bec68","rawStopReason":"completed","durationMs":4393,"thinkingLevel":"high"}}
+{"type":"message","id":"8d9f2c0d","parentId":"cef1d2dd","timestamp":"2026-10-28T21:41:15.320Z","message":{"role":"toolResult","toolCallId":"call_2b7ac2f9914d4c2da36300af3a602d5e|fc_0e05b35c94578fcb016ae26be9d248819188a0d3ee362a810a","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223662.312752592] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22774460233231092,2.0844783296165446,1.6334247882592328,-1.119696352368475,0.002011036180461048,2.49168103259622],\"max_segment_joint_change\":0.22250770540570428,\"measured_q\":[-0.19359128177165985,2.0299289226531982,1.565936803817749,-1.0698388814926147,0.0637018159031868,2.444208860397339],\"grip\":[0.9992598511955955],\"tcp\":{\"pose\":[-0.22299369029360194,-0.02944199607878123,0.8439472305082514,0.3711196237801609,0.5970864937471737,-0.3314537937041148,0.6292029294823159],\"timestamp\":2.2}},\n    \"right\": {\"target_q\":[0.22891849509608184,2.2770167296021664,1.946258985399993,-1.2399737668878201,0.0038143270660531453,-2.7921574517349774],\"max_segment_joint_change\":0.2557527373667139,\"measured_q\":[0.19502022862434387,2.2170157432556152,1.8614673614501953,-1.1837477684020996,-0.06620503216981888,-2.7387287616729736],\"grip\":[1.0035958859053524],\"tcp\":{\"pose\":[0.20106956124805897,0.01130916671671306,0.8441301058365255,-0.5273385980969397,-0.4719980791625396,0.5607531031936284,-0.4297531541277429],\"timestamp\":2.2}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.2,\"waited_s\":2.2},\n    \"file\": \"/tmp/kinex-observation-vuwcfag0.png\"\n  }\n}"},{"type":"image","mimeType":"image/png","data":"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}\n}"},{"type":"image","mimeType":"image/png","data":"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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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}\n}"},{"type":"image","mimeType":"image/png","data":"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}\n}"},{"type":"image","mimeType":"image/png","data":"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}\n}"},{"type":"image","mimeType":"image/png","data":"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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"devices: 4 calls","isError":false}},"isError":false,"durationMs":89,"timestamp":1793223865261}}
+{"type":"message","id":"f987cc8b","parentId":"20d111e0","timestamp":"2026-10-28T21:45:05.154Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Refining resource memo estimates**\n\n**Recording gripper state**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0e05b35c94578fcb016ae26cbd1c0c81918256e2d03ca14b13\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Refining resource memo estimates**\"},{\"type\":\"summary_text\",\"text\":\"**Recording gripper state**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mzGEDbYxpU8BKU30oTljwjt9Jg6IHUKHRneZTLnx3NXh_sul-6hVagfMjPZ9Yx5g27iVxtmZgQFc54lqL-OkFMtUMBcM_Xkm8kecYAleHPhmyNNXjIVJV3q61GmjGkuq8S889FACTKljV6kOY6B3ve7ZVKeKYaW74kXuTLwMIRjMSkMKwg5ymo7knxB9lliRZ_13B4jDgOdp5CUl5pkoKx4Ag07szzW5FmyQjEsHpON1td9OBRvUFrfmy3HGZloW-Rpu2IZpsg6mRH1857MTdTzDI0-75Jg66y8kBBnTu64MGdQ8-tLtQQ2eyoVj5k3IFcsYKGOK4zc4upao-An_aTXLEoTpss-axp1FKSlbCaV9rlUEM30bOXX9_c-IakRkuaOtYe5SPOuFm5RuKyCtMUC3-V_xYKE3FIVP-xqQlq5SVYvlYdzN867XqfXKSr64RLZvzOuLlWsrlyydBrdkGokT8Erxe7R0JvW2uKaUIIuuhkcup4YSnTFnjSXrACWQFloXlwy0Pclc9zjI2-ixQxAlYtcebmZZB8gphpuS_IPQZoV5yQ03TvPLgLW4emswySC-8KYaW49qoGPTUO8A4XsoGADFjKqNJ1jVwUIeegoTQ1RhI64j5Pct-IYdhXd_V71uHxJz9Ej1vwrU-uuLhtmW_YTM8o9mZrUmX564rTBWUCKGaSKH75pUOX0xdKEqLOPjtOhgX57m73mHouuJ6wBio3ZKx1keE806JF_mEnKQVc3z1t6F9hAzwjFsVV0ZnGVBSmX2KUzLrS84ZaTBm9cSs9_nj3XkfGoKgyMLMHfushqrHv4ZM2AhCShtHc0pgoTO2qXi3x8mJ7chV86Ox_DXz9T1SXHHvD-FKRNKcKaIU25tYeDsSOI2y5kydRIU_r-u-o1I6cEuSVpZBfj042G1i00sViCT8jyHDFgJrw3SBR415W7bE1RfvF1jBeAnqHUin-PffgktWKdb_nN_O_X63MJlztfcPZCWzME4xJzs8E2Mu6F3Mx__oLaAewJHgK1QIaUOC5uk8w-TJZeW2qMz-cqRaVQlYIRwKgNqPjWYroAgpCvkBppzHtCG3Aocw_6SbmNDJbN8pH1yzd9agLF4MyzfaNrxf6iMq6SSxrHCDrsjDqNWKkQ-aIP_OrBYZLW9N3xZzGx4ftv2uloEganfEEX1Ytcr_9Z-gWb7Y9acGY_1-AUi3DmDcvBv3x7K_3KYgVtSbNc3TgVrs1oAb8O6Vr6t4B9MvxoAaJyRzBgpJavrWnquwCK3yyQOadU-3TPVhLpSjyZwVYSCS_3ZPLI-FtHPS8t6-42lkA332mBe3FeKILddxzPUkbOohYhIoL5rYPZNAShJ4hGk6yug34CcF1BBJ3dht2g2IXpblpdQLzafiBbOiM79epON_6qGmAsETLh9fVC16J_iU67lQ2S3rCKv3HKhGmoA44iGztqZfJnlFd_OxSFFGbeCD-yzPVcUVXCRRzBMmj1dwICZiB3oMo1uZANvFZWPGcpVAu1fCUcDlJ9zvCnAtzNTT5FWkygv1jERp9E9KV7c-DFC-d278lv5EGunQbBXy5uULSi6i45LypLMFzQ4HPKlSaHwtqFdiAqeBsIHKLhajHPNp4dd31BjJQyp0PkgWxbFiHEd2vYpVwjcW0phUNrmBabI36k-v9rrbWOtUMilXqxAb2RCGD3DzUjXOKahkC9E1zwHoWTmaPgyFIdAYOwVjeiPcVnra7ITs40dALeVnds8Pv3yE-aMyxbql7og8PkUxfK1oY40kos6ELkAax99eft4CoP-8KsNW-peSM-AQ1OT3VatOYj6juLrZHdRP-bKoLxnaTnzqXuCi2xpBRWTJF6Y5-aeKzbOVgn2gT7kTR2-uCJNA7kVwGboe-IQTy2UIvA42a0HSwfNOZvnRE95EGbxp8oJNMtwSRqfCzgMX4yFT72WjLNyL_nWqKc1qf2n7jUY9kMVC3EStWL90QLTNjSoQxbLARoSpshyFcKiI_PUXzS-Nc6JOLUe6mUY8K1ompw_rdiFPTW6-oYE8KqcC2CAil2XQvsdyH5Vf9xTcrF1SNJ1M4QvzmePc2PD8F-IBfzXoEmnYREq0qVzPPLp_CH84-W1f0MRjLWUi8fPUfKCU1zQDjM1XvZPtAm4AALan2f7LGtrhjwMA0US1Bgq17L2_BFe1qNcXdDTB1khPSUZUTLduV82MirNu-3mLEwFkM1-i7lxpUpuh8ziUms5R80ddYSNvzokR2f7D-Y109AKCh_9tjZw_KNC7yt5FkJkCpyJh3pbQjq8aAMPIx4xNRlvDJZFuGy1Dz16hQCrsbRSBi2blgMcMQSQ0P9vICD2GgADrDYUsb92OUG0vfSFS_-SXNZWVBskf1FUSDFxGqOwD02q0d3ScP10C6rAdWYzO8OdvNQlsCeIYHY276Iif1SfkJixAMS57ogoDCqLRkdhX4QPP0vwb9Gu1RvXLcwrEwJSKpRyyI-c069uZTtN7q5n-mKrM33hKY08QubHxozhuy3z4QPosctX91A0OcY1ZWq5xPS037RKAsJ78pBvLcWJ5B1ODdwiOkhsqe1Vq_KxxvdHTaK1jy8KyLoKoWUKQFbxnvQLF8w-X3qfb3P5H453TiV8ylL1TiVJ9-a2SGNh1Y_PEuIS6cekxR19u7K_iGUKH91exHb5-HOSViGpM-IZFIR4bHvlGFV-rHRxizFyVXlaCRhNZA9FQ7zPpAmGud7-T7NB3JVsu3fTx5LDERVNS2f6c4zHJE8UPRTrXom4luyLFM93sda0540_0-FaCb--mA94LIv4h5TeBdDS9DJThG-93X9f_zc10IjHQQbFGja9Vil71zNBRU93DJeqlrxtr6aSlLozOty7EJIIQ60JpBfcJ4fJ2AqD7yzuoLIm17wqTZ5h2qclOIlx86JO88OjVypjWm6NCXgI8ulJcGywS4C1t1aq-W7bD2Z7DmWF8gSH4iTr0wS5x0QBNqaib9ITqd1g0w966ms9E5pZyK0KscK6rQdIGhaKhojvKJvaqiW68JsTkExK4APcaG8hfWey7a18rHWpHuWiogyyEC9B-qtHx9ColM7M0S70FHUAKDji2L_f9kWBPTOPAXlTR2QU_K_z0b0R7iw7GFeJA4qzKY0g2ipI5ALrdDFsO7A9N7s842tjJjBptU1XXecTalQD2ZyYLXAP7C8pwOI0RiI3c29BELo5gt4nxg1dZUAEnaV0fOWb2Ri4Vfx5KLRXFi5ZeG19WPoh5NXPnlDvWL8Ng3NGFQxzx6QUJDZCuLFVL4d3k1ziJn-Y-8WQ7aO-WzQGjRtKLHyGs_M9xEwFoYq6ith0jilg6BxCYjMxiFFT08tBVYKkoWTUdXTtJcwE2Q0PiaKZFUqXoutit2Rnd7mIowyLyeq73bLDIIJTIzznsGDNRf8nTbzvITvuAKMU9TJQv9AYT38bfo5Eu_xq7ORK5eiCLoRyTu1NkJepIYITzMGhT_L1gLegrccGfSFI4JKk6qJHtgrBl_MIyUu6lVrJ_v3CZBTliratMkZhmBCu6fVaTWPSPEBlWisN7oXdoMl48P3VZleZcQFSEcruNh7zc7xqqL9jrLF1dQKWChDJLs8AIH-P3n1v5b18LUY38w5uTYbToiTw58SqBGvm8YI9jRVWow9SfR2sAcS6k0UuXVkYgRYMpe7QPB0MWbQczlSvvvc5O56K4Vn-TxDIKvxa1rrhk5b3fktHTfAigEXD1EK3gwK20kc_P2j5vlMvP3SUk1QQ-gHB780ZZDd536mWHTdonzin78zSC3zQly4ClCyBX20mROdEewpV9EfFfcQjWBomYMorkLWlZcfFVF_-mgV7CcwwmDcrxu4DB628RkdZL4GKmK9JhW9_BULxzYXay3VNw-8U2zL8o3EJ-QegCq_jxAc8x9BZfzKiLzx73DdIrkIn3kKLZURO2gBu8RgFl4UwGyvG20nCbldOzjUGTvYtLacB-L6VsRUZCukOAkxB-de_6P8uBgjYtYb_OX43VZXOY-atBj3K85O5P1PvJWaPxwl3Txum0wRGOjXFpXS2uJJ2cugePfFOPMR_gBVVO5zuAbA5dT1yylcnetFcljweQZDIVd_LTWlFOEtwS28Rb9zhuY1UsJB86Tik_cvN88xedxlZvi-MqriFMti2Rp0RYR0OxDMSXjLYCmg53lmb_-DFLj_m9IUBuvnKU5crhSCg8W251QlQr7LOFStxRTS9q4p5p-xlWVXMFiCXGhU4THriIQgVqFFNy3_5qFN-L4yXvdZg9FScoPsoX1RqyDa\"}"},{"type":"toolCall","id":"call_ce2bbe8cfcac48fa9a6cbaea14bb18f6|fc_0e05b35c94578fcb016ae26cc583608191b8162570ad00b3ce","name":"write","arguments":{"path":".kinex/memos/dual_shoes.md","content":"# RoboTwin dual shoes / ALOHA AgileX\n\nPublic interface: read harness.md and refreshed profiles each episode; load world, both arms/grippers, head_camera. Native period 0.04 s, stepped world.wait only. Use typed kinex.communication operations. Do not reset or inspect private simulator state.\n\nReusable tool: `.kinex/tools/dual_shoes.py` accepts world-frame TCP targets, duration/settle, normalized grip targets, and dry_run. Uses measured joint state, calibrated base transforms, 0.12 m flange-to-TCP translation, Cartesian interpolation/Slerp and continuous seeded IK. Publishes both trajectories before one sequential wait. Returns measured TCP/grips and camera. Read schemas before using. JointModel.fk/ik require list, not ROS array; camera_frame returns (image,depth).\n\nPerception: paired head depth mm -> optical coordinates using profile intrinsics -> world via camera mount. In observed scene table z=.640, box bottom .642, rim .740, inner x about [-.125,.130], y[-.215,-.045]. Shoes length ~.22 m, width .07 m. Re-measure positions each scene. Initial left shoe pointed roughly [-.435,.9005,0], right [.10,.995,0]. Grasp across width at center: downward TCP x axis; local z opposite shoe toe direction; local y=cross(z,x). Grasp TCP z=.695 worked, lift to .865. Both measured grips stalled ~.82-.84 and visual lift confirmed shoes attached (stall alone is not evidence).\n\nPlace tips world -X using quaternion xyzw [0,.70710678,0,.70710678]. Near row for initially left shoe: observed target TCP [.003,-.171,.795]. Far row: [.003,-.087,.795]. Grasp offsets kept soles roughly .055 below TCP. Release just above rim: gripper fingers cannot descend to floor in narrow box without contacting walls. Shoe falls flat into box. Place sequentially; keep other held shoe high/outside, then withdraw released arm up/outward while bringing other shoe above far row. Example left clearance [-.23,-.18,.94]. Waits .3-.4 s after trajectories reduce tracking error; verify actual state. Near shoe was visibly flat, toe left, before placing far shoe.\n\nWorld can terminate during a release wait before requested duration. A rejected/interrupted wait is NOT proof of success. Stop and inspect final images/state; do not keep advancing terminal world. Observed terminal at t=13.48 during second release (337 frames): image showed both shoes flat inside box with tips left and initially left shoe nearer robot. Open commands had been sent to both; final openings left .9983 and right .9529. Native verdict is owner-only; do not claim it. Tool currently surfaces rejected wait as error, so final observation requires separate device reads.\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":519,"output":839,"cacheRead":35584,"cacheWrite":0,"reasoning":200,"totalTokens":36942,"cost":{"input":0.00519,"output":0.04195,"cacheRead":0.035584,"cacheWrite":0,"total":0.08272399999999999}},"stopReason":"toolUse","timestamp":1793223865289,"responseId":"resp_0e05b35c94578fcb016ae26cb9cfb88191a9a340a418969cc4","rawStopReason":"completed","durationMs":39865,"thinkingLevel":"high"}}
+{"type":"message","id":"3184d722","parentId":"f987cc8b","timestamp":"2026-10-28T21:45:05.157Z","message":{"role":"toolResult","toolCallId":"call_ce2bbe8cfcac48fa9a6cbaea14bb18f6|fc_0e05b35c94578fcb016ae26cc583608191b8162570ad00b3ce","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/memos/dual_shoes.md"}],"isError":false,"durationMs":2,"timestamp":1793223905157}}
+{"type":"message","id":"6e5bebe3","parentId":"3184d722","timestamp":"2026-10-28T21:45:09.896Z","message":{"role":"assistant","content":[{"type":"text","text":"Both shoes appear flat in the box, tips pointing left, with the initially left shoe nearer the robot. Both grippers were commanded open. The episode ended during final release; the native verdict remains unconfirmed.","textSignature":"{\"v\":1,\"id\":\"msg_0e05b35c94578fcb016ae26ce4b7d48191859c9b4ca65ee66a\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":229,"output":46,"cacheRead":36736,"cacheWrite":0,"reasoning":0,"totalTokens":37011,"cost":{"input":0.0022900000000000004,"output":0.0023,"cacheRead":0.036736,"cacheWrite":0,"total":0.041326}},"stopReason":"stop","timestamp":1793223905179,"responseId":"resp_0e05b35c94578fcb016ae26ce1ba908191aad05378484e50b8","rawStopReason":"completed","durationMs":4717,"thinkingLevel":"high"}}
+{"type":"custom","customType":"kinex-active-time","data":{"milliseconds":402975.86121500004},"id":"c3115fc9","parentId":"6e5bebe3","timestamp":"2026-10-28T21:45:10.052Z"}
diff --git a/experiments/robotwin-native/kinex-2/terminal.jpg b/experiments/robotwin-native/kinex-2/terminal.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c4685cb19522bf6ad3d0d7ca1070d931b1a26f3e
Binary files /dev/null and b/experiments/robotwin-native/kinex-2/terminal.jpg differ
diff --git a/experiments/robotwin-native/kinex-2/tool-calls.json b/experiments/robotwin-native/kinex-2/tool-calls.json
new file mode 100644
index 0000000000000000000000000000000000000000..0035dde08c85aa9247482ba59f961a59e65fcf3d
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/tool-calls.json
@@ -0,0 +1,620 @@
+[
+  {
+    "name": "read",
+    "input": {
+      "path": "harness.md",
+      "offset": null,
+      "limit": null
+    },
+    "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n",
+    "call_id": "call_367e99d2533843ab8b2927f77681118b|fc_0e05b35c94578fcb016ae26b55808c819199906d0ce7db8551",
+    "is_error": false
+  },
+  {
+    "name": "ls",
+    "input": {
+      "path": ".kinex"
+    },
+    "output": "devices/\nkinex.json\nmemos/\npython/\nskills/\ntools/\nworkspace.yaml",
+    "call_id": "call_7ce3085100fd420dbb7436140ec43e28|fc_0e05b35c94578fcb016ae26b57d6788191b2062cc3c6a54dd6",
+    "is_error": false
+  },
+  {
+    "name": "bash",
+    "input": {
+      "command": "find .kinex/devices .kinex/tools .kinex/skills .kinex/memos -maxdepth 3 -type f",
+      "timeout": null
+    },
+    "output": ".kinex/devices/left_arm/links.py\n.kinex/devices/left_arm/arm.urdf\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/links.py\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/left_gripper/gripper.urdf\n.kinex/devices/right_arm/links.py\n.kinex/devices/right_arm/arm.urdf\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/links.py\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/right_gripper/gripper.urdf\n.kinex/devices/world/profile.yaml\n",
+    "call_id": "call_267b6ee82c144a20a1f2c3245704c3a6|fc_0e05b35c94578fcb016ae26b5a73808191abbc02c144259293",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/world/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n",
+    "call_id": "call_a523247bf39b433e98fc113a09b276d5|fc_0e05b35c94578fcb016ae26b5cb7f881919f83c987b965d722",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_arm/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n",
+    "call_id": "call_4cceb880d6784989bd7c305d425d4dfa|fc_0e05b35c94578fcb016ae26b5f6d248191a72c4e3a2ad19716",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/right_arm/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n",
+    "call_id": "call_0ac1df91f68e4f75a427082873c0a21e|fc_0e05b35c94578fcb016ae26b615f4c8191b9d12716aaffa541",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_gripper/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "call_id": "call_e23d1a1ce4e3491598e87028dbbe8b60|fc_0e05b35c94578fcb016ae26b615f4c8191b6a76c2b250df9eb",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/right_gripper/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "call_id": "call_57217fff6fb842a2ba2ebf0be8d60587|fc_0e05b35c94578fcb016ae26b615f4c81918138bbfb49b2064e",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/head_camera/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n",
+    "call_id": "call_cea94425e2fa4e5f85a2ed68057a9bdd|fc_0e05b35c94578fcb016ae26b615f4c8191a3b10ce3816ee991",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/workspace.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n",
+    "call_id": "call_e6b73f775a6747bd9a935378ace8ee6f|fc_0e05b35c94578fcb016ae26b615f4c8191b0a29903d0af9be0",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "from kinex import runtime,finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})",
+      "description": "inspect operation contracts"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n  }\n}",
+    "call_id": "call_400056f3564b4e9c9ea3a21b816e7625|fc_0e05b35c94578fcb016ae26b63222481918bcd825a71e9a64e",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "world",
+          "action": "load"
+        },
+        {
+          "deviceID": "left_arm",
+          "action": "load"
+        },
+        {
+          "deviceID": "right_arm",
+          "action": "load"
+        },
+        {
+          "deviceID": "left_gripper",
+          "action": "load"
+        },
+        {
+          "deviceID": "right_gripper",
+          "action": "load"
+        },
+        {
+          "deviceID": "head_camera",
+          "action": "load"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}",
+    "call_id": "call_62f70586a5e84cba9a1d9fbb8a895ecf|fc_0e05b35c94578fcb016ae26b68fbec8191ba256eb599d22906",
+    "is_error": false
+  },
+  {
+    "name": "device",
+    "input": {
+      "deviceID": "head_camera",
+      "action": "image"
+    },
+    "output": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-s12ft6c7.png\"\n}",
+    "call_id": "call_f7e1095205114cd39ccc99aad24386ff|fc_0e05b35c94578fcb016ae26b7351348191b04857fa875c4726",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt",
+      "offset": null,
+      "limit": null
+    },
+    "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n",
+    "call_id": "call_4f112267e1f54522ac8278187813134c|fc_0e05b35c94578fcb016ae26b748d7481918c67e322c71a25f4",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_gripper/gripper.urdf",
+      "offset": null,
+      "limit": null
+    },
+    "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n",
+    "call_id": "call_eadc0a081774488eb0bb83932c05dca2|fc_0e05b35c94578fcb016ae26b748d748191a4cfaf6b3fee5d5f",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py",
+      "offset": 1,
+      "limit": 300
+    },
+    "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n",
+    "call_id": "call_33489e3730f749d496ec9b7b34d2fca5|fc_0e05b35c94578fcb016ae26b7ac83481919db7af8831435ab4",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import asyncio\nfrom kinex import runtime, helpers, communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'q':m.position,'fk':helpers.joint_model(d).fk(m.position),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())",
+      "description": "inspect measured state and schemas"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223561.071991926] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 14, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 8, in main\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n    q = joint_target(positions, self.joints, check_limits=False)\\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n    vector(value, len(joints), \\\"joint target\\\")\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n    raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}",
+    "call_id": "call_dd0ab53fe66841fd9221dc3616c18876|fc_0e05b35c94578fcb016ae26b82fea881919b755b5ff018d7d5",
+    "is_error": true
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   q=list(m.position)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(q),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())",
+      "description": "measure initial scene"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223568.172439105] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 15, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 12, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}",
+    "call_id": "call_ebc53b54c9144398add4c4510616f6fe|fc_0e05b35c94578fcb016ae26b8ba19881918312f9fe01c977d2",
+    "is_error": true
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py",
+      "offset": 1,
+      "limit": 220
+    },
+    "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n",
+    "call_id": "call_c2b8d5d34932451c817efe51bdba851d|fc_0e05b35c94578fcb016ae26b9318f08191b9765db7a8ffc3d7",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(list(m.position)),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  im,dep=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())",
+      "description": "measure initial scene"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223578.614148509] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\"fk\":[0.1056935554023861,-0.0011901315627094447,0.15999839199046592,-0.000007715996484952103,0.0000021542621098628648,-2.4666643061721644e-7,0.9999999999678809],\"tcp\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0}},\n    \"right_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\"fk\":[0.10569355567032844,-0.0011901318683733137,0.15999839080293787,-0.000007716210574917094,0.0000021565576609891396,-2.4864112524367e-7,0.9999999999678738],\"tcp\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}},\n    \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"right_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"right_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}}\n  }\n}",
+    "call_id": "call_90dc0d2cd0e74d118298a624ddad2d76|fc_0e05b35c94578fcb016ae26b967e8c81919e4629fbd557a833",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nf=np.load('/tmp/head.npz');d=f['depth']*.001\nrot=R.from_quat([-.948683301192,0,0,.316227769682]).as_matrix();t=np.array([-.032,-.45,1.35])\nv,u=np.indices(d.shape);p=np.stack([(u-159.5)*d/358.64218,(v-119.5)*d/358.64218,d],axis=-1)@rot.T+t\nnp.save('/tmp/points.npy',p)\nfinish({str((u,v)):p[v,u].tolist() for u,v in [(65,124),(70,137),(78,156),(83,176),(258,109),(260,127),(264,149),(261,166),(120,145),(235,145),(111,214),(241,214),(175,195),(175,170),(45,198)]})",
+      "description": "project shoe and box points"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(65, 124)\": [-0.24701096162197103,0.03140910949709719,0.6910568320592497],\n    \"(70, 137)\": [-0.21991292201045629,-0.027594196989208564,0.7255543520299529],\n    \"(78, 156)\": [-0.20947912417886824,-0.044987609248771654,0.6775092925698912],\n    \"(83, 176)\": [-0.183232908521803,-0.11395591142723871,0.7157830657360426],\n    \"(258, 109)\": [0.19705560076620107,0.06993367883975005,0.6974502592814215],\n    \"(260, 127)\": [0.1815303772690653,-0.005548079149456486,0.7308389405389644],\n    \"(264, 149)\": [0.20284967663312783,-0.01943781830802399,0.6654216358573333],\n    \"(261, 166)\": [0.18054192688656984,-0.07729713643164932,0.6907771470716708],\n    \"(120, 145)\": [-0.11416267255569326,-0.0448333771340384,0.721374966820128],\n    \"(235, 145)\": [0.12504510830265422,-0.0448333771340384,0.721374966820128],\n    \"(111, 214)\": [-0.1258512028897438,-0.1798917686242506,0.6850811723963807],\n    \"(241, 214)\": [0.1259359683794026,-0.17950256367990514,0.6841230761030037],\n    \"(175, 195)\": [0.001018982875912705,-0.12026751685270726,0.6422993613618274],\n    \"(175, 170)\": [0.002574851178966185,-0.060117673106498126,0.6424117444706855],\n    \"(45, 198)\": [-0.2752759024607758,-0.12622992376337028,0.6403275561418671]\n  }\n}",
+    "call_id": "call_d3bf3218a40449379a6d81f7d8ad6ce3|fc_0e05b35c94578fcb016ae26ba2b72081919637c5b5562ccdd2",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import numpy as np\nfrom kinex import finish\np=np.load('/tmp/points.npy');v,u=np.indices(p.shape[:2]);out={}\nfor name,mask in [('left',(u>45)&(u<106)&(v>105)&(v<194)),('right',(u>239)&(u<287)&(v>90)&(v<180))]:\n sel=p[mask&(p[:,:,2]>.653)&(p[:,:,2]<.78)]\n xy=sel[:,:2];cov=np.cov(xy.T);w,V=np.linalg.eigh(cov);axis=V[:,-1];axis*=np.sign(axis[1]);proj=xy@axis;perp=np.array([axis[1],-axis[0]])\n out[name]={'bounds':np.percentile(sel,[0,5,50,95,100],axis=0),'axis':axis,'along':np.percentile(proj,[0,50,100]),'across':np.percentile(xy@perp,[0,50,100])}\nfinish(out)",
+      "description": "estimate shoe geometry"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": {\"bounds\":[[-0.27703949870034816,-0.11849672931365157,0.6530756384513174],[-0.26232817835314304,-0.11589681011362746,0.6648956826818977],[-0.2084795206185731,-0.048453607301866264,0.6989171661594558],[-0.16517033707524315,0.04587652017197147,0.7280738549115844],[-0.15957980670316027,0.0673668820840671,0.7397426331669105]],\"axis\":[-0.4348054796080431,0.9005243999486185],\"along\":[-0.02822664134255312,0.04733788568632173,0.17347461851403906],\"across\":[-0.2438903331252665,-0.20888409446241507,-0.17148698275945262]},\n    \"right\": {\"bounds\":[[0.12579376536245684,-0.12617690826397737,0.6530134103302274],[0.16420628560756578,-0.07877877371633266,0.6623519701729963],[0.19117578484493938,-0.005752951117641736,0.69816639561117],[0.22156610312819316,0.09690986291317003,0.7299211106280531],[0.23969001705265122,0.12021601950746491,0.7417706810964337]],\"axis\":[0.16462025977367772,0.9863570195786345],\"along\":[-0.10348059217440107,0.02641075351907217,0.15416704938916012],\"across\":[0.13832065240589805,0.18958687182277106,0.2274015727704394]}\n  }\n}",
+    "call_id": "call_359440a490204ab5b768a4249857ef22|fc_0e05b35c94578fcb016ae26bb92b4c8191aff65d64307ea8e1",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/lib/joints.py",
+      "offset": 1,
+      "limit": 240
+    },
+    "output": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n    \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n    Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in <device>/base; link\n    defaults to the model's tip_link.\n    \"\"\"\n\n    def __init__(self, profile, directory=None):\n        if profile.get(\"type\") != \"joint\":\n            raise ValueError(\"expected a joint device profile\")\n        model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n        if self.tree is None:\n            if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n                raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n            self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n        self.joints = urdf.commanded(self.tree)\n        self.names = [joint[\"name\"] for joint in self.joints]\n        self.tip = model[\"tip_link\"]\n\n    def chain(self, link):\n        link = link or self.tip\n        if not link:\n            raise ValueError(\"name a link; the model declares no tip_link\")\n        return Chain(self.tree, link)\n\n    def fk(self, positions, link=None):\n        \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n        q = joint_target(positions, self.joints, check_limits=False)\n        return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n    def ik(self, target, seed, link=None, **options):\n        \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n        Joints off the link's path keep their seed values. options override the\n        solver tolerances, damping, max_iterations and search budget.\n        \"\"\"\n        unknown = options.keys() - {*DEFAULTS, \"budget\"}\n        if unknown:\n            raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n        values = dict(zip(self.names, project_seed(seed, self.joints)))\n        values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n        return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n    \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n    home = profile.get(\"home\")\n    if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n        return 0.0\n    return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n    \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n    joint_target(actions[-1], joints)\n    previous = None\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n        for i, (q, joint) in enumerate(zip(target, joints)):\n            limits = joint[\"limits\"]\n            if limits is None:\n                continue\n            margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n            low, high = limits[\"min\"], limits[\"max\"]\n            if not low - margin <= q <= high + margin:\n                raise ValueError(\"target outside joint limits recovery tolerance\")\n            if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n                raise ValueError(\"joint limits recovery must move inward\")\n        previous = target\n\n\ndef command(profile, actions, joints):\n    \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n    if not isinstance(actions, list) or not actions:\n        raise ValueError(\"actions must be a nonempty sequence\")\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n    if profile[\"limits\"] == \"strict\":\n        recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n    return actions\n\n\ndef project_seed(value, joints):\n    \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n    values = vector(value, len(joints), \"IK seed\")\n    return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n            for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n    \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n    if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n        raise ValueError(\"joint state requires unique names and matching positions\")\n    positions = dict(zip(message.name, message.position))\n    names = [joint[\"name\"] for joint in joints]\n    if any(name not in positions for name in names):\n        raise ValueError(\"joint state is missing modeled joints\")\n    return joint_target([positions[name] for name in names], joints, check_limits=False)\n",
+    "call_id": "call_74b06e15e6d5437b8dbf59b863ebf7bc|fc_0e05b35c94578fcb016ae26bc810f88191bbc88266170ed223",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/tools/dual_shoes.py",
+      "content": "\"\"\"Measured Cartesian dual-arm moves through public Kinex ROS operations.\nInvoke with exec file and args: targets {left/right: [x,y,z,qx,qy,qz,qw]},\nduration, settle; optional grips {left/right: opening}. World coordinates.\nA dry_run solves without publishing; each path uses measured start FK.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, finish, args\n\nasync def state(io, device):\n    async with io.subscribe(device+'.joint_state') as stream:\n        return await stream.next(timeout_s=10)\n\ndef matrix(p):\n    t=np.eye(4);t[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();t[:3,3]=p[:3];return t\n\ndef pose(t):\n    return [*t[:3,3],*Rotation.from_matrix(t[:3,:3]).as_quat()]\n\nasync def main():\n    a=args(); duration=a.get('duration',1.2); plans={}; report={}\n    async with communication() as io:\n        for side,target in a.get('targets',{}).items():\n            dev=side+'_arm'; model=helpers.joint_model(dev)\n            measured=await state(io,dev); q=list(measured.position)\n            base=matrix((await helpers.relative_pose(io,{'frame':dev+':base'},{'frame':'world:base'}))['pose'])\n            tcp=np.eye(4);tcp[0,3]=.12\n            start=base@matrix(model.fk(q))@tcp; end=matrix(target)\n            interp=Slerp([0,1],Rotation.from_matrix(np.stack([start[:3,:3],end[:3,:3]])))\n            points=[]; max_jump=0\n            for i in range(1,a.get('segments',20)+1):\n                f=i/a.get('segments',20);t=np.eye(4);t[:3,3]=start[:3,3]*(1-f)+end[:3,3]*f;t[:3,:3]=interp(f).as_matrix()\n                goal=pose(np.linalg.inv(base)@t@np.linalg.inv(tcp))\n                qnew=model.ik(goal,q);max_jump=max(max_jump,float(np.max(np.abs(np.array(qnew)-q))));q=qnew\n                ns=round(duration*f*1e9)\n                points.append({'positions':q,'time_from_start':{'sec':ns//10**9,'nanosec':ns%10**9}})\n            if max_jump>1.0: raise ValueError(f'{side}: IK discontinuity {max_jump}')\n            plans[dev]={'joint_names':model.names,'points':points}\n            report[side]={'target_q':q,'max_segment_joint_change':max_jump}\n        if not a.get('dry_run',False):\n            for dev,payload in plans.items(): await io.publish(dev+'.joint_target',payload)\n            for side,opening in a.get('grips',{}).items(): await io.publish(side+'_gripper.target',{'data':opening})\n            report['wait']=await io.request('world.wait',duration_s=duration+a.get('settle',.2))\n            for side in ['left','right']:\n                m=await state(io,side+'_arm');g=await state(io,side+'_gripper')\n                report[side]={**report.get(side,{}),'measured_q':list(m.position),'grip':list(g.position),'tcp':await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})}\n            im,dep=await helpers.camera_frame(io,'head_camera')\n            np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n            image=helpers.save_image(im)\n        else: image=None\n    finish(report,file=image)\n\nif __name__=='__main__': asyncio.run(main())\n"
+    },
+    "output": "Successfully wrote to .kinex/tools/dual_shoes.py",
+    "call_id": "call_0868a220b62b474994731df198d503cc|fc_0e05b35c94578fcb016ae26bd23c40819191af04b6129b3ae0",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nout={}\nfor s,axis in [('left',[-.435,.9005,0]),('right',[.10,.995,0]),('placed',[-1,0,0])]:\n x=np.array([0,0,-1]);z=-np.array(axis);z=z/np.linalg.norm(z);y=np.cross(z,x)\n out[s]=R.from_matrix(np.column_stack([x,y,z])).as_quat()\nfinish(out)",
+      "description": "construct grasp orientations"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": [0.3758414690119471,0.5989517427731067,-0.3758414690119471,0.5989517427731066],\n    \"right\": [0.5244041261335186,0.47434197842288933,-0.5244041261335186,0.47434197842288933],\n    \"placed\": [0,0.7071067811865475,0,0.7071067811865475]\n  }\n}",
+    "call_id": "call_ae9204374e2d4a3cbfbeb1673bf19662|fc_0e05b35c94578fcb016ae26be3d0a88191ad5024466f984815",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            -0.218,
+            -0.025,
+            0.84,
+            0.37584147,
+            0.59895174,
+            -0.37584147,
+            0.59895174
+          ],
+          "right": [
+            0.195,
+            0.016,
+            0.84,
+            0.52440413,
+            0.47434198,
+            -0.52440413,
+            0.47434198
+          ]
+        },
+        "grips": {
+          "left": 1,
+          "right": 1
+        },
+        "duration": 2,
+        "settle": 0.2
+      },
+      "description": "approach both shoes"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223662.312752592] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22774460233231092,2.0844783296165446,1.6334247882592328,-1.119696352368475,0.002011036180461048,2.49168103259622],\"max_segment_joint_change\":0.22250770540570428,\"measured_q\":[-0.19359128177165985,2.0299289226531982,1.565936803817749,-1.0698388814926147,0.0637018159031868,2.444208860397339],\"grip\":[0.9992598511955955],\"tcp\":{\"pose\":[-0.22299369029360194,-0.02944199607878123,0.8439472305082514,0.3711196237801609,0.5970864937471737,-0.3314537937041148,0.6292029294823159],\"timestamp\":2.2}},\n    \"right\": {\"target_q\":[0.22891849509608184,2.2770167296021664,1.946258985399993,-1.2399737668878201,0.0038143270660531453,-2.7921574517349774],\"max_segment_joint_change\":0.2557527373667139,\"measured_q\":[0.19502022862434387,2.2170157432556152,1.8614673614501953,-1.1837477684020996,-0.06620503216981888,-2.7387287616729736],\"grip\":[1.0035958859053524],\"tcp\":{\"pose\":[0.20106956124805897,0.01130916671671306,0.8441301058365255,-0.5273385980969397,-0.4719980791625396,0.5607531031936284,-0.4297531541277429],\"timestamp\":2.2}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.2,\"waited_s\":2.2},\n    \"file\": \"/tmp/kinex-observation-vuwcfag0.png\"\n  }\n}",
+    "call_id": "call_2b7ac2f9914d4c2da36300af3a602d5e|fc_0e05b35c94578fcb016ae26be9d248819188a0d3ee362a810a",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            -0.218,
+            -0.025,
+            0.695,
+            0.37584147,
+            0.59895174,
+            -0.37584147,
+            0.59895174
+          ],
+          "right": [
+            0.195,
+            0.016,
+            0.695,
+            0.52440413,
+            0.47434198,
+            -0.52440413,
+            0.47434198
+          ]
+        },
+        "duration": 1.3,
+        "settle": 0.4
+      },
+      "description": "lower to shoe grasp height"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223682.804429596] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22587400015586576,2.3274100314457447,1.4029364137932692,-0.6462568482762985,0.004351146139304931,2.4966583272771543],\"max_segment_joint_change\":0.023721534514010267,\"measured_q\":[-0.2192988246679306,2.315551280975342,1.404956340789795,-0.6554389595985413,0.006320989690721035,2.4952962398529053],\"grip\":[1.000401415608146],\"tcp\":{\"pose\":[-0.22011327724616148,-0.023654303271224808,0.6998869872945025,0.3753037660380011,0.6003254223692802,-0.37166967146596513,0.6005148838848378],\"timestamp\":3.92}},\n    \"right\": {\"target_q\":[0.23063784230102252,2.4714411321064156,1.6872846072585452,-0.7865804977321514,0.006038718669629817,-2.7882938737519463],\"max_segment_joint_change\":0.023468032426248397,\"measured_q\":[0.22381655871868134,2.4601662158966064,1.689003586769104,-0.7951595187187195,0.0037091674748808146,-2.786982297897339],\"grip\":[1.000115448778326],\"tcp\":{\"pose\":[0.19746808623661757,0.017369893443527185,0.7001516902630236,0.526383385692651,0.47392899178612075,-0.526300156869011,0.470446582399094],\"timestamp\":3.92}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":3.92,\"waited_s\":1.7199999999999998},\n    \"file\": \"/tmp/kinex-observation-49lcysgb.png\"\n  }\n}",
+    "call_id": "call_0511f7ad7f06497797e36e204cd4d242|fc_0e05b35c94578fcb016ae26c0083dc8191ac840fb8534c16d8",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "grips": {
+          "left": 0,
+          "right": 0
+        },
+        "duration": 0.6,
+        "settle": 0
+      },
+      "description": "grasp both shoes"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223699.319257784] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":4.5200000000000005,\"waited_s\":0.6000000000000005},\n    \"left\": {\"measured_q\":[-0.2207689732313156,2.3197131156921387,1.4022727012634277,-0.646481454372406,0.004202340263873339,2.4976632595062256],\"grip\":[0.8248065384951505],\"tcp\":{\"pose\":[-0.21981260859167018,-0.023525114497031374,0.6977895497680353,0.3768516763506197,0.600604329088065,-0.37237554199764294,0.5988269446462514],\"timestamp\":4.52}},\n    \"right\": {\"measured_q\":[0.2260061502456665,2.4656732082366943,1.6879534721374512,-0.7873106598854065,0.006010189186781645,-2.786764621734619],\"grip\":[0.8215297054160725],\"tcp\":{\"pose\":[0.19675509976374933,0.01755103700765198,0.6975930297370944,0.5268113232251944,0.4745009262840679,-0.5249098951839438,0.47094405465539396],\"timestamp\":4.52}},\n    \"file\": \"/tmp/kinex-observation-eib75mac.png\"\n  }\n}",
+    "call_id": "call_449e5a31bc6546f5ae60a615b4bf5beb|fc_0e05b35c94578fcb016ae26c104f948191ba0d2851f900e1bb",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            -0.218,
+            -0.025,
+            0.865,
+            0.37584147,
+            0.59895174,
+            -0.37584147,
+            0.59895174
+          ],
+          "right": [
+            0.195,
+            0.016,
+            0.865,
+            0.52440413,
+            0.47434198,
+            -0.52440413,
+            0.47434198
+          ]
+        },
+        "duration": 1.4,
+        "settle": 0.3
+      },
+      "description": "lift and verify both grasps"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223713.308781947] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22844013591941273,2.0678736313216377,1.7085401660146515,-1.2114074372635,0.0019312142797508374,2.4907547833771906],\"max_segment_joint_change\":0.031346100891458395,\"measured_q\":[-0.2239367812871933,2.0769543647766113,1.692858338356018,-1.19147789478302,0.0024510447401553392,2.492839813232422],\"grip\":[0.8405653969808059],\"tcp\":{\"pose\":[-0.2196101692368914,-0.02448676045971626,0.8576266700337278,0.3735575027731245,0.598660183425509,-0.3766380207818991,0.6001704576237794],\"timestamp\":6.24}},\n    \"right\": {\"target_q\":[0.22832395150517062,2.271398028168711,2.0359137374683787,-1.334673049706994,0.003974610351548502,-2.7930490273624735],\"max_segment_joint_change\":0.03253001686157586,\"measured_q\":[0.22686776518821716,2.278221845626831,2.0181984901428223,-1.3149802684783936,0.004381020087748766,-2.79101824760437],\"grip\":[0.8270359554074027],\"tcp\":{\"pose\":[0.19568852418996996,0.016476706520842377,0.857661751844948,-0.5246346404832504,-0.4720684139275534,0.5257000886763412,-0.474920333679894],\"timestamp\":6.24}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.24,\"waited_s\":1.7199999999999998},\n    \"file\": \"/tmp/kinex-observation-chiiyo5r.png\"\n  }\n}",
+    "call_id": "call_2bc836be3239418799c46678e90f3a39|fc_0e05b35c94578fcb016ae26c1f024081919edebb58865181ee",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "python": "import numpy as np\nfrom kinex import finish\np=np.load('/tmp/points.npy');finish({str((u,v)):p[v,u].tolist() for u,v in [(175,140),(175,145),(175,155),(175,163),(175,210),(175,218),(175,221),(175,225),(108,200),(245,200)]})",
+      "description": "measure box rim and interior"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(175, 140)\": [-0.00040722973521965106,-0.044827186153967746,0.7401296101250818],\n    \"(175, 145)\": [0.00024104872438596853,-0.0448333771340384,0.721374966820128],\n    \"(175, 155)\": [0.001624042771544619,-0.04480792020862767,0.6813940593653645],\n    \"(175, 163)\": [0.0027909439988347273,-0.04511127723001751,0.6474165414712326],\n    \"(175, 210)\": [0.00015461159643855027,-0.1537931500053472,0.6421551363302781],\n    \"(175, 218)\": [-0.00027757404329852714,-0.17087271682999766,0.6418454611257971],\n    \"(175, 221)\": [-0.004858741824511546,-0.21538516883598618,0.7409611222694521],\n    \"(175, 225)\": [-0.004426556184774468,-0.2173418458088874,0.7269936144780211],\n    \"(108, 200)\": [-0.12591254536764196,-0.17503626728740773,0.7387227986229724],\n    \"(245, 200)\": [0.12582025415973105,-0.17167279655086232,0.731245401664232]\n  }\n}",
+    "call_id": "call_0b538cf057c14c61bac97c1cfb0cd058|fc_0e05b35c94578fcb016ae26c428a3c8191b797015760f0ed6f",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            0.003,
+            -0.173,
+            0.83,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ],
+          "right": [
+            0.2,
+            0.016,
+            0.94,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ]
+        },
+        "duration": 2,
+        "settle": 0.4
+      },
+      "description": "rotate tips left and position near shoe"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223776.430757245] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.9123295133525193,2.040620894932573,1.526702598322709,-1.0568256105508256,0.0017302909673467626,0.6856940754923477],\"max_segment_joint_change\":0.09190104728563075,\"measured_q\":[-0.8980924487113953,2.0321245193481445,1.51948082447052,-1.0570282936096191,0.0017319483449682593,0.7115294933319092],\"grip\":[0.8391735607927496],\"tcp\":{\"pose\":[-0.0018207208108431128,-0.16987044001734503,0.8313141069326292,0.004367543172538346,0.7067911316088494,-0.003909370937005834,0.7073980051319024],\"timestamp\":8.64}},\n    \"right\": {\"target_q\":[0.2149396947228276,2.3495298995565355,2.472670536500331,-1.6938670996952807,0.004595068148560996,-4.476247787375465],\"max_segment_joint_change\":0.0844328656620017,\"measured_q\":[0.22082558274269104,2.3440868854522705,2.461483955383301,-1.6856166124343872,0.004535673651844263,-4.451779842376709],\"grip\":[0.8248719681393016],\"tcp\":{\"pose\":[0.197473170754076,0.015326894137119296,0.9395929366625656,0.007442272502197977,0.7072963359824993,-0.005721082827813494,0.7068548471199813],\"timestamp\":8.64}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.64,\"waited_s\":2.4000000000000004},\n    \"file\": \"/tmp/kinex-observation-h5dm5cs9.png\"\n  }\n}",
+    "call_id": "call_3d16781764cf4a0c98cccccacb12ec20|fc_0e05b35c94578fcb016ae26c5c99308191bc0740dfc2a6ca3e",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            0.003,
+            -0.171,
+            0.795,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ]
+        },
+        "duration": 0.6,
+        "settle": 0.24
+      },
+      "description": "lower near shoe above box rim"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223796.860208708] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.9076583925397403,2.089445255422863,1.4546758564746218,-0.9359729029341021,0.0020983747438904758,0.6910483067882552],\"max_segment_joint_change\":0.006180449019134837,\"measured_q\":[-0.901189923286438,2.0784759521484375,1.4630359411239624,-0.9493597149848938,0.0016712871147319674,0.6950069069862366],\"grip\":[0.8396634703332728],\"tcp\":{\"pose\":[0.0018037855152258642,-0.1686042508031495,0.8009633382598002,0.0005767295511718286,0.7055685951862317,0.002302956350375791,0.7086376515977687],\"timestamp\":9.48}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":9.48,\"waited_s\":0.8399999999999999},\n    \"right\": {\"measured_q\":[0.21868762373924255,2.349149227142334,2.472313165664673,-1.6932917833328247,0.004566576331853867,-4.473906993865967],\"grip\":[0.8240381804379551],\"tcp\":{\"pose\":[0.19837489371624997,0.015575779624339592,0.9400232677332943,-0.0002924959106377356,0.7071658500472698,0.0007037309396101165,0.7070472966752855],\"timestamp\":9.48}},\n    \"file\": \"/tmp/kinex-observation-w3fhvu6z.png\"\n  }\n}",
+    "call_id": "call_3410e13568674dba9c78cb58786142e1|fc_0e05b35c94578fcb016ae26c7127d881918d1c9f0399970eff",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "grips": {
+          "left": 1
+        },
+        "duration": 0.52,
+        "settle": 0
+      },
+      "description": "release near shoe into box"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223810.138928558] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10,\"waited_s\":0.5199999999999996},\n    \"left\": {\"measured_q\":[-0.9030988812446594,2.0846312046051025,1.4563316106796265,-0.9375838041305542,0.002041139407083392,0.6914576292037964],\"grip\":[0.9781321043317969],\"tcp\":{\"pose\":[0.0023022107816283444,-0.16925432379288402,0.7971472161026872,-0.00036430205739246274,0.7057867511512136,0.0025707515284416164,0.7084195934755086],\"timestamp\":10}},\n    \"right\": {\"measured_q\":[0.21778197586536407,2.349522113800049,2.472609519958496,-1.6934796571731567,0.004572504665702581,-4.474327564239502],\"grip\":[0.8236210156570781],\"tcp\":{\"pose\":[0.19876386776936317,0.015692001397513644,0.9399359967789964,-0.00021207173898674109,0.7071431483194491,0.0004420190870238846,0.7070702422175338],\"timestamp\":10}},\n    \"file\": \"/tmp/kinex-observation-mqaw_xsm.png\"\n  }\n}",
+    "call_id": "call_9de1b747a19543c5b5ad28f0a1b14e35|fc_0e05b35c94578fcb016ae26c8001c881919949c54d9be27036",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "left": [
+            -0.23,
+            -0.18,
+            0.94,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ],
+          "right": [
+            0.003,
+            -0.087,
+            0.88,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ]
+        },
+        "duration": 1.6,
+        "settle": 0.3
+      },
+      "description": "clear near row and position far shoe"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223825.237700211] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.3029406615483511,1.4601317643672989,1.2774506423510985,-1.38814618012482,0.00008826971323923154,1.2861114749667022],\"max_segment_joint_change\":0.04320701833293622,\"measured_q\":[-0.3268199861049652,1.473667860031128,1.268896222114563,-1.3711458444595337,0.00013764412142336369,1.2655565738677979],\"grip\":[0.9954985949126157],\"tcp\":{\"pose\":[-0.2234420230825827,-0.1791360265468177,0.9347732029075545,-0.000600390655715932,0.70762774173887,-0.002856605292725736,0.7065794070441124],\"timestamp\":11.92}},\n    \"right\": {\"target_q\":[0.7180670816966506,2.2785383810171047,2.1069806150836756,-1.3991631957307018,0.0038320742290281293,-3.973956566069662],\"max_segment_joint_change\":0.06328158398588979,\"measured_q\":[0.7010597586631775,2.268319606781006,2.102823495864868,-1.402452826499939,0.004190298728644848,-3.985568046569824],\"grip\":[0.822635371576656],\"tcp\":{\"pose\":[0.00977399022646136,-0.08302936425841839,0.8832660165004471,0.0027850239077577012,0.7076324371141649,-0.0010922535121931497,0.7065744012968245],\"timestamp\":11.92}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.92,\"waited_s\":1.92},\n    \"file\": \"/tmp/kinex-observation-5145i7tt.png\"\n  }\n}",
+    "call_id": "call_2bcab9063a5f471db271bc4fcecdb629|fc_0e05b35c94578fcb016ae26c8ef634819193f3542af79d4bf7",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "targets": {
+          "right": [
+            0.003,
+            -0.087,
+            0.795,
+            0,
+            0.70710678,
+            0,
+            0.70710678
+          ]
+        },
+        "duration": 0.9,
+        "settle": 0.32
+      },
+      "description": "lower far shoe above box rim"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223842.720896783] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"right\": {\"target_q\":[0.7200100387934371,2.314264594631272,1.8292255773152175,-1.0856937116394345,0.004208342230618559,-3.971584752701272],\"max_segment_joint_change\":0.019064546089732737,\"measured_q\":[0.712832510471344,2.305619955062866,1.8402382135391235,-1.099536657333374,0.004314719699323177,-3.9705166816711426],\"grip\":[0.8249491155147552],\"tcp\":{\"pose\":[0.004880994000601269,-0.08428218124142661,0.8013795046666288,0.004495685880999968,0.7084282284060708,-0.001358558475059154,0.7057672338135041],\"timestamp\":13.16}},\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.16,\"waited_s\":1.2400000000000002},\n    \"left\": {\"measured_q\":[-0.3054666519165039,1.4618515968322754,1.2769138813018799,-1.3879971504211426,0.00009789218893274665,1.2860099077224731],\"grip\":[0.9978405155918815],\"tcp\":{\"pose\":[-0.22938359630191002,-0.1801734683142392,0.9394668852690865,0.00013833433721333058,0.7073163702745995,-0.0015805167780718991,0.7068953495192136],\"timestamp\":13.16}},\n    \"file\": \"/tmp/kinex-observation-qgiw6y15.png\"\n  }\n}",
+    "call_id": "call_317c811a2b8d45a8add0019d9ecae1d3|fc_0e05b35c94578fcb016ae26ca08f3481919e08747def8fee92",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "file": ".kinex/tools/dual_shoes.py",
+      "args": {
+        "grips": {
+          "right": 1,
+          "left": 1
+        },
+        "duration": 0.56,
+        "settle": 0
+      },
+      "description": "release far shoe"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223853.744106609] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"episode ended during wait\",\n  \"code\": \"operation_rejected\",\n  \"details\": {\n    \"operation\": \"world.wait\",\n    \"type\": \"kinex_interfaces/srv/Wait\",\n    \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":13.48,\"waited_s\":0.3200000000000003}\n  }\n}",
+    "call_id": "call_855bae41d79b4122a218708c9f2a6f1d|fc_0e05b35c94578fcb016ae26cab1c3c81918c8ad84d143a629f",
+    "is_error": true
+  },
+  {
+    "name": "device_stop",
+    "input": {
+      "reason": "World reported episode ended during release wait; hold and inspect final measurements."
+    },
+    "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped",
+    "call_id": "call_5daa5ef62b724ef2867e3c346d1a3544|fc_0e05b35c94578fcb016ae26cb37ab4819183bfd8faad8f7b3c",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "head_camera",
+          "action": "image"
+        },
+        {
+          "deviceID": "left_gripper",
+          "action": "joint_state"
+        },
+        {
+          "deviceID": "right_gripper",
+          "action": "joint_state"
+        },
+        {
+          "deviceID": "world",
+          "action": "time"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":13.48,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-hilw3rij.png\"},\n    {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9982522617686879],\"velocities\":[0.00007679601284590635],\"joint_names\":[\"left_gripper\"],\"timestamp\":13.48}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9529129727320238],\"velocities\":[0.17699392681772058],\"joint_names\":[\"right_gripper\"],\"timestamp\":13.48}},\n    {\"deviceID\":\"world\",\"action\":\"time\",\"ok\":true,\"data\":{\"clock\":{\"sec\":13,\"nanosec\":480000000}}}\n  ]\n}",
+    "call_id": "call_5a7dcfb565f84f16b427e00d4290db11|fc_0e05b35c94578fcb016ae26cb87d088191850ccbe07a34f9ff",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/memos/dual_shoes.md",
+      "content": "# RoboTwin dual shoes / ALOHA AgileX\n\nPublic interface: read harness.md and refreshed profiles each episode; load world, both arms/grippers, head_camera. Native period 0.04 s, stepped world.wait only. Use typed kinex.communication operations. Do not reset or inspect private simulator state.\n\nReusable tool: `.kinex/tools/dual_shoes.py` accepts world-frame TCP targets, duration/settle, normalized grip targets, and dry_run. Uses measured joint state, calibrated base transforms, 0.12 m flange-to-TCP translation, Cartesian interpolation/Slerp and continuous seeded IK. Publishes both trajectories before one sequential wait. Returns measured TCP/grips and camera. Read schemas before using. JointModel.fk/ik require list, not ROS array; camera_frame returns (image,depth).\n\nPerception: paired head depth mm -> optical coordinates using profile intrinsics -> world via camera mount. In observed scene table z=.640, box bottom .642, rim .740, inner x about [-.125,.130], y[-.215,-.045]. Shoes length ~.22 m, width .07 m. Re-measure positions each scene. Initial left shoe pointed roughly [-.435,.9005,0], right [.10,.995,0]. Grasp across width at center: downward TCP x axis; local z opposite shoe toe direction; local y=cross(z,x). Grasp TCP z=.695 worked, lift to .865. Both measured grips stalled ~.82-.84 and visual lift confirmed shoes attached (stall alone is not evidence).\n\nPlace tips world -X using quaternion xyzw [0,.70710678,0,.70710678]. Near row for initially left shoe: observed target TCP [.003,-.171,.795]. Far row: [.003,-.087,.795]. Grasp offsets kept soles roughly .055 below TCP. Release just above rim: gripper fingers cannot descend to floor in narrow box without contacting walls. Shoe falls flat into box. Place sequentially; keep other held shoe high/outside, then withdraw released arm up/outward while bringing other shoe above far row. Example left clearance [-.23,-.18,.94]. Waits .3-.4 s after trajectories reduce tracking error; verify actual state. Near shoe was visibly flat, toe left, before placing far shoe.\n\nWorld can terminate during a release wait before requested duration. A rejected/interrupted wait is NOT proof of success. Stop and inspect final images/state; do not keep advancing terminal world. Observed terminal at t=13.48 during second release (337 frames): image showed both shoes flat inside box with tips left and initially left shoe nearer robot. Open commands had been sent to both; final openings left .9983 and right .9529. Native verdict is owner-only; do not claim it. Tool currently surfaces rejected wait as error, so final observation requires separate device reads.\n"
+    },
+    "output": "Successfully wrote to .kinex/memos/dual_shoes.md",
+    "call_id": "call_ce2bbe8cfcac48fa9a6cbaea14bb18f6|fc_0e05b35c94578fcb016ae26cc583608191b8162570ad00b3ce",
+    "is_error": false
+  }
+]
diff --git a/experiments/robotwin-native/kinex-2/trace.json b/experiments/robotwin-native/kinex-2/trace.json
new file mode 100644
index 0000000000000000000000000000000000000000..1578584155a1f3c00f08d8ee821f9504ea1756bf
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/trace.json
@@ -0,0 +1 @@
+{"schema": "rlebench/kinex-trace/1", "source": "Pi native session", "usage_source": "pi-normalized", "cache_coverage": "normalized-values", "reasoning_coverage": "normalized-values", "info": {"type": "session", "version": 3, "id": "01a18473-31ef-7247-8e38-e8b2337b6833", "timestamp": "2026-10-28T21:38:24.368Z", "cwd": "/workspace"}, "messages": [{"id": "2624d288", "role": "system", "timestamp": "2026-10-28T21:38:27.076Z", "parts": [{"type": "text", "text": ""}]}, {"id": "0493caf2", "role": "user", "timestamp": "2026-10-28T21:38:27.076Z", "parts": [{"type": "text", "text": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}]}, {"id": "6f184821", "role": "context", "timestamp": "2026-10-28T21:38:27.077Z", "parts": [{"type": "text", "text": "<kinex_context>\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-bd0a44110fcf49c58277037f711e7975/config/device, /opt/kinex/packages/core/assets/builtin/device.\n\ncore.python:\n{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}\n\ncore.recording:\n(none)\n\ncore.skills:\n<available_skills>\nLoad a skill with the skill tool when its description matches the task.\n  - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n  - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n  - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n  - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n  - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n  - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n  - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n  - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n  - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n  - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>\n\nproblems:\n(none)\n</kinex_context>"}], "context": true}, {"id": "c292d17d", "role": "assistant", "timestamp": "2026-10-28T21:38:29.943Z", "parts": [{"type": "tool", "callID": "call_367e99d2533843ab8b2927f77681118b|fc_0e05b35c94578fcb016ae26b55808c819199906d0ce7db8551", "name": "read", "input": {"path": "harness.md", "offset": null, "limit": null}, "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11324, "outputTokens": 27, "cachedInputTokens": 0, "cacheReportedInputTokens": 11324, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11324, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 27}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "4e139be9", "role": "toolResult", "timestamp": "2026-10-28T21:38:29.951Z", "parts": [{"type": "text", "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n"}]}, {"id": "c61aa38e", "role": "assistant", "timestamp": "2026-10-28T21:38:32.445Z", "parts": [{"type": "tool", "callID": "call_7ce3085100fd420dbb7436140ec43e28|fc_0e05b35c94578fcb016ae26b57d6788191b2062cc3c6a54dd6", "name": "ls", "input": {"path": ".kinex"}, "output": "devices/\nkinex.json\nmemos/\npython/\nskills/\ntools/\nworkspace.yaml", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11455, "outputTokens": 20, "cachedInputTokens": 11264, "cacheReportedInputTokens": 11455, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11455, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 20}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7266cd3b", "role": "toolResult", "timestamp": "2026-10-28T21:38:32.449Z", "parts": [{"type": "text", "text": "devices/\nkinex.json\nmemos/\npython/\nskills/\ntools/\nworkspace.yaml"}]}, {"id": "cbd2c355", "role": "assistant", "timestamp": "2026-10-28T21:38:35.253Z", "parts": [{"type": "tool", "callID": "call_267b6ee82c144a20a1f2c3245704c3a6|fc_0e05b35c94578fcb016ae26b5a73808191abbc02c144259293", "name": "bash", "input": {"command": "find .kinex/devices .kinex/tools .kinex/skills .kinex/memos -maxdepth 3 -type f", "timeout": null}, "output": ".kinex/devices/left_arm/links.py\n.kinex/devices/left_arm/arm.urdf\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/links.py\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/left_gripper/gripper.urdf\n.kinex/devices/right_arm/links.py\n.kinex/devices/right_arm/arm.urdf\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/links.py\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/right_gripper/gripper.urdf\n.kinex/devices/world/profile.yaml\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11503, "outputTokens": 49, "cachedInputTokens": 11392, "cacheReportedInputTokens": 11503, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11503, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 49}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "1467eccb", "role": "toolResult", "timestamp": "2026-10-28T21:38:35.269Z", "parts": [{"type": "text", "text": ".kinex/devices/left_arm/links.py\n.kinex/devices/left_arm/arm.urdf\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/links.py\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/left_gripper/gripper.urdf\n.kinex/devices/right_arm/links.py\n.kinex/devices/right_arm/arm.urdf\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/links.py\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/right_gripper/gripper.urdf\n.kinex/devices/world/profile.yaml\n"}]}, {"id": "9962d2a6", "role": "assistant", "timestamp": "2026-10-28T21:38:37.160Z", "parts": [{"type": "tool", "callID": "call_a523247bf39b433e98fc113a09b276d5|fc_0e05b35c94578fcb016ae26b5cb7f881919f83c987b965d722", "name": "read", "input": {"path": ".kinex/devices/world/profile.yaml", "offset": null, "limit": null}, "output": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11751, "outputTokens": 32, "cachedInputTokens": 11392, "cacheReportedInputTokens": 11751, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11751, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 32}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "a56669e5", "role": "toolResult", "timestamp": "2026-10-28T21:38:37.164Z", "parts": [{"type": "text", "text": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n"}]}, {"id": "8539a895", "role": "assistant", "timestamp": "2026-10-28T21:38:41.459Z", "parts": [{"type": "tool", "callID": "call_4cceb880d6784989bd7c305d425d4dfa|fc_0e05b35c94578fcb016ae26b5f6d248191a72c4e3a2ad19716", "name": "read", "input": {"path": ".kinex/devices/left_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_0ac1df91f68e4f75a427082873c0a21e|fc_0e05b35c94578fcb016ae26b615f4c8191b9d12716aaffa541", "name": "read", "input": {"path": ".kinex/devices/right_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_e23d1a1ce4e3491598e87028dbbe8b60|fc_0e05b35c94578fcb016ae26b615f4c8191b6a76c2b250df9eb", "name": "read", "input": {"path": ".kinex/devices/left_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_57217fff6fb842a2ba2ebf0be8d60587|fc_0e05b35c94578fcb016ae26b615f4c81918138bbfb49b2064e", "name": "read", "input": {"path": ".kinex/devices/right_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_cea94425e2fa4e5f85a2ed68057a9bdd|fc_0e05b35c94578fcb016ae26b615f4c8191a3b10ce3816ee991", "name": "read", "input": {"path": ".kinex/devices/head_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_e6b73f775a6747bd9a935378ace8ee6f|fc_0e05b35c94578fcb016ae26b615f4c8191b0a29903d0af9be0", "name": "read", "input": {"path": ".kinex/workspace.yaml", "offset": null, "limit": null}, "output": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 12198, "outputTokens": 195, "cachedInputTokens": 11648, "cacheReportedInputTokens": 12198, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 12198, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 195}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "fff103a2", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.465Z", "parts": [{"type": "text", "text": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n"}]}, {"id": "c87f235c", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.465Z", "parts": [{"type": "text", "text": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n"}]}, {"id": "8eb24d71", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.465Z", "parts": [{"type": "text", "text": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n"}]}, {"id": "b73b5d41", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.466Z", "parts": [{"type": "text", "text": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n"}]}, {"id": "19b57d67", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.466Z", "parts": [{"type": "text", "text": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n"}]}, {"id": "0fa84f2c", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.466Z", "parts": [{"type": "text", "text": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n"}]}, {"id": "d85504a9", "role": "assistant", "timestamp": "2026-10-28T21:38:45.671Z", "parts": [{"type": "tool", "callID": "call_400056f3564b4e9c9ea3a21b816e7625|fc_0e05b35c94578fcb016ae26b63222481918bcd825a71e9a64e", "name": "exec", "input": {"python": "from kinex import runtime,finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})", "description": "inspect operation contracts"}, "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 16449, "outputTokens": 55, "cachedInputTokens": 12288, "cacheReportedInputTokens": 16449, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 16449, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 55}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ac5dc29d", "role": "toolResult", "timestamp": "2026-10-28T21:38:46.600Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n    \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n  }\n}"}]}, {"id": "65aa7afa", "role": "assistant", "timestamp": "2026-10-28T21:38:51.042Z", "parts": [{"type": "tool", "callID": "call_62f70586a5e84cba9a1d9fbb8a895ecf|fc_0e05b35c94578fcb016ae26b68fbec8191ba256eb599d22906", "name": "devices", "input": {"calls": [{"deviceID": "world", "action": "load"}, {"deviceID": "left_arm", "action": "load"}, {"deviceID": "right_arm", "action": "load"}, {"deviceID": "left_gripper", "action": "load"}, {"deviceID": "right_gripper", "action": "load"}, {"deviceID": "head_camera", "action": "load"}]}, "output": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 17690, "outputTokens": 78, "cachedInputTokens": 16384, "cacheReportedInputTokens": 17690, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 17690, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 78}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "f4b57c28", "role": "toolResult", "timestamp": "2026-10-28T21:38:56.432Z", "parts": [{"type": "text", "text": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}"}]}, {"id": "ff126801", "role": "assistant", "timestamp": "2026-10-28T21:39:00.667Z", "parts": [{"type": "tool", "callID": "call_f7e1095205114cd39ccc99aad24386ff|fc_0e05b35c94578fcb016ae26b7351348191b04857fa875c4726", "name": "device", "input": {"deviceID": "head_camera", "action": "image"}, "output": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-s12ft6c7.png\"\n}", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_4f112267e1f54522ac8278187813134c|fc_0e05b35c94578fcb016ae26b748d7481918c67e322c71a25f4", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt", "offset": null, "limit": null}, "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_eadc0a081774488eb0bb83932c05dca2|fc_0e05b35c94578fcb016ae26b748d748191a4cfaf6b3fee5d5f", "name": "read", "input": {"path": ".kinex/devices/left_gripper/gripper.urdf", "offset": null, "limit": null}, "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 17920, "outputTokens": 108, "cachedInputTokens": 17664, "cacheReportedInputTokens": 17920, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 17920, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 108}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "07deeeee", "role": "toolResult", "timestamp": "2026-10-28T21:39:00.867Z", "parts": [{"type": "text", "text": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-s12ft6c7.png\"\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "cd6142f6", "role": "toolResult", "timestamp": "2026-10-28T21:39:00.872Z", "parts": [{"type": "text", "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n"}]}, {"id": "9385ed05", "role": "toolResult", "timestamp": "2026-10-28T21:39:00.874Z", "parts": [{"type": "text", "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n"}]}, {"id": "36b3b3e8", "role": "assistant", "timestamp": "2026-10-28T21:39:06.893Z", "parts": [{"type": "reasoning", "text": "**Inspecting shoe-box kinematics**"}, {"type": "tool", "callID": "call_33489e3730f749d496ec9b7b34d2fca5|fc_0e05b35c94578fcb016ae26b7ac83481919db7af8831435ab4", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", "offset": 1, "limit": 300}, "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 19127, "outputTokens": 84, "cachedInputTokens": 17920, "cacheReportedInputTokens": 19127, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 19127, "reasoningOutputTokens": 40, "reasoningReportedOutputTokens": 84}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ed5f7847", "role": "toolResult", "timestamp": "2026-10-28T21:39:06.897Z", "parts": [{"type": "text", "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n"}]}, {"id": "9d8cbba9", "role": "assistant", "timestamp": "2026-10-28T21:39:19.714Z", "parts": [{"type": "tool", "callID": "call_dd0ab53fe66841fd9221dc3616c18876|fc_0e05b35c94578fcb016ae26b82fea881919b755b5ff018d7d5", "name": "exec", "input": {"python": "import asyncio\nfrom kinex import runtime, helpers, communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'q':m.position,'fk':helpers.joint_model(d).fk(m.position),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())", "description": "inspect measured state and schemas"}, "output": "{\n  \"output\": \"[INFO] [1793223561.071991926] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 14, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 8, in main\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n    q = joint_target(positions, self.joints, check_limits=False)\\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n    vector(value, len(joints), \\\"joint target\\\")\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n    raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 19421, "outputTokens": 253, "cachedInputTokens": 19072, "cacheReportedInputTokens": 19421, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 19421, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 253}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7e43e20c", "role": "toolResult", "timestamp": "2026-10-28T21:39:21.475Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223561.071991926] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 14, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 8, in main\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n    q = joint_target(positions, self.joints, check_limits=False)\\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n    vector(value, len(joints), \\\"joint target\\\")\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n    raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}"}]}, {"id": "6b962d6d", "role": "assistant", "timestamp": "2026-10-28T21:39:26.606Z", "parts": [{"type": "tool", "callID": "call_ebc53b54c9144398add4c4510616f6fe|fc_0e05b35c94578fcb016ae26b8ba19881918312f9fe01c977d2", "name": "exec", "input": {"python": "import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   q=list(m.position)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(q),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  f=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(f['image']),depth=helpers.image_array(f['depth']))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())", "description": "measure initial scene"}, "output": "{\n  \"output\": \"[INFO] [1793223568.172439105] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 15, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 12, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 20209, "outputTokens": 256, "cachedInputTokens": 19456, "cacheReportedInputTokens": 20209, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20209, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 256}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "175a61e9", "role": "toolResult", "timestamp": "2026-10-28T21:39:29.070Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223568.172439105] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"Traceback (most recent call last):\\n  File \\\"<frozen runpy>\\\", line 198, in _run_module_as_main\\n  File \\\"<frozen runpy>\\\", line 88, in _run_code\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in <module>\\n    script()\\n  File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n    exec(compile(_request[\\\"source\\\"], \\\"<kinex-exec>\\\", \\\"exec\\\"), module.__dict__)\\n  File \\\"<kinex-exec>\\\", line 15, in <module>\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n    return runner.run(main)\\n           ^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n    return self._loop.run_until_complete(task)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n    return future.result()\\n           ^^^^^^^^^^^^^^^\\n  File \\\"<kinex-exec>\\\", line 12, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}]}, {"id": "1573d9c6", "role": "assistant", "timestamp": "2026-10-28T21:39:31.726Z", "parts": [{"type": "tool", "callID": "call_c2b8d5d34932451c817efe51bdba851d|fc_0e05b35c94578fcb016ae26b9318f08191b9765db7a8ffc3d7", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", "offset": 1, "limit": 220}, "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 20861, "outputTokens": 39, "cachedInputTokens": 20352, "cacheReportedInputTokens": 20861, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20861, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "74e73206", "role": "toolResult", "timestamp": "2026-10-28T21:39:31.730Z", "parts": [{"type": "text", "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n"}]}, {"id": "d7788a05", "role": "assistant", "timestamp": "2026-10-28T21:39:37.269Z", "parts": [{"type": "tool", "callID": "call_90dc0d2cd0e74d118298a624ddad2d76|fc_0e05b35c94578fcb016ae26b967e8c81919e4629fbd557a833", "name": "exec", "input": {"python": "import asyncio\nfrom kinex import runtime,helpers,communication,finish\nasync def main():\n async with communication() as io:\n  data={}\n  for d in ['left_arm','right_arm']:\n   async with io.subscribe(d+'.joint_state') as s: m=await s.next(timeout_s=10)\n   data[d]={'state':m,'fk':helpers.joint_model(d).fk(list(m.position)),'tcp':await helpers.relative_pose(io,{'frame':d.replace('arm','gripper')+':tcp'},{'frame':'world:base'})}\n  im,dep=await helpers.camera_frame(io,'head_camera')\n  import numpy as np\n  np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n  data['schemas']={op:runtime.describe(op) for op in ['left_arm.joint_target','right_arm.joint_target','left_gripper.target','right_gripper.target','world.wait']}\n  finish(data)\nasyncio.run(main())", "description": "measure initial scene"}, "output": "{\n  \"output\": \"[INFO] [1793223578.614148509] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\"fk\":[0.1056935554023861,-0.0011901315627094447,0.15999839199046592,-0.000007715996484952103,0.0000021542621098628648,-2.4666643061721644e-7,0.9999999999678809],\"tcp\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0}},\n    \"right_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\"fk\":[0.10569355567032844,-0.0011901318683733137,0.15999839080293787,-0.000007716210574917094,0.0000021565576609891396,-2.4864112524367e-7,0.9999999999678738],\"tcp\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}},\n    \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"right_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"right_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}}\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 21132, "outputTokens": 250, "cachedInputTokens": 20736, "cacheReportedInputTokens": 21132, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21132, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 250}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "20d81030", "role": "toolResult", "timestamp": "2026-10-28T21:39:39.212Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223578.614148509] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\"fk\":[0.1056935554023861,-0.0011901315627094447,0.15999839199046592,-0.000007715996484952103,0.0000021542621098628648,-2.4666643061721644e-7,0.9999999999678809],\"tcp\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0}},\n    \"right_arm\": {\"state\":{\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\"fk\":[0.10569355567032844,-0.0011901318683733137,0.15999839080293787,-0.000007716210574917094,0.0000021565576609891396,-2.4864112524367e-7,0.9999999999678738],\"tcp\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}},\n    \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"right_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"right_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/right_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}}\n  }\n}"}]}, {"id": "0c9864a9", "role": "assistant", "timestamp": "2026-10-28T21:39:51.107Z", "parts": [{"type": "reasoning", "text": "**Estimating shoe 3D points**"}, {"type": "tool", "callID": "call_d3bf3218a40449379a6d81f7d8ad6ce3|fc_0e05b35c94578fcb016ae26ba2b72081919637c5b5562ccdd2", "name": "exec", "input": {"python": "import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nf=np.load('/tmp/head.npz');d=f['depth']*.001\nrot=R.from_quat([-.948683301192,0,0,.316227769682]).as_matrix();t=np.array([-.032,-.45,1.35])\nv,u=np.indices(d.shape);p=np.stack([(u-159.5)*d/358.64218,(v-119.5)*d/358.64218,d],axis=-1)@rot.T+t\nnp.save('/tmp/points.npy',p)\nfinish({str((u,v)):p[v,u].tolist() for u,v in [(65,124),(70,137),(78,156),(83,176),(258,109),(260,127),(264,149),(261,166),(120,145),(235,145),(111,214),(241,214),(175,195),(175,170),(45,198)]})", "description": "project shoe and box points"}, "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(65, 124)\": [-0.24701096162197103,0.03140910949709719,0.6910568320592497],\n    \"(70, 137)\": [-0.21991292201045629,-0.027594196989208564,0.7255543520299529],\n    \"(78, 156)\": [-0.20947912417886824,-0.044987609248771654,0.6775092925698912],\n    \"(83, 176)\": [-0.183232908521803,-0.11395591142723871,0.7157830657360426],\n    \"(258, 109)\": [0.19705560076620107,0.06993367883975005,0.6974502592814215],\n    \"(260, 127)\": [0.1815303772690653,-0.005548079149456486,0.7308389405389644],\n    \"(264, 149)\": [0.20284967663312783,-0.01943781830802399,0.6654216358573333],\n    \"(261, 166)\": [0.18054192688656984,-0.07729713643164932,0.6907771470716708],\n    \"(120, 145)\": [-0.11416267255569326,-0.0448333771340384,0.721374966820128],\n    \"(235, 145)\": [0.12504510830265422,-0.0448333771340384,0.721374966820128],\n    \"(111, 214)\": [-0.1258512028897438,-0.1798917686242506,0.6850811723963807],\n    \"(241, 214)\": [0.1259359683794026,-0.17950256367990514,0.6841230761030037],\n    \"(175, 195)\": [0.001018982875912705,-0.12026751685270726,0.6422993613618274],\n    \"(175, 170)\": [0.002574851178966185,-0.060117673106498126,0.6424117444706855],\n    \"(45, 198)\": [-0.2752759024607758,-0.12622992376337028,0.6403275561418671]\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23102, "outputTokens": 339, "cachedInputTokens": 21248, "cacheReportedInputTokens": 23102, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23102, "reasoningOutputTokens": 98, "reasoningReportedOutputTokens": 339}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "a69f3863", "role": "toolResult", "timestamp": "2026-10-28T21:39:53.041Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"(65, 124)\": [-0.24701096162197103,0.03140910949709719,0.6910568320592497],\n    \"(70, 137)\": [-0.21991292201045629,-0.027594196989208564,0.7255543520299529],\n    \"(78, 156)\": [-0.20947912417886824,-0.044987609248771654,0.6775092925698912],\n    \"(83, 176)\": [-0.183232908521803,-0.11395591142723871,0.7157830657360426],\n    \"(258, 109)\": [0.19705560076620107,0.06993367883975005,0.6974502592814215],\n    \"(260, 127)\": [0.1815303772690653,-0.005548079149456486,0.7308389405389644],\n    \"(264, 149)\": [0.20284967663312783,-0.01943781830802399,0.6654216358573333],\n    \"(261, 166)\": [0.18054192688656984,-0.07729713643164932,0.6907771470716708],\n    \"(120, 145)\": [-0.11416267255569326,-0.0448333771340384,0.721374966820128],\n    \"(235, 145)\": [0.12504510830265422,-0.0448333771340384,0.721374966820128],\n    \"(111, 214)\": [-0.1258512028897438,-0.1798917686242506,0.6850811723963807],\n    \"(241, 214)\": [0.1259359683794026,-0.17950256367990514,0.6841230761030037],\n    \"(175, 195)\": [0.001018982875912705,-0.12026751685270726,0.6422993613618274],\n    \"(175, 170)\": [0.002574851178966185,-0.060117673106498126,0.6424117444706855],\n    \"(45, 198)\": [-0.2752759024607758,-0.12622992376337028,0.6403275561418671]\n  }\n}"}]}, {"id": "5fef81d8", "role": "assistant", "timestamp": "2026-10-28T21:40:13.207Z", "parts": [{"type": "reasoning", "text": "**Planning shoe grasp**\n\n**Aligning gripper for shoe**"}, {"type": "tool", "callID": "call_359440a490204ab5b768a4249857ef22|fc_0e05b35c94578fcb016ae26bb92b4c8191aff65d64307ea8e1", "name": "exec", "input": {"python": "import numpy as np\nfrom kinex import finish\np=np.load('/tmp/points.npy');v,u=np.indices(p.shape[:2]);out={}\nfor name,mask in [('left',(u>45)&(u<106)&(v>105)&(v<194)),('right',(u>239)&(u<287)&(v>90)&(v<180))]:\n sel=p[mask&(p[:,:,2]>.653)&(p[:,:,2]<.78)]\n xy=sel[:,:2];cov=np.cov(xy.T);w,V=np.linalg.eigh(cov);axis=V[:,-1];axis*=np.sign(axis[1]);proj=xy@axis;perp=np.array([axis[1],-axis[0]])\n out[name]={'bounds':np.percentile(sel,[0,5,50,95,100],axis=0),'axis':axis,'along':np.percentile(proj,[0,50,100]),'across':np.percentile(xy@perp,[0,50,100])}\nfinish(out)", "description": "estimate shoe geometry"}, "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": {\"bounds\":[[-0.27703949870034816,-0.11849672931365157,0.6530756384513174],[-0.26232817835314304,-0.11589681011362746,0.6648956826818977],[-0.2084795206185731,-0.048453607301866264,0.6989171661594558],[-0.16517033707524315,0.04587652017197147,0.7280738549115844],[-0.15957980670316027,0.0673668820840671,0.7397426331669105]],\"axis\":[-0.4348054796080431,0.9005243999486185],\"along\":[-0.02822664134255312,0.04733788568632173,0.17347461851403906],\"across\":[-0.2438903331252665,-0.20888409446241507,-0.17148698275945262]},\n    \"right\": {\"bounds\":[[0.12579376536245684,-0.12617690826397737,0.6530134103302274],[0.16420628560756578,-0.07877877371633266,0.6623519701729963],[0.19117578484493938,-0.005752951117641736,0.69816639561117],[0.22156610312819316,0.09690986291317003,0.7299211106280531],[0.23969001705265122,0.12021601950746491,0.7417706810964337]],\"axis\":[0.16462025977367772,0.9863570195786345],\"along\":[-0.10348059217440107,0.02641075351907217,0.15416704938916012],\"across\":[0.13832065240589805,0.18958687182277106,0.2274015727704394]}\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23987, "outputTokens": 512, "cachedInputTokens": 23296, "cacheReportedInputTokens": 23987, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23987, "reasoningOutputTokens": 253, "reasoningReportedOutputTokens": 512}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0f2b928c", "role": "toolResult", "timestamp": "2026-10-28T21:40:14.556Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": {\"bounds\":[[-0.27703949870034816,-0.11849672931365157,0.6530756384513174],[-0.26232817835314304,-0.11589681011362746,0.6648956826818977],[-0.2084795206185731,-0.048453607301866264,0.6989171661594558],[-0.16517033707524315,0.04587652017197147,0.7280738549115844],[-0.15957980670316027,0.0673668820840671,0.7397426331669105]],\"axis\":[-0.4348054796080431,0.9005243999486185],\"along\":[-0.02822664134255312,0.04733788568632173,0.17347461851403906],\"across\":[-0.2438903331252665,-0.20888409446241507,-0.17148698275945262]},\n    \"right\": {\"bounds\":[[0.12579376536245684,-0.12617690826397737,0.6530134103302274],[0.16420628560756578,-0.07877877371633266,0.6623519701729963],[0.19117578484493938,-0.005752951117641736,0.69816639561117],[0.22156610312819316,0.09690986291317003,0.7299211106280531],[0.23969001705265122,0.12021601950746491,0.7417706810964337]],\"axis\":[0.16462025977367772,0.9863570195786345],\"along\":[-0.10348059217440107,0.02641075351907217,0.15416704938916012],\"across\":[0.13832065240589805,0.18958687182277106,0.2274015727704394]}\n  }\n}"}]}, {"id": "c22adbd2", "role": "assistant", "timestamp": "2026-10-28T21:40:24.192Z", "parts": [{"type": "reasoning", "text": "**Refining the box mask**"}, {"type": "text", "text": "I’ll grasp each shoe from above, rotate the tips left, and place the initially left shoe in the nearer row of the box."}, {"type": "tool", "callID": "call_74b06e15e6d5437b8dbf59b863ebf7bc|fc_0e05b35c94578fcb016ae26bc810f88191bbc88266170ed223", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/lib/joints.py", "offset": 1, "limit": 240}, "output": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n    \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n    Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in <device>/base; link\n    defaults to the model's tip_link.\n    \"\"\"\n\n    def __init__(self, profile, directory=None):\n        if profile.get(\"type\") != \"joint\":\n            raise ValueError(\"expected a joint device profile\")\n        model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n        if self.tree is None:\n            if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n                raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n            self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n        self.joints = urdf.commanded(self.tree)\n        self.names = [joint[\"name\"] for joint in self.joints]\n        self.tip = model[\"tip_link\"]\n\n    def chain(self, link):\n        link = link or self.tip\n        if not link:\n            raise ValueError(\"name a link; the model declares no tip_link\")\n        return Chain(self.tree, link)\n\n    def fk(self, positions, link=None):\n        \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n        q = joint_target(positions, self.joints, check_limits=False)\n        return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n    def ik(self, target, seed, link=None, **options):\n        \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n        Joints off the link's path keep their seed values. options override the\n        solver tolerances, damping, max_iterations and search budget.\n        \"\"\"\n        unknown = options.keys() - {*DEFAULTS, \"budget\"}\n        if unknown:\n            raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n        values = dict(zip(self.names, project_seed(seed, self.joints)))\n        values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n        return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n    \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n    home = profile.get(\"home\")\n    if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n        return 0.0\n    return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n    \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n    joint_target(actions[-1], joints)\n    previous = None\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n        for i, (q, joint) in enumerate(zip(target, joints)):\n            limits = joint[\"limits\"]\n            if limits is None:\n                continue\n            margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n            low, high = limits[\"min\"], limits[\"max\"]\n            if not low - margin <= q <= high + margin:\n                raise ValueError(\"target outside joint limits recovery tolerance\")\n            if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n                raise ValueError(\"joint limits recovery must move inward\")\n        previous = target\n\n\ndef command(profile, actions, joints):\n    \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n    if not isinstance(actions, list) or not actions:\n        raise ValueError(\"actions must be a nonempty sequence\")\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n    if profile[\"limits\"] == \"strict\":\n        recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n    return actions\n\n\ndef project_seed(value, joints):\n    \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n    values = vector(value, len(joints), \"IK seed\")\n    return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n            for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n    \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n    if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n        raise ValueError(\"joint state requires unique names and matching positions\")\n    positions = dict(zip(message.name, message.position))\n    names = [joint[\"name\"] for joint in joints]\n    if any(name not in positions for name in names):\n        raise ValueError(\"joint state is missing modeled joints\")\n    return joint_target([positions[name] for name in names], joints, check_limits=False)\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 24976, "outputTokens": 236, "cachedInputTokens": 24320, "cacheReportedInputTokens": 24976, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 24976, "reasoningOutputTokens": 160, "reasoningReportedOutputTokens": 236}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "86de06f4", "role": "toolResult", "timestamp": "2026-10-28T21:40:24.196Z", "parts": [{"type": "text", "text": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n    \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n    Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in <device>/base; link\n    defaults to the model's tip_link.\n    \"\"\"\n\n    def __init__(self, profile, directory=None):\n        if profile.get(\"type\") != \"joint\":\n            raise ValueError(\"expected a joint device profile\")\n        model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n        if self.tree is None:\n            if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n                raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n            self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n        self.joints = urdf.commanded(self.tree)\n        self.names = [joint[\"name\"] for joint in self.joints]\n        self.tip = model[\"tip_link\"]\n\n    def chain(self, link):\n        link = link or self.tip\n        if not link:\n            raise ValueError(\"name a link; the model declares no tip_link\")\n        return Chain(self.tree, link)\n\n    def fk(self, positions, link=None):\n        \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n        q = joint_target(positions, self.joints, check_limits=False)\n        return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n    def ik(self, target, seed, link=None, **options):\n        \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n        Joints off the link's path keep their seed values. options override the\n        solver tolerances, damping, max_iterations and search budget.\n        \"\"\"\n        unknown = options.keys() - {*DEFAULTS, \"budget\"}\n        if unknown:\n            raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n        values = dict(zip(self.names, project_seed(seed, self.joints)))\n        values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n        return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n    \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n    home = profile.get(\"home\")\n    if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n        return 0.0\n    return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n    \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n    joint_target(actions[-1], joints)\n    previous = None\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n        for i, (q, joint) in enumerate(zip(target, joints)):\n            limits = joint[\"limits\"]\n            if limits is None:\n                continue\n            margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n            low, high = limits[\"min\"], limits[\"max\"]\n            if not low - margin <= q <= high + margin:\n                raise ValueError(\"target outside joint limits recovery tolerance\")\n            if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n                raise ValueError(\"joint limits recovery must move inward\")\n        previous = target\n\n\ndef command(profile, actions, joints):\n    \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n    if not isinstance(actions, list) or not actions:\n        raise ValueError(\"actions must be a nonempty sequence\")\n    for target in actions:\n        joint_target(target, joints, check_limits=False)\n    if profile[\"limits\"] == \"strict\":\n        recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n    return actions\n\n\ndef project_seed(value, joints):\n    \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n    values = vector(value, len(joints), \"IK seed\")\n    return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n            for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n    \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n    if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n        raise ValueError(\"joint state requires unique names and matching positions\")\n    positions = dict(zip(message.name, message.position))\n    names = [joint[\"name\"] for joint in joints]\n    if any(name not in positions for name in names):\n        raise ValueError(\"joint state is missing modeled joints\")\n    return joint_target([positions[name] for name in names], joints, check_limits=False)\n"}]}, {"id": "bc292a92", "role": "assistant", "timestamp": "2026-10-28T21:40:49.414Z", "parts": [{"type": "reasoning", "text": "**Creating staged motion script**"}, {"type": "tool", "callID": "call_0868a220b62b474994731df198d503cc|fc_0e05b35c94578fcb016ae26bd23c40819191af04b6129b3ae0", "name": "write", "input": {"path": ".kinex/tools/dual_shoes.py", "content": "\"\"\"Measured Cartesian dual-arm moves through public Kinex ROS operations.\nInvoke with exec file and args: targets {left/right: [x,y,z,qx,qy,qz,qw]},\nduration, settle; optional grips {left/right: opening}. World coordinates.\nA dry_run solves without publishing; each path uses measured start FK.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, finish, args\n\nasync def state(io, device):\n    async with io.subscribe(device+'.joint_state') as stream:\n        return await stream.next(timeout_s=10)\n\ndef matrix(p):\n    t=np.eye(4);t[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();t[:3,3]=p[:3];return t\n\ndef pose(t):\n    return [*t[:3,3],*Rotation.from_matrix(t[:3,:3]).as_quat()]\n\nasync def main():\n    a=args(); duration=a.get('duration',1.2); plans={}; report={}\n    async with communication() as io:\n        for side,target in a.get('targets',{}).items():\n            dev=side+'_arm'; model=helpers.joint_model(dev)\n            measured=await state(io,dev); q=list(measured.position)\n            base=matrix((await helpers.relative_pose(io,{'frame':dev+':base'},{'frame':'world:base'}))['pose'])\n            tcp=np.eye(4);tcp[0,3]=.12\n            start=base@matrix(model.fk(q))@tcp; end=matrix(target)\n            interp=Slerp([0,1],Rotation.from_matrix(np.stack([start[:3,:3],end[:3,:3]])))\n            points=[]; max_jump=0\n            for i in range(1,a.get('segments',20)+1):\n                f=i/a.get('segments',20);t=np.eye(4);t[:3,3]=start[:3,3]*(1-f)+end[:3,3]*f;t[:3,:3]=interp(f).as_matrix()\n                goal=pose(np.linalg.inv(base)@t@np.linalg.inv(tcp))\n                qnew=model.ik(goal,q);max_jump=max(max_jump,float(np.max(np.abs(np.array(qnew)-q))));q=qnew\n                ns=round(duration*f*1e9)\n                points.append({'positions':q,'time_from_start':{'sec':ns//10**9,'nanosec':ns%10**9}})\n            if max_jump>1.0: raise ValueError(f'{side}: IK discontinuity {max_jump}')\n            plans[dev]={'joint_names':model.names,'points':points}\n            report[side]={'target_q':q,'max_segment_joint_change':max_jump}\n        if not a.get('dry_run',False):\n            for dev,payload in plans.items(): await io.publish(dev+'.joint_target',payload)\n            for side,opening in a.get('grips',{}).items(): await io.publish(side+'_gripper.target',{'data':opening})\n            report['wait']=await io.request('world.wait',duration_s=duration+a.get('settle',.2))\n            for side in ['left','right']:\n                m=await state(io,side+'_arm');g=await state(io,side+'_gripper')\n                report[side]={**report.get(side,{}),'measured_q':list(m.position),'grip':list(g.position),'tcp':await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})}\n            im,dep=await helpers.camera_frame(io,'head_camera')\n            np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))\n            image=helpers.save_image(im)\n        else: image=None\n    finish(report,file=image)\n\nif __name__=='__main__': asyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/dual_shoes.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 26391, "outputTokens": 1054, "cachedInputTokens": 25088, "cacheReportedInputTokens": 26391, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 26391, "reasoningOutputTokens": 118, "reasoningReportedOutputTokens": 1054}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ffb9c778", "role": "toolResult", "timestamp": "2026-10-28T21:40:49.418Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/dual_shoes.py"}]}, {"id": "f9c690f4", "role": "assistant", "timestamp": "2026-10-28T21:40:54.106Z", "parts": [{"type": "tool", "callID": "call_ae9204374e2d4a3cbfbeb1673bf19662|fc_0e05b35c94578fcb016ae26be3d0a88191ad5024466f984815", "name": "exec", "input": {"python": "import numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import finish\nout={}\nfor s,axis in [('left',[-.435,.9005,0]),('right',[.10,.995,0]),('placed',[-1,0,0])]:\n x=np.array([0,0,-1]);z=-np.array(axis);z=z/np.linalg.norm(z);y=np.cross(z,x)\n out[s]=R.from_matrix(np.column_stack([x,y,z])).as_quat()\nfinish(out)", "description": "construct grasp orientations"}, "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": [0.3758414690119471,0.5989517427731067,-0.3758414690119471,0.5989517427731066],\n    \"right\": [0.5244041261335186,0.47434197842288933,-0.5244041261335186,0.47434197842288933],\n    \"placed\": [0,0.7071067811865475,0,0.7071067811865475]\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27467, "outputTokens": 140, "cachedInputTokens": 27264, "cacheReportedInputTokens": 27467, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27467, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 140}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ecd0a698", "role": "toolResult", "timestamp": "2026-10-28T21:40:55.933Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"left\": [0.3758414690119471,0.5989517427731067,-0.3758414690119471,0.5989517427731066],\n    \"right\": [0.5244041261335186,0.47434197842288933,-0.5244041261335186,0.47434197842288933],\n    \"placed\": [0,0.7071067811865475,0,0.7071067811865475]\n  }\n}"}]}, {"id": "cef1d2dd", "role": "assistant", "timestamp": "2026-10-28T21:41:00.359Z", "parts": [{"type": "tool", "callID": "call_2b7ac2f9914d4c2da36300af3a602d5e|fc_0e05b35c94578fcb016ae26be9d248819188a0d3ee362a810a", "name": "exec", "input": {"file": ".kinex/tools/dual_shoes.py", "args": {"targets": {"left": [-0.218, -0.025, 0.84, 0.37584147, 0.59895174, -0.37584147, 0.59895174], "right": [0.195, 0.016, 0.84, 0.52440413, 0.47434198, -0.52440413, 0.47434198]}, "grips": {"left": 1, "right": 1}, "duration": 2, "settle": 0.2}, "description": "approach both shoes"}, "output": "{\n  \"output\": \"[INFO] [1793223662.312752592] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"left\": {\"target_q\":[-0.22774460233231092,2.0844783296165446,1.6334247882592328,-1.119696352368475,0.002011036180461048,2.49168103259622],\"max_segment_joint_change\":0.22250770540570428,\"measured_q\":[-0.19359128177165985,2.0299289226531982,1.565936803817749,-1.0698388814926147,0.0637018159031868,2.444208860397339],\"grip\":[0.9992598511955955],\"tcp\":{\"pose\":[-0.22299369029360194,-0.02944199607878123,0.8439472305082514,0.3711196237801609,0.5970864937471737,-0.3314537937041148,0.6292029294823159],\"timestamp\":2.2}},\n    \"right\": 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Native period 0.04 s, stepped world.wait only. Use typed kinex.communication operations. Do not reset or inspect private simulator state.\n\nReusable tool: `.kinex/tools/dual_shoes.py` accepts world-frame TCP targets, duration/settle, normalized grip targets, and dry_run. Uses measured joint state, calibrated base transforms, 0.12 m flange-to-TCP translation, Cartesian interpolation/Slerp and continuous seeded IK. Publishes both trajectories before one sequential wait. Returns measured TCP/grips and camera. Read schemas before using. JointModel.fk/ik require list, not ROS array; camera_frame returns (image,depth).\n\nPerception: paired head depth mm -> optical coordinates using profile intrinsics -> world via camera mount. In observed scene table z=.640, box bottom .642, rim .740, inner x about [-.125,.130], y[-.215,-.045]. Shoes length ~.22 m, width .07 m. Re-measure positions each scene. Initial left shoe pointed roughly [-.435,.9005,0], right [.10,.995,0]. Grasp across width at center: downward TCP x axis; local z opposite shoe toe direction; local y=cross(z,x). Grasp TCP z=.695 worked, lift to .865. Both measured grips stalled ~.82-.84 and visual lift confirmed shoes attached (stall alone is not evidence).\n\nPlace tips world -X using quaternion xyzw [0,.70710678,0,.70710678]. Near row for initially left shoe: observed target TCP [.003,-.171,.795]. Far row: [.003,-.087,.795]. Grasp offsets kept soles roughly .055 below TCP. Release just above rim: gripper fingers cannot descend to floor in narrow box without contacting walls. Shoe falls flat into box. Place sequentially; keep other held shoe high/outside, then withdraw released arm up/outward while bringing other shoe above far row. Example left clearance [-.23,-.18,.94]. Waits .3-.4 s after trajectories reduce tracking error; verify actual state. Near shoe was visibly flat, toe left, before placing far shoe.\n\nWorld can terminate during a release wait before requested duration. A rejected/interrupted wait is NOT proof of success. Stop and inspect final images/state; do not keep advancing terminal world. Observed terminal at t=13.48 during second release (337 frames): image showed both shoes flat inside box with tips left and initially left shoe nearer robot. Open commands had been sent to both; final openings left .9983 and right .9529. Native verdict is owner-only; do not claim it. Tool currently surfaces rejected wait as error, so final observation requires separate device reads.\n"}, "output": "Successfully wrote to .kinex/memos/dual_shoes.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 36103, "outputTokens": 839, "cachedInputTokens": 35584, "cacheReportedInputTokens": 36103, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 36103, "reasoningOutputTokens": 200, "reasoningReportedOutputTokens": 839}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3184d722", "role": "toolResult", "timestamp": "2026-10-28T21:45:05.157Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/memos/dual_shoes.md"}]}, {"id": "6e5bebe3", "role": "assistant", "timestamp": "2026-10-28T21:45:09.896Z", "parts": [{"type": "text", "text": "Both shoes appear flat in the box, tips pointing left, with the initially left shoe nearer the robot. Both grippers were commanded open. The episode ended during final release; the native verdict remains unconfirmed."}], "usage": {"inputTokens": 36965, "outputTokens": 46, "cachedInputTokens": 36736, "cacheReportedInputTokens": 36965, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 36965, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 46}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "stop"}]}
diff --git a/experiments/robotwin-native/kinex-2/usage-audit.json b/experiments/robotwin-native/kinex-2/usage-audit.json
new file mode 100644
index 0000000000000000000000000000000000000000..72bca3ed033bda3f225ed594493d352fff3e8cd6
--- /dev/null
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diff --git a/experiments/robotwin-native/kinex-2/workspace.json b/experiments/robotwin-native/kinex-2/workspace.json
new file mode 100644
index 0000000000000000000000000000000000000000..0e08cda0e56184bf98bdb5efe85503109d983925
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace.json
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+{
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+    ".kinex/devices/right_arm/arm.urdf",
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+    ".kinex/devices/world/profile.yaml",
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+    ".kinex/memos/dual_shoes.md",
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+      "sha256": "703ec9cee9365c7c5c4b967219de9a9e7b9ca17517eae82cf7af30431de4026e"
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+    ".kinex/memos/dual_shoes.md": {
+      "bytes": 2628,
+      "sha256": "2471546bfcf53973bcb6c7c6289370d88500aabee87971d9cce7e6c71b45ddd9"
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+      "sha256": "dcd118f98fb81df24938ad8c6d38805de3fb823356c90da24ced18c6ec6a55e4"
+    },
+    ".kinex/workspace.yaml": {
+      "bytes": 335,
+      "sha256": "8a0cbe7e2438339e12693ecdda4530b8601e092a11c9e1d5f6301bd67a415ca7"
+    },
+    "harness.md": {
+      "bytes": 444,
+      "sha256": "81c1ef513247775681eb49b5ac07a5f4eebc7848afab963ef21a3bca473b8485"
+    },
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+      "sha256": "2a3f75a12a0c993a55034eba3fe4146b33dd852cdece7e4babb9ccc909d10db0"
+    }
+  },
+  "modified": [],
+  "removed": [],
+  "schema": "rlebench/episode-workspace/1",
+  "scope": [
+    "instruction.md",
+    "harness.md",
+    ".kinex/workspace.yaml",
+    ".kinex/devices",
+    ".kinex/kinex.json",
+    "tools",
+    "skills",
+    "memos",
+    ".kinex/tools",
+    ".kinex/skills",
+    ".kinex/memos"
+  ],
+  "skipped_symlinks": []
+}
diff --git a/experiments/robotwin-native/kinex-2/workspace.tar.gz b/experiments/robotwin-native/kinex-2/workspace.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..405658a592334920f73de50485d72b615bb1bb7e
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace.tar.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71314c04c838ff7fa9b7717be16d80887bfd5c3c35d259c94c6c7a2f70450a81
+size 9516
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/head_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..df32c26d09c67569653292a05627b319e621bf01
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/head_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: head_camera
+description: "Fixed RGB-D head camera (D435 model) above the table. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.097Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.03200000152
+      - -0.449999988079
+      - 1.350000023842
+    rotation_xyzw:
+      - -0.948683301192
+      - 0
+      - 0
+      - 0.316227769682
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->
+<robot name="aloha_left_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fl_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c2a80f8d972d01f8f804b67bb0ecaf5d610b1ae6
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: left_arm
+description: "ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/left_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/left_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.102Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.296999871731
+      - -0.419500023127
+      - 0.782000243664
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 0.714142563298
+      - 0.700000389985
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fl_joint1: 0
+  fl_joint2: 0
+  fl_joint3: 0
+  fl_joint4: 0
+  fl_joint5: 0
+  fl_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - left_arm
+type: joint
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..82e5122399fcdabd1635a9e5364bf634fafb6093
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_camera/profile.yaml
@@ -0,0 +1,100 @@
+name: left_camera
+runtime:
+  domain_id: 83
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: RGB image measurements with encoding, dimensions, row stride and
+      acquisition timestamp in the Image header. The frame_id identifies the calibrated
+      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current
+      frame at once, also while the world is paused, then one per camera sample; reading
+      never advances world time.
+    transport: topic
+    direction: output
+    endpoint: /left_camera/image
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: '16UC1 depth registered to image in millimetres along the optical
+      axis; 0 marks missing or out-of-range samples. Rendered with its image: same
+      stamp and frame_id, so pair the two by stamp.'
+    transport: topic
+    direction: output
+    endpoint: /left_camera/depth
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: Projection and distortion calibration for the image stream, including
+      K, D, image size and optical frame. Use the declared distortion model and raw/rectified
+      image space when projecting pixels; intrinsics are not TF transforms.
+    transport: topic
+    direction: output
+    endpoint: /left_camera/camera_info
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: false
+meta:
+  created_at: '2026-10-24T00:00:00Z'
+  updated_at: '2026-10-24T00:00:00Z'
+type: rgbd
+description: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with
+  pixel-center depth from a raster twin; calibration and the optical-frame mount on
+  the left_arm flange are in the profile.
+source: introspected
+calibrated_at: null
+method: introspection
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406656
+    vertical_deg: 37.000001358777
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
+frames:
+  base:
+    translation_m:
+    - 0.07000001149
+    - 0.031999994336
+    - 0.065000047158
+    rotation_xyzw:
+    - -0.589367966393
+    - 0.589367919412
+    - -0.390698641271
+    - 0.39069867358
+    parent: left_arm:flange
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_left_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="left_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fl_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ef650c41c0fc48675e8e2d9d742607bd757786ef
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/left_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: left_gripper
+description: "ALOHA AgileX left parallel two-finger gripper on the left_arm flange. The grip joint left_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/left_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/left_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.101Z"
+frames:
+  base:
+    parent: "left_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "left_gripper:base"
+    translation_m:
+      - 0.1199999989
+      - 7.305e-9
+      - -7.152e-9
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 3.2247e-8
+      - 0.999999998609
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: left_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - left_gripper
+type: gripper
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right ARX5 arm from arx5_description_isaac.urdf: fr_base_link..fr_link6; flange is the fr_link6 frame. -->
+<robot name="aloha_right_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fr_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fe9d075b20d466891ff66b13a2c970c75d1d6103
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: right_arm
+description: "ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/right_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/right_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.100Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - 0.306300014257
+      - -0.418499916792
+      - 0.78100001812
+    rotation_xyzw:
+      - -2.9802e-8
+      - 5.9605e-8
+      - 0.71063355368
+      - 0.703562409721
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fr_joint1: 0
+  fr_joint2: 0
+  fr_joint3: 0
+  fr_joint4: 0
+  fr_joint5: 0
+  fr_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - right_arm
+type: joint
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bec00f5bc2171f87ff0196e56d8253a628380a35
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_camera/profile.yaml
@@ -0,0 +1,100 @@
+name: right_camera
+runtime:
+  domain_id: 83
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: RGB image measurements with encoding, dimensions, row stride and
+      acquisition timestamp in the Image header. The frame_id identifies the calibrated
+      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current
+      frame at once, also while the world is paused, then one per camera sample; reading
+      never advances world time.
+    transport: topic
+    direction: output
+    endpoint: /right_camera/image
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: '16UC1 depth registered to image in millimetres along the optical
+      axis; 0 marks missing or out-of-range samples. Rendered with its image: same
+      stamp and frame_id, so pair the two by stamp.'
+    transport: topic
+    direction: output
+    endpoint: /right_camera/depth
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: Projection and distortion calibration for the image stream, including
+      K, D, image size and optical frame. Use the declared distortion model and raw/rectified
+      image space when projecting pixels; intrinsics are not TF transforms.
+    transport: topic
+    direction: output
+    endpoint: /right_camera/camera_info
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: false
+meta:
+  created_at: '2026-10-24T00:00:00Z'
+  updated_at: '2026-10-24T00:00:00Z'
+type: rgbd
+description: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with
+  pixel-center depth from a raster twin; calibration and the optical-frame mount on
+  the right_arm flange are in the profile.
+source: introspected
+calibrated_at: null
+method: introspection
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406656
+    vertical_deg: 37.000001358777
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
+frames:
+  base:
+    translation_m:
+    - 0.069999999887
+    - 0.031999976227
+    - 0.064999980711
+    rotation_xyzw:
+    - -0.58936798274
+    - 0.589367967612
+    - -0.390698619841
+    - 0.390698640561
+    parent: right_arm:flange
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right parallel gripper on the fr_link6 flange. right_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_right_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="right_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fr_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0cab3072b983f682403c63b6dd978295a69f7b49
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/right_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: right_gripper
+description: "ALOHA AgileX right parallel two-finger gripper on the right_arm flange. The grip joint right_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured right_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/right_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/right_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.103Z"
+frames:
+  base:
+    parent: "right_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "right_gripper:base"
+    translation_m:
+      - 0.119999992883
+      - -9.757e-9
+      - -7.153e-9
+    rotation_xyzw:
+      - 1e-12
+      - 2.9802e-8
+      - -6.4393e-8
+      - 0.999999973161
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: right_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - right_gripper
+type: gripper
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/world/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a7abb5c3fa2cf479a270a9945c883fb6754717f
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/devices/world/profile.yaml
@@ -0,0 +1,58 @@
+name: world
+description: Scene reference and authoritative world clock of the RoboTwin 2.0 table. Kinex supplies frame transforms independently. This device exposes no task instructions or evaluator results.
+source: default
+calibrated_at: null
+method: unknown
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: time
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  time:
+    description: Authoritative world clock
+    transport: topic
+    direction: output
+    endpoint: "/clock"
+    type: rosgraph_msgs/msg/Clock
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  wait:
+    description: "Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results."
+    transport: service
+    direction: input
+    endpoint: "/world/wait"
+    type: kinex_interfaces/srv/Wait
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 120
+  stop:
+    description: Cancel the pending wait.
+    transport: service
+    direction: input
+    endpoint: "/world/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.095Z"
+frames:
+  base:
+    parent: null
+    translation_m: null
+    rotation_xyzw: null
+bounds: null
+surfaces: []
+type: world
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/kinex.json b/experiments/robotwin-native/kinex-2/workspace/.kinex/kinex.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/kinex.json
@@ -0,0 +1,5 @@
+{
+  "python": {
+    "provider": "ros-jazzy"
+  }
+}
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/memos/dual_shoes.md b/experiments/robotwin-native/kinex-2/workspace/.kinex/memos/dual_shoes.md
new file mode 100644
index 0000000000000000000000000000000000000000..ca85b4ff3b492fd890e63309b80d57f563816459
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/memos/dual_shoes.md
@@ -0,0 +1,11 @@
+# RoboTwin dual shoes / ALOHA AgileX
+
+Public interface: read harness.md and refreshed profiles each episode; load world, both arms/grippers, head_camera. Native period 0.04 s, stepped world.wait only. Use typed kinex.communication operations. Do not reset or inspect private simulator state.
+
+Reusable tool: `.kinex/tools/dual_shoes.py` accepts world-frame TCP targets, duration/settle, normalized grip targets, and dry_run. Uses measured joint state, calibrated base transforms, 0.12 m flange-to-TCP translation, Cartesian interpolation/Slerp and continuous seeded IK. Publishes both trajectories before one sequential wait. Returns measured TCP/grips and camera. Read schemas before using. JointModel.fk/ik require list, not ROS array; camera_frame returns (image,depth).
+
+Perception: paired head depth mm -> optical coordinates using profile intrinsics -> world via camera mount. In observed scene table z=.640, box bottom .642, rim .740, inner x about [-.125,.130], y[-.215,-.045]. Shoes length ~.22 m, width .07 m. Re-measure positions each scene. Initial left shoe pointed roughly [-.435,.9005,0], right [.10,.995,0]. Grasp across width at center: downward TCP x axis; local z opposite shoe toe direction; local y=cross(z,x). Grasp TCP z=.695 worked, lift to .865. Both measured grips stalled ~.82-.84 and visual lift confirmed shoes attached (stall alone is not evidence).
+
+Place tips world -X using quaternion xyzw [0,.70710678,0,.70710678]. Near row for initially left shoe: observed target TCP [.003,-.171,.795]. Far row: [.003,-.087,.795]. Grasp offsets kept soles roughly .055 below TCP. Release just above rim: gripper fingers cannot descend to floor in narrow box without contacting walls. Shoe falls flat into box. Place sequentially; keep other held shoe high/outside, then withdraw released arm up/outward while bringing other shoe above far row. Example left clearance [-.23,-.18,.94]. Waits .3-.4 s after trajectories reduce tracking error; verify actual state. Near shoe was visibly flat, toe left, before placing far shoe.
+
+World can terminate during a release wait before requested duration. A rejected/interrupted wait is NOT proof of success. Stop and inspect final images/state; do not keep advancing terminal world. Observed terminal at t=13.48 during second release (337 frames): image showed both shoes flat inside box with tips left and initially left shoe nearer robot. Open commands had been sent to both; final openings left .9983 and right .9529. Native verdict is owner-only; do not claim it. Tool currently surfaces rejected wait as error, so final observation requires separate device reads.
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/tools/dual_shoes.py b/experiments/robotwin-native/kinex-2/workspace/.kinex/tools/dual_shoes.py
new file mode 100644
index 0000000000000000000000000000000000000000..f85bb2bce7e421f4ff42c443a19ad7f189070ebc
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/tools/dual_shoes.py
@@ -0,0 +1,54 @@
+"""Measured Cartesian dual-arm moves through public Kinex ROS operations.
+Invoke with exec file and args: targets {left/right: [x,y,z,qx,qy,qz,qw]},
+duration, settle; optional grips {left/right: opening}. World coordinates.
+A dry_run solves without publishing; each path uses measured start FK.
+"""
+import asyncio
+import numpy as np
+from scipy.spatial.transform import Rotation, Slerp
+from kinex import communication, helpers, finish, args
+
+async def state(io, device):
+    async with io.subscribe(device+'.joint_state') as stream:
+        return await stream.next(timeout_s=10)
+
+def matrix(p):
+    t=np.eye(4);t[:3,:3]=Rotation.from_quat(p[3:]).as_matrix();t[:3,3]=p[:3];return t
+
+def pose(t):
+    return [*t[:3,3],*Rotation.from_matrix(t[:3,:3]).as_quat()]
+
+async def main():
+    a=args(); duration=a.get('duration',1.2); plans={}; report={}
+    async with communication() as io:
+        for side,target in a.get('targets',{}).items():
+            dev=side+'_arm'; model=helpers.joint_model(dev)
+            measured=await state(io,dev); q=list(measured.position)
+            base=matrix((await helpers.relative_pose(io,{'frame':dev+':base'},{'frame':'world:base'}))['pose'])
+            tcp=np.eye(4);tcp[0,3]=.12
+            start=base@matrix(model.fk(q))@tcp; end=matrix(target)
+            interp=Slerp([0,1],Rotation.from_matrix(np.stack([start[:3,:3],end[:3,:3]])))
+            points=[]; max_jump=0
+            for i in range(1,a.get('segments',20)+1):
+                f=i/a.get('segments',20);t=np.eye(4);t[:3,3]=start[:3,3]*(1-f)+end[:3,3]*f;t[:3,:3]=interp(f).as_matrix()
+                goal=pose(np.linalg.inv(base)@t@np.linalg.inv(tcp))
+                qnew=model.ik(goal,q);max_jump=max(max_jump,float(np.max(np.abs(np.array(qnew)-q))));q=qnew
+                ns=round(duration*f*1e9)
+                points.append({'positions':q,'time_from_start':{'sec':ns//10**9,'nanosec':ns%10**9}})
+            if max_jump>1.0: raise ValueError(f'{side}: IK discontinuity {max_jump}')
+            plans[dev]={'joint_names':model.names,'points':points}
+            report[side]={'target_q':q,'max_segment_joint_change':max_jump}
+        if not a.get('dry_run',False):
+            for dev,payload in plans.items(): await io.publish(dev+'.joint_target',payload)
+            for side,opening in a.get('grips',{}).items(): await io.publish(side+'_gripper.target',{'data':opening})
+            report['wait']=await io.request('world.wait',duration_s=duration+a.get('settle',.2))
+            for side in ['left','right']:
+                m=await state(io,side+'_arm');g=await state(io,side+'_gripper')
+                report[side]={**report.get(side,{}),'measured_q':list(m.position),'grip':list(g.position),'tcp':await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})}
+            im,dep=await helpers.camera_frame(io,'head_camera')
+            np.savez('/tmp/head.npz',rgb=helpers.image_array(im),depth=helpers.image_array(dep))
+            image=helpers.save_image(im)
+        else: image=None
+    finish(report,file=image)
+
+if __name__=='__main__': asyncio.run(main())
diff --git a/experiments/robotwin-native/kinex-2/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/kinex-2/workspace/.kinex/workspace.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/.kinex/workspace.yaml
@@ -0,0 +1,16 @@
+identity:
+  id: robotwin-aloha-agilex
+  name: RoboTwin ALOHA AgileX
+  kind: arm
+  model: ALOHA AgileX (dual ARX5)
+  source: user
+devices:
+  source: user
+safety:
+  source: user
+  max_joint_velocity: null
+  max_cartesian_velocity: null
+  keep_out_zones: []
+meta:
+  created_at: '2026-10-24T00:00:00Z'
+  updated_at: '2026-10-24T00:00:00Z'
diff --git a/experiments/robotwin-native/kinex-2/workspace/harness.md b/experiments/robotwin-native/kinex-2/workspace/harness.md
new file mode 100644
index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/harness.md
@@ -0,0 +1,6 @@
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
diff --git a/experiments/robotwin-native/kinex-2/workspace/instruction.md b/experiments/robotwin-native/kinex-2/workspace/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c11369ff0b46fa7f1916cac5f8d4d112bd43ccf6
--- /dev/null
+++ b/experiments/robotwin-native/kinex-2/workspace/instruction.md
@@ -0,0 +1,45 @@
+# Task: robotwin/place-dual-shoes-aloha-agilex
+
+use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open
+
+## Work to do
+
+Solve the task efficiently using the running rosbridge environment. Follow the
+task instruction above, including its completion requirements. Read `harness.md`
+and the device profiles for public interfaces, units and calibrated frames.
+Reuse your saved tools, skills, and memos.
+
+## Limits
+
+You have 600 native action frames. The execution mode is `stepped`: physics is
+frozen while idle. Use `world.wait` with `duration_s` to advance the native clock.
+Joint targets are ROS JointTrajectory messages; gripper targets use the units
+declared in their profiles. Reads and command publication do not advance physics.
+Waits are sequential; concurrent waits are rejected. A command or elapsed time
+does not prove arrival; inspect measured feedback. Device stop holds the device
+without ending the episode. The safety timeout is 28800 seconds.
+Do not launch or reset a simulation.
+
+## Completion
+
+Return when finished. The owner finalizes the episode and reads its native
+verdict. An interrupted or rejected wait does not establish task success. The
+native step limit stops physics even if your requested wait is longer.
+
+## Saved resources
+
+Maintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes
+in `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated
+content. Create a new file only when no existing resource fits; do not create
+per-attempt reflections or numbered copies. These files persist across fresh
+conversations. Update them before finishing your response.
+
+## Robot and device access
+
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
+
diff --git a/experiments/robotwin-native/kinex_policy-1/artifacts.json b/experiments/robotwin-native/kinex_policy-1/artifacts.json
new file mode 100644
index 0000000000000000000000000000000000000000..87ceaf3160a0b1b8e76206dc746f4bcc8c3361e7
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/artifacts.json
@@ -0,0 +1,109 @@
+{
+  "evidence": {
+    "complete": true,
+    "path": "evidence.tar.gz"
+  },
+  "recording": {
+    "complete": true,
+    "enabled": true,
+    "encoder_queue_drops": 0,
+    "lossless": false,
+    "manifest": {
+      "bag_messages": 0,
+      "error": null,
+      "fps": 10.0,
+      "run_id": "83632f6147cc",
+      "schema": "rosbridge/recording/1",
+      "spectator_views": [
+        "third_person",
+        "left_wrist",
+        "right_wrist"
+      ],
+      "state": "complete",
+      "task": "robotwin/handover-block-aloha-agilex",
+      "topics": [],
+      "type": "mp4",
+      "views": {
+        "left_wrist": {
+          "dropped": 0,
+          "error": null,
+          "received": 211,
+          "segments": [
+            {
+              "file": "left_wrist.mp4",
+              "first_stamp_ns": 0,
+              "fps": 10.0,
+              "frames": 210,
+              "height": 720,
+              "last_stamp_ns": 20920000000,
+              "width": 960
+            }
+          ]
+        },
+        "right_wrist": {
+          "dropped": 0,
+          "error": null,
+          "received": 211,
+          "segments": [
+            {
+              "file": "right_wrist.mp4",
+              "first_stamp_ns": 0,
+              "fps": 10.0,
+              "frames": 210,
+              "height": 720,
+              "last_stamp_ns": 20920000000,
+              "width": 960
+            }
+          ]
+        },
+        "third_person": {
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+          "segments": [
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+              "height": 720,
+              "last_stamp_ns": 20920000000,
+              "width": 960
+            }
+          ]
+        }
+      }
+    },
+    "video_available": true,
+    "videos": [
+      {
+        "bytes": 2032279,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 210,
+        "path": "recording/third_person.mp4"
+      },
+      {
+        "bytes": 1390411,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 210,
+        "path": "recording/left_wrist.mp4"
+      },
+      {
+        "bytes": 1392970,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 210,
+        "path": "recording/right_wrist.mp4"
+      }
+    ]
+  },
+  "schema": "rlebench/episode-artifacts/1",
+  "terminal_evidence": {
+    "available": false,
+    "complete": false,
+    "error": "FileNotFoundError: [Errno 2] No such file or directory: '/run/rlebench/private/episode_01/evidence/terminal.json'"
+  },
+  "workspace_complete": true
+}
diff --git a/experiments/robotwin-native/kinex_policy-1/combined.mp4 b/experiments/robotwin-native/kinex_policy-1/combined.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..9f76164bd61988bc315f7d91e539ba6b5e837578
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/combined.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:172c37c4de70d5f25f67f33633b7a6642699acb3976d1899537baf3dade739d1
+size 3757569
diff --git a/experiments/robotwin-native/kinex_policy-1/detail.json b/experiments/robotwin-native/kinex_policy-1/detail.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e137b9c42ae4a6aa271efb9b822e2766ed4be40
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/detail.json
@@ -0,0 +1,1388 @@
+{
+  "instruction": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open",
+  "observation": "Kinex wrote its own one-chunk policy tool with fresh feedback. Five 16-action inferences used the complete original instruction, without subtask rewriting. It then switched to agent-written geometric control and succeeded at native frame 523. This episode shows autonomous switching; it does not establish a planning benefit from VLA.",
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+  "native_instruction": "",
+  "policy": "modified",
+  "prompt": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "prompt_template": "<!-- rlebench:resolved-episode-instruction -->\n# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "schema": "rlebench/episode-instruction/3",
+  "source": "catalog",
+  "task_id": "robotwin/handover-block-aloha-agilex",
+  "template": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open"
+}
diff --git a/experiments/robotwin-native/kinex_policy-1/media.json b/experiments/robotwin-native/kinex_policy-1/media.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5ad72c1fe3b891ef4efdd74a1f858c5a8d5c8d9
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/media.json
@@ -0,0 +1,101 @@
+{
+  "native_frames": 210,
+  "published_frames": 210,
+  "pts_verified": true,
+  "source_start_pts": 0.2,
+  "published_start_pts": 0.0,
+  "pts_offset": -0.2,
+  "terminal_frame_retained": true,
+  "views": [
+    "third_person",
+    "left_wrist",
+    "right_wrist"
+  ],
+  "fps": "10/1",
+  "native_recording": {
+    "schema": "rosbridge/recording/1",
+    "run_id": "83632f6147cc",
+    "task": "robotwin/handover-block-aloha-agilex",
+    "state": "complete",
+    "views": {
+      "third_person": {
+        "received": 211,
+        "dropped": 0,
+        "segments": [
+          {
+            "file": "third_person.mp4",
+            "frames": 210,
+            "width": 960,
+            "height": 720,
+            "fps": 10.0,
+            "first_stamp_ns": 0,
+            "last_stamp_ns": 20920000000
+          }
+        ],
+        "error": null
+      },
+      "left_wrist": {
+        "received": 211,
+        "dropped": 0,
+        "segments": [
+          {
+            "file": "left_wrist.mp4",
+            "frames": 210,
+            "width": 960,
+            "height": 720,
+            "fps": 10.0,
+            "first_stamp_ns": 0,
+            "last_stamp_ns": 20920000000
+          }
+        ],
+        "error": null
+      },
+      "right_wrist": {
+        "received": 211,
+        "dropped": 0,
+        "segments": [
+          {
+            "file": "right_wrist.mp4",
+            "frames": 210,
+            "width": 960,
+            "height": 720,
+            "fps": 10.0,
+            "first_stamp_ns": 0,
+            "last_stamp_ns": 20920000000
+          }
+        ],
+        "error": null
+      }
+    },
+    "type": "mp4",
+    "spectator_views": [
+      "third_person",
+      "left_wrist",
+      "right_wrist"
+    ],
+    "fps": 10.0,
+    "topics": [],
+    "error": null,
+    "bag_messages": 0
+  },
+  "all_frame_visual_checks": {
+    "third_person": {
+      "frames_compared": 210,
+      "mean_ssim": 0.9982162809523809,
+      "minimum_ssim": 0.997081,
+      "label_area_excluded": true
+    },
+    "left_wrist": {
+      "frames_compared": 210,
+      "mean_ssim": 0.9985995904761905,
+      "minimum_ssim": 0.997311,
+      "label_area_excluded": true
+    },
+    "right_wrist": {
+      "frames_compared": 210,
+      "mean_ssim": 0.9984424333333334,
+      "minimum_ssim": 0.996738,
+      "label_area_excluded": true
+    }
+  }
+}
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diff --git a/experiments/robotwin-native/kinex_policy-1/policy-calls.json b/experiments/robotwin-native/kinex_policy-1/policy-calls.json
new file mode 100644
index 0000000000000000000000000000000000000000..54a05ae046de0672809d40f44ba312dd379df786
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/policy-calls.json
@@ -0,0 +1,2727 @@
+[
+  {
+    "call_id": "a3451376b6fc4c218e23e7f52501133c",
+    "task": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/policy/a3451376b6fc4c218e23e7f52501133c-0.png",
+      "experiments/robotwin-native/kinex_policy-1/policy/a3451376b6fc4c218e23e7f52501133c-1.png",
+      "experiments/robotwin-native/kinex_policy-1/policy/a3451376b6fc4c218e23e7f52501133c-2.png"
+    ],
+    "input": {
+      "wall_time_ns": 1793223461567498903,
+      "event": "input",
+      "call_id": "a3451376b6fc4c218e23e7f52501133c",
+      "task": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open",
+      "obs": {
+        "images": [
+          "/workspace/artifacts/policy/a3451376b6fc4c218e23e7f52501133c-0.png",
+          "/workspace/artifacts/policy/a3451376b6fc4c218e23e7f52501133c-1.png",
+          "/workspace/artifacts/policy/a3451376b6fc4c218e23e7f52501133c-2.png"
+        ]
+      }
+    },
+    "output": {
+      "wall_time_ns": 1793223462913166759,
+      "event": "output",
+      "call_id": "a3451376b6fc4c218e23e7f52501133c",
+      "inference_s": 1.4321543741971254,
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diff --git a/experiments/robotwin-native/kinex_policy-1/policy/model.json b/experiments/robotwin-native/kinex_policy-1/policy/model.json
new file mode 100644
index 0000000000000000000000000000000000000000..c1428c70508701d16c52747e0ca2c317ca575b46
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/policy/model.json
@@ -0,0 +1,80 @@
+{
+  "checkpoint_sha256": "8ccee8ddb33fa5df83a9a5e2b7d4f737db93f19a051b23f738af8a484fe2d7e5",
+  "normalization_sha256": "00cff63dc4e97a2d65ec2033a8df9c45e09a403effd76e5d5d54bff8d191f097",
+  "base_vlm_config_sha256": "edac7703329133edfc53e46ac0081835144c99d7eebf28b71c732694d435224d",
+  "effective_config": {
+    "datasets": {
+      "vla_data": {
+        "data_mix": "robotwin",
+        "data_root_dir": "playground/Datasets/RoboTwin",
+        "dataset_py": "lerobot_datasets",
+        "image_size": [
+          224,
+          224
+        ],
+        "per_device_batch_size": 8,
+        "video_backend": "torchvision_av"
+      }
+    },
+    "framework": {
+      "action_model": {
+        "action_dim": 14,
+        "action_hidden_dim": 2560,
+        "action_model_type": "DiT-B",
+        "future_action_window_size": 15,
+        "past_action_window_size": 0,
+        "action_horizon": 16
+      },
+      "name": "QwenOFT",
+      "qwenvl": {
+        "base_vlm": "/models/Qwen3-VL-4B-Instruct",
+        "attn_implementation": "sdpa"
+      }
+    },
+    "output_dir": "./results/Checkpoints/1208_robotwin_QwenOFT_state_qwen3",
+    "run_id": "1208_robotwin_QwenOFT_state_qwen3",
+    "run_root_dir": "./results/Checkpoints",
+    "seed": 42,
+    "trainer": {
+      "eval_interval": 100,
+      "freeze_modules": true,
+      "gradient_accumulation_steps": 1,
+      "gradient_clipping": 1.0,
+      "is_resume": true,
+      "learning_rate": {
+        "action_model": 0.0001,
+        "base": 3e-05,
+        "qwen_vl_interface": 1e-05
+      },
+      "logging_frequency": 50,
+      "lr_scheduler_type": "cosine_with_min_lr",
+      "max_train_steps": 100000,
+      "num_warmup_steps": 5000,
+      "optimizer": {
+        "betas": [
+          0.9,
+          0.95
+        ],
+        "eps": 1e-08,
+        "weight_decay": 1e-08
+      },
+      "save_interval": 10000,
+      "scheduler_specific_kwargs": {
+        "min_lr": 5e-07
+      }
+    },
+    "wandb_entity": "jinhuiye",
+    "wandb_project": "StarVLA_Robotwin",
+    "version_id": "0.21"
+  },
+  "unnorm_key": "new_embodiment",
+  "image_size": [
+    224,
+    224
+  ],
+  "cameras": 3,
+  "action_horizon": 16,
+  "action_dimension": 14,
+  "embodiment": "robotwin",
+  "state_input": false
+}
diff --git a/experiments/robotwin-native/kinex_policy-1/poster.jpg b/experiments/robotwin-native/kinex_policy-1/poster.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c4c860930a221987584eed9f4f5568a7569f2fd7
Binary files /dev/null and b/experiments/robotwin-native/kinex_policy-1/poster.jpg differ
diff --git a/experiments/robotwin-native/kinex_policy-1/provider-requests.jsonl b/experiments/robotwin-native/kinex_policy-1/provider-requests.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e50b1175febeb1eb7d46da42eb94af8b5ba80e0c
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/provider-requests.jsonl
@@ -0,0 +1,241 @@
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diff --git a/experiments/robotwin-native/kinex_policy-1/session.json b/experiments/robotwin-native/kinex_policy-1/session.json
new file mode 100644
index 0000000000000000000000000000000000000000..6e9d46a0d51ecffb7e55f2d1bebb284f9242230e
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/session.json
@@ -0,0 +1,804 @@
+[
+  {
+    "role": "system",
+    "name": null,
+    "text": "",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:05.503Z"
+  },
+  {
+    "role": "user",
+    "name": null,
+    "text": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:05.503Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: ls\n{\"path\": \"/workspace\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:08.068Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "ls",
+    "text": ".kinex/\nharness.md\ninstruction.md",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:08.076Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \"/workspace/harness.md\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:12.659Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:12.669Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: find\n{\"pattern\": \"**/*\", \"path\": \"/workspace/.kinex\", \"limit\": 200}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:14.751Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "find",
+    "text": 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ex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/PKG-INFO\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/SOURCES.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/dependency_links.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/top_level.txt\npython/ros/install/kinex_interfaces/share/\npython/ros/install/kinex_interfaces/share/ament_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/kinex_interfaces\npython/ros/install/kinex_interfaces/share/colcon-core/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/kinex_interfaces/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_dependencies-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_include_directories-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_libraries-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_targets-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_pyExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_pyExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfacesConfig-version.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfacesConfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_generator_py-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_aggregate_target-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_export_typesupport_libraries-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_export_typesupport_targets-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/ament_prefix_path.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/ament_prefix_path.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/path.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/path.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/pythonpath.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.ps1\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.xml\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.srv\npython/ros/install/local_setup.bash\npython/ros/install/local_setup.sh\npython/ros/install/local_setup.zsh\npython/ros/install/setup.ps1\npython/ros/install/setup.sh\npython/ros-ready.json\nskills/\ntools/\n\n[200 results limit reached. Use limit=400 for more, or refine pattern]",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:14.775Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: bash\n{\"command\": \"ls .kinex/tools .kinex/skills .kinex/memos .kinex/devices; find .kinex/devices -name profile.yaml\", \"timeout\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:18.258Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "bash",
+    "text": ".kinex/devices:\nhead_camera\nleft_arm\nleft_camera\nleft_gripper\npolicy\nright_arm\nright_camera\nright_gripper\nworld\n\n.kinex/memos:\n\n.kinex/skills:\n\n.kinex/tools:\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/policy/profile.yaml\n.kinex/devices/world/profile.yaml\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:18.278Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \".kinex/workspace.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/world/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/policy/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/head_camera/profile.yaml\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.892Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n  in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n  absolute ALOHA joint actions. Use the native run_policy tool with command order\n  left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n  one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n  created_at: '2026-10-28T21:34:56.980232+00:00'\n  updated_at: '2026-10-28T21:34:56.980232+00:00'\ncompute:\n  index: 0\n  kind: cuda\nartifacts: []\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: node.py\n    factory: create\n  overlays:\n  - env.sh\n  ready_timeout_s: 600\noperations:\n  infer:\n    description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n      RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n      left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n      +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n    transport: service\n    direction: input\n    endpoint: /kinex/policy/infer\n    type: kinex_interfaces/srv/Json\n    timeout_s: 120\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:22.899Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \".kinex/devices/left_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:26.256Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: left_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /left_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.07000001149\n    - 0.031999994336\n    - 0.065000047158\n    rotation_xyzw:\n    - -0.589367966393\n    - 0.589367919412\n    - -0.390698641271\n    - 0.39069867358\n    parent: left_arm:flange\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:26.260Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "name: right_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /right_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.069999999887\n    - 0.031999976227\n    - 0.064999980711\n    rotation_xyzw:\n    - -0.58936798274\n    - 0.589367967612\n    - -0.390698619841\n    - 0.390698640561\n    parent: right_arm:flange\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:26.260Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:26.260Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"world\", \"action\": \"load\"}, {\"deviceID\": \"left_arm\", \"action\": \"load\"}, {\"deviceID\": \"right_arm\", \"action\": \"load\"}, {\"deviceID\": \"left_gripper\", \"action\": \"load\"}, {\"deviceID\": \"right_gripper\", \"action\": \"load\"}, {\"deviceID\": \"head_camera\", \"action\": \"load\"}, {\"deviceID\": \"left_camera\", \"action\": \"load\"}, {\"deviceID\": \"right_camera\", \"action\": \"load\"}, {\"deviceID\": \"policy\", \"action\": \"load\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:36:29.297Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:17.921Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"inspect operation schemas\", \"python\": \"from kinex import runtime, finish\\nfinish({x:runtime.describe(x) for x in ['policy.infer','left_arm.joint_target','left_gripper.target','world.wait','head_camera.image']})\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:21.075Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"policy.infer\": {\"description\":\"Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/kinex/policy/infer\",\"type\":\"kinex_interfaces/srv/Json\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"request\":\"string\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"response\":\"string\"},\"definitions\":{}},\n    \"left_arm.joint_target\": {\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\n    \"left_gripper.target\": {\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\n    \"world.wait\": {\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}},\n    \"head_camera.image\": {\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"height\":\"uint32\",\"width\":\"uint32\",\"encoding\":\"string\",\"is_bigendian\":\"uint8\",\"step\":\"uint32\",\"data\":\"sequence<uint8>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:22.513Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Planning reusable script**\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py\", \"offset\": 1, \"limit\": 240}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:26.674Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n                     max_actions=None, inference_args=None):\n    \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n    model serves a Json \"infer\" operation taking obs (and task) and returning\n    actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n    entries in flat-vector split order; named action maps use device names.\n    space \"device\" takes absolute joint values in URDF order, or end-effector\n    values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n    \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n    tip_link, frozen at the observation stamp. max_actions caps the horizon and\n    action_hz paces actions on the world clock. Joint trajectories submit\n    asynchronously; end-effector targets are paced by world waits before\n    returning. Returns actions_submitted, duration_s and ik_failure\n    (device, action_index, error) or None.\n\n    Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n    \"\"\"\n    import asyncio\n    from kinex.communication import duration\n    from kinex.helpers.frames import IDENTITY, transform_pose\n    from kinex.lib.geometry import invert_rt, rt_to_pose\n    plans = [_commanded(entry) for entry in command]\n    if len({entry[\"name\"] for entry in plans}) != len(plans):\n        raise ValueError(\"command must name each device once\")\n    wait = f\"{world}.wait\"\n    if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n        raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n    options = inference_args or {}\n    if set(options) & {\"obs\", \"task\"}:\n        raise ValueError(\"inference_args cannot override obs or task\")\n    data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n    actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n    if not actions:\n        raise ValueError(\"infer must return nonempty actions\")\n    actions = actions[:max_actions or len(actions)]\n    split = [_share(action, plans) for action in actions]\n    count, failure, sequences = len(split), None, {}\n    for entry in plans:\n        name, spec = entry[\"name\"], entry[\"spec\"]\n        samples = [item[name] for item in split[:count]]\n        if spec.get(\"space\", \"device\") != \"device\":\n            stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n            try:\n                state = await stream.next(timeout_s=10)\n            finally:\n                stream.close()\n            joints = entry[\"model\"]\n            seed = measured_positions(state, joints.joints)\n            base = f\"{name}/base\"\n            ik_frame = f\"{name}/{joints.tip}\"\n            target_frame = spec.get(\"target_frame\", ik_frame)\n            reference = spec.get(\"frame\", base)\n            # target_frame is an attached tool frame. Freeze its offset at the\n            # observation stamp while resolving the policy's Cartesian chunk;\n            # URDF FK of the measured joints places the tip.\n            offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n            tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n            current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n            converted = []\n            for sample in samples:\n                if spec[\"space\"] == \"pose_delta\":\n                    value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n                    current = value[\"translation\"] + value[\"rotation\"]\n                else:\n                    current = sample\n                ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n                goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n                try:\n                    seed = joints.ik(goal, seed)\n                except ValueError as error:\n                    failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n                    break\n                converted.append(seed)\n            samples = converted\n            count = min(count, len(samples))\n        sequences[name] = samples\n    if count == 0:\n        return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n    seconds = count / number(action_hz, \"action_hz\")\n    prepared, scalars = [], []\n    for entry in plans:\n        name, prof = entry[\"name\"], entry[\"profile\"]\n        samples = sequences[name][:count]\n        if entry[\"model\"]:\n            joint_command(prof, samples, entry[\"model\"].joints)\n            fields = {\"joint_names\": entry[\"model\"].names,\n                      \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n                                 for index, value in enumerate(samples)]}\n            operation = f\"{name}.joint_target\"\n        else:\n            target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n            targets = [target(prof, value[0]) for value in samples]\n            fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n            scalars.append((operation, targets))\n        prepared.append((operation, fields))\n\n    await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n    # End-effector targets persist. Timing belongs to this workflow and the world,\n    # never a device-side duration or a background worker.\n    if scalars:\n        for index in range(count):\n            if index:\n                await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n                                       for operation, values in scalars))\n            await io.request(wait, duration_s=seconds / count)\n    return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n    \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n    name = entry[\"device\"]\n    prof = profile(name)\n    kind = prof.get(\"type\")\n    if kind not in END_EFFECTORS | {\"joint\"}:\n        raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n    model = None if kind in END_EFFECTORS else JointModel(prof)\n    # A gripper or suction tool takes one command value whatever it reports back.\n    width = _cartesian_width(entry, prof)\n    return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n            \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n    \"\"\"One action, split across the commanded devices in order.\"\"\"\n    if isinstance(action, dict):\n        missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n        if missing:\n            raise ValueError(f\"action names no value for {', '.join(missing)}\")\n        return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n    vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n    shares, cursor = {}, 0\n    for entry in plan:\n        shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n        cursor += entry[\"width\"]\n    return shares\n\n\ndef _cartesian_width(entry, prof):\n    space = entry.get(\"space\", \"device\")\n    if space not in {\"device\", \"pose\", \"pose_delta\"}:\n        raise ValueError(f\"unknown policy space: {space}\")\n    if space == \"device\":\n        if \"frame\" in entry or \"target_frame\" in entry:\n            raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n        return None\n    if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n        raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n    return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n    \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n    action = vector(action, 6, \"pose_delta\")\n    angle = math.hypot(*action[3:])\n    scale = math.sin(angle / 2) / angle if angle else 0.5\n    rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n    return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n    value = number(value, \"action\")\n    if not -1 <= value <= 1:\n        raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n    return value\n\n\ndef _grip(profile, value):\n    \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n    grip = profile[\"grip\"]\n    if not isinstance(grip, dict):\n        raise ValueError(\"the gripper has no declared grip joint\")\n    return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n    \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n    value = _action(value)\n    if profile[\"vacuum\"] == \"binary\":\n        return 1.0 if value > 0 else 0.0\n    if profile[\"vacuum\"] != \"proportional\":\n        raise ValueError(\"the suction device declares no vacuum control\")\n    return (value + 1) / 2\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:26.679Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: device\n{\"deviceID\": \"head_camera\", \"action\": \"image\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:29.087Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "device",
+    "text": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-orrg2pv4.png\"\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/0d916554-1.png"
+    ],
+    "timestamp": "2026-10-28T21:37:29.293Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: write\n{\"path\": \".kinex/tools/handover_policy.py\", \"content\": \"\\\"\\\"\\\"One bounded policy chunk followed by fresh measured feedback. Devices must be loaded.\\\"\\\"\\\"\\nimport asyncio\\nfrom kinex import communication, helpers, args, finish\\n\\nTASK = 'use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open'\\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\\n\\nasync def main():\\n    opts = args()\\n    async with communication() as io:\\n        images = []\\n        for camera in ['head_camera', 'left_camera', 'right_camera']:\\n            with io.subscribe(camera + '.image', depth=1) as stream:\\n                image = await stream.next(timeout_s=10)\\n            images.append(helpers.save_image(image))\\n        result = await helpers.run_policy(io, 'policy', {'images': images},\\n            [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\\n            action_hz=25, max_actions=opts.get('max_actions', 32))\\n        states = {}\\n        for device in DEVICES:\\n            with io.subscribe(device + '.joint_state', depth=1) as stream:\\n                state = await stream.next(timeout_s=10)\\n            states[device] = {'position': list(state.position), 'stamp': state.header.stamp}\\n        with io.subscribe('head_camera.image', depth=1) as stream:\\n            image = await stream.next(timeout_s=10)\\n        path = helpers.save_image(image)\\n    finish({'execution': result, 'measured': states}, file=path)\\n\\nasyncio.run(main())\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:36.926Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/tools/handover_policy.py",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:36.929Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"grasp approach policy\", \"file\": \".kinex/tools/handover_policy.py\", \"timeout_s\": 180}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:39.608Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223460.918079201] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.000841967121232301,0.5760459899902344,0.5073635578155518,0.11193818598985672,0.38478362560272217,0.011398900300264359],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"left_gripper\":{\"position\":[1.0020332282239741],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_arm\":{\"position\":[-0.003331375541165471,0.00015323919069487602,0.004788832738995552,-0.0013015313306823373,-0.0026872919406741858,-0.002288535935804248],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_gripper\":{\"position\":[0.9922204071825201],\"stamp\":{\"sec\":0,\"nanosec\":640000000}}},\n    \"file\": \"/tmp/kinex-observation-vu2mzt27.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/b5bff240-1.png"
+    ],
+    "timestamp": "2026-10-28T21:37:45.442Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"approach red block\", \"file\": \".kinex/tools/handover_policy.py\", \"timeout_s\": 180}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:47.746Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223469.135563916] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04086500033736229,1.74478018283844,1.4346884489059448,0.307611346244812,1.0182968378067017,0.03287385031580925],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"left_gripper\":{\"position\":[0.9985552294687792],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_arm\":{\"position\":[0.012346736155450344,0.08589094132184982,0.07706005871295929,-0.05403941124677658,-0.005238042213022709,-0.005991658661514521],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_gripper\":{\"position\":[0.9958593384786086],\"stamp\":{\"sec\":1,\"nanosec\":280000000}}},\n    \"file\": \"/tmp/kinex-observation-n_o6e3b9.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/94f007e4-1.png"
+    ],
+    "timestamp": "2026-10-28T21:37:53.297Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"align left grasp\", \"file\": \".kinex/tools/handover_policy.py\", \"timeout_s\": 180}",
+    "images": [],
+    "timestamp": "2026-10-28T21:37:55.907Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223477.284126808] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.058308765292167664,1.929189920425415,1.599496841430664,0.3592958450317383,1.0783706903457642,-0.016372166574001312],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"left_gripper\":{\"position\":[1.0109648487784646],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_arm\":{\"position\":[0.09248543530702591,0.4811040759086609,0.44249579310417175,-0.3714327812194824,0.0018695585895329714,-0.09739844501018524],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_gripper\":{\"position\":[0.9959367567842657],\"stamp\":{\"sec\":1,\"nanosec\":920000000}}},\n    \"file\": \"/tmp/kinex-observation-u4ddnv07.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/366115a6-1.png"
+    ],
+    "timestamp": "2026-10-28T21:38:01.032Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Checking camera framing**\nTool call: exec\n{\"description\": \"close left grasp\", \"file\": \".kinex/tools/handover_policy.py\", \"timeout_s\": 180}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:05.713Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223486.950859814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04435889795422554,1.231294870376587,1.0781995058059692,0.17081087827682495,0.6730543971061707,-0.069943368434906],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"left_gripper\":{\"position\":[1.0099254927851937],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_arm\":{\"position\":[0.2739063501358032,1.267305612564087,1.2323172092437744,-0.9213956594467163,0.01157537754625082,-0.31220439076423645],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_gripper\":{\"position\":[0.9980137077244846],\"stamp\":{\"sec\":2,\"nanosec\":560000000}}},\n    \"file\": \"/tmp/kinex-observation-2otm1ktx.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/3da59974-1.png"
+    ],
+    "timestamp": "2026-10-28T21:38:10.478Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "\nTool call: devices\n{\"calls\": [{\"deviceID\": \"left_camera\", \"action\": \"image\"}, {\"deviceID\": \"right_camera\", \"action\": \"image\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:15.957Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-z3rv78oi.png\"},\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dch2bguz.png\"}\n  ]\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/ec2f180d-1.png",
+      "experiments/robotwin-native/kinex_policy-1/observations/ec2f180d-2.png"
+    ],
+    "timestamp": "2026-10-28T21:38:16.215Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"reobserve and infer handover\", \"file\": \".kinex/tools/handover_policy.py\", \"timeout_s\": 180}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:18.646Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223500.049809102] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.0294959656894207,0.6043052077293396,0.5100125670433044,0.10033784061670303,0.33127984404563904,-0.05462042614817619],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"left_gripper\":{\"position\":[1.004523279992017],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_arm\":{\"position\":[0.36748456954956055,1.8742890357971191,1.8310723304748535,-1.2713841199874878,-0.005831398069858551,-0.4040740430355072],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_gripper\":{\"position\":[1.0009333870627664],\"stamp\":{\"sec\":3,\"nanosec\":200000000}}},\n    \"file\": \"/tmp/kinex-observation-mpnweq9k.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/e3468321-1.png"
+    ],
+    "timestamp": "2026-10-28T21:38:23.371Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Checking red block geometry**\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_arm/arm.urdf\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/gripper.urdf\", \"offset\": null, \"limit\": null}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:30.304Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:30.307Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->\n<robot name=\"aloha_left_arm\">\n  <link name=\"base_link\"/>\n  <link name=\"link1\"/>\n  <link name=\"link2\"/>\n  <link name=\"link3\"/>\n  <link name=\"link4\"/>\n  <link name=\"link5\"/>\n  <link name=\"flange\"/>\n  <joint name=\"fl_joint1\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"link1\"/>\n    <origin xyz=\"0 0 0.058\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint2\" type=\"revolute\">\n    <parent link=\"link1\"/>\n    <child link=\"link2\"/>\n    <origin xyz=\"0.025013 0.00060169 0.042\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint3\" type=\"revolute\">\n    <parent link=\"link2\"/>\n    <child link=\"link3\"/>\n    <origin xyz=\"-0.26396 0.0044548 0\" rpy=\"3.14158530718 0 -0.015928\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint4\" type=\"revolute\">\n    <parent link=\"link3\"/>\n    <child link=\"link4\"/>\n    <origin xyz=\"0.246 -0.00025 -0.06\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint5\" type=\"revolute\">\n    <parent link=\"link4\"/>\n    <child link=\"link5\"/>\n    <origin xyz=\"0.06775 0.0015 -0.0855\" rpy=\"0 0 -0.015928\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint6\" type=\"revolute\">\n    <parent link=\"link5\"/>\n    <child link=\"flange\"/>\n    <origin xyz=\"0.03095 0 0.0855\" rpy=\"3.14158530718 0 0\"/>\n    <axis xyz=\"1 0 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n</robot>\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:30.308Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:30.308Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Reconstructing red-block depth**\nTool call: exec\n{\"description\": \"measure scene geometry\", \"python\": \"import asyncio, numpy as np\\nfrom kinex import communication, helpers, finish\\nasync def main():\\n async with communication() as io:\\n  f=await helpers.camera_frame(io,'head_camera')\\n  finish({'frame_type':str(type(f)), 'keys':list(f) if isinstance(f,dict) else None})\\nasyncio.run(main())\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:37.550Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223518.853304393] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"frame_type\": \"<class 'tuple'>\",\n    \"keys\": null\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:39.626Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py\", \"offset\": 1, \"limit\": 170}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.954Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "read",
+    "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:41.959Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"locate block and pad\", \"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation\\nfrom kinex import communication, helpers,finish,profile\\nasync def main():\\n async with communication() as io:\\n  im,dep=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); d=helpers.image_array(dep)*.001\\n  p=profile('head_camera'); fr=p['frames']['base']; r=Rotation.from_quat(fr['rotation_xyzw']); out={}\\n  for name,mask in [('red',(a[:,:,0]>150)&(a[:,:,1]<80)&(a[:,:,2]<80)),('blue',(a[:,:,2]>150)&(a[:,:,0]<80)&(a[:,:,1]<80))]:\\n   v,u=np.where(mask & (d>0)); z=d[v,u]; xyz=np.array([(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z]).T; w=r.apply(xyz)+fr['translation_m']\\n   out[name]={'bounds': [w.min(0),w.max(0)],'median':np.median(w,0),'pixels':[u.min(),v.min(),u.max(),v.max()]}\\n  for arm in ['left','right']:\\n   out[arm]=await helpers.relative_pose(io,{'frame':arm+'_gripper:tcp'},{'frame':'world:base'})\\n finish(out)\\nasyncio.run(main())\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:49.163Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223531.315992288] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"red\": {\"bounds\":[[-0.1951807511227014,0.1619458755140133,0.7403946746887228],[-0.13826190259151744,0.17530309445014303,0.9257439697592391]],\"median\":[-0.16744837463208706,0.16852240987786182,0.8441180625125884],\"pixels\":[77,0,115,67]},\n    \"blue\": {\"bounds\":[[0.0630836271454373,0.10104295310740785,0.7421885613696891],[0.16190775280083636,0.19940375685842826,0.746340314718351]],\"median\":[0.11238624941640478,0.14487610727741018,0.7460899470946761],\"pixels\":[199,55,244,86]},\n    \"left\": {\"pose\":[-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\"timestamp\":3.2},\n    \"right\": {\"pose\":[0.15039597241518135,-0.04177619542231542,0.9706620944772414,-0.5838925600381027,0.20359696955881887,0.7134881688183462,0.32947289004538605],\"timestamp\":3.2}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:38:51.990Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Positioning for tall block**\n\n**Aligning tool for tall block**\nI’m using the camera geometry to guide the grasp and handoff, checking the measured pose after each move.\nTool call: write\n{\"path\": \".kinex/tools/cartesian_move.py\", \"content\": \"\\\"\\\"\\\"Bounded world-frame TCP motion with interpolated IK and measured endpoint reporting.\\\"\\\"\\\"\\nimport asyncio\\nimport numpy as np\\nfrom scipy.spatial.transform import Rotation, Slerp\\nfrom kinex import communication, helpers, args, finish, profile\\nfrom kinex.communication import duration\\n\\nasync def main():\\n    o=args(); arm=o.get('arm','left_arm'); side=arm.split('_')[0]; grip=side+'_gripper'\\n    async with communication() as io:\\n        model=helpers.joint_model(arm)\\n        with io.subscribe(arm+'.joint_state',depth=1) as stream:\\n            state=await stream.next(timeout_s=10)\\n        q=list(state.position)\\n        tcp=(await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'}))['pose']\\n        base=profile(arm)['frames']['base']; rb=Rotation.from_quat(base['rotation_xyzw']); tb=np.array(base['translation_m'])\\n        target=o.get('pose',tcp); seconds=o.get('duration_s',1.6)\\n        n=max(1,int(seconds/.04)); rots=Slerp([0,1],Rotation.from_quat([tcp[3:],target[3:]])); points=[]\\n        for i in range(1,n+1):\\n            f=i/n; r=rots(f); pos=np.array(tcp[:3])*(1-f)+np.array(target[:3])*f\\n            flange=pos-r.apply([.12,0,0]); local=rb.inv().apply(flange-tb); rot=rb.inv()*r\\n            q=model.ik([*local,*rot.as_quat()],q)\\n            points.append({'positions':q,'time_from_start':duration(i*seconds/n)})\\n        if o.get('dry_run'):\\n            finish({'start':tcp,'target':target,'q':q}); return\\n        await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\\n        if 'grip' in o: await io.publish(grip+'.target',{'data':o['grip']})\\n        wait=await io.request('world.wait',duration_s=seconds+o.get('settle_s',.16))\\n        measured={}\\n        for device in [arm,grip]:\\n            with io.subscribe(device+'.joint_state',depth=1) as stream: s=await stream.next(timeout_s=10)\\n            measured[device]=list(s.position)\\n        actual=await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'})\\n        with io.subscribe('head_camera.image',depth=1) as stream: im=await stream.next(timeout_s=10)\\n        image=helpers.save_image(im)\\n    finish({'wait':wait,'measured':measured,'tcp':actual},file=image)\\nasyncio.run(main())\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:20.244Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/tools/cartesian_move.py",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:20.248Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"plan left block approach\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"pose\": [-0.167, 0.09, 0.86, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 2, \"dry_run\": true}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:23.965Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223565.977362546] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"start\": [-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\n    \"target\": [-0.167,0.09,0.86,0,0,0.70710678,0.70710678],\n    \"q\": [-0.37572560008508377,2.272537219262441,0.9478505586577836,1.3291237834325125,-0.32947784190081975,0.012933085784625174]\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:26.765Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"left pregrasp motion\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"pose\": [-0.167, 0.09, 0.86, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 2, \"grip\": 1}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:29.294Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223571.307316769] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":5.36,\"waited_s\":2.16},\n    \"measured\": {\"left_arm\":[-0.36332982778549194,2.2126855850219727,0.8944761157035828,1.319221019744873,-0.30946946144104004,0.010132407769560814],\"left_gripper\":[1.0015521916476162]},\n    \"tcp\": {\"pose\":[-0.1736376085877378,0.08114210634963587,0.8698353295464993,-0.0019216401989559503,0.00033106205038283427,0.7041296202769071,0.7100687822637771],\"timestamp\":5.36},\n    \"file\": \"/tmp/kinex-observation-isoz5fmq.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/aec57b8e-1.png"
+    ],
+    "timestamp": "2026-10-28T21:39:39.161Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Approaching surface**\nTool call: exec\n{\"description\": \"left grasp insertion\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"pose\": [-0.166, 0.19, 0.86, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 1.2, \"grip\": 1, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:44.115Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223586.167183748] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.8,\"waited_s\":1.4399999999999995},\n    \"measured\": {\"left_arm\":[-0.3176051676273346,2.466261625289917,1.4066729545593262,1.0674532651901245,-0.2630546987056732,0.010167332366108894],\"left_gripper\":[1.0017503770914944]},\n    \"tcp\": {\"pose\":[-0.16256753019454037,0.1836445965947115,0.8630782542882497,0.0011261512119247997,-0.002236338043285821,0.7049367356530278,0.7092657677503246],\"timestamp\":6.8},\n    \"file\": \"/tmp/kinex-observation-1254cd4e.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/c76830d5-1.png"
+    ],
+    "timestamp": "2026-10-28T21:39:52.093Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"secure left grasp\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"duration_s\": 0.4, \"grip\": 0, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:39:54.483Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223596.492892278] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":7.44,\"waited_s\":0.6400000000000006},\n    \"measured\": {\"left_arm\":[-0.3172675371170044,2.46722412109375,1.408494234085083,1.067131757736206,-0.2634282112121582,0.011745059862732887],\"left_gripper\":[0.7420821664008228]},\n    \"tcp\": {\"pose\":[-0.16274491039040148,0.18399577706677667,0.8631423899018195,0.0018189100348115054,-0.0019111422555692988,0.7051953688926306,0.70900813168394],\"timestamp\":7.44},\n    \"file\": \"/tmp/kinex-observation-oh4rbjpz.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/bbad1c11-1.png"
+    ],
+    "timestamp": "2026-10-28T21:39:59.308Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Adjusting grip on block**\nTool call: exec\n{\"description\": \"lift red block with left arm\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"pose\": [-0.163, 0.135, 0.98, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 1.2, \"grip\": 0, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:04.453Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223606.394584128] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.88,\"waited_s\":1.4400000000000004},\n    \"measured\": {\"left_arm\":[-0.341536283493042,2.0650742053985596,1.2379626035690308,0.8357267379760742,-0.3042803704738617,0.016574207693338394],\"left_gripper\":[0.7436837025664069]},\n    \"tcp\": {\"pose\":[-0.1662269352506661,0.13941764966077147,0.975097409892858,0.0015167229424354568,-0.0011079411215326478,0.7093387285919696,0.7048652638182104],\"timestamp\":8.88},\n    \"file\": \"/tmp/kinex-observation-1azh43uv.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/d32290f8-1.png"
+    ],
+    "timestamp": "2026-10-28T21:40:12.889Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Positioning block for handoff**\n\n**Reconstructing block depth**\nTool call: exec\n{\"description\": \"stage handover\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"pose\": [-0.03, 0.09, 1, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 1.6, \"grip\": 0, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:24.457Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223626.588877790] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"left_arm\":[-0.6534572243690491,2.0685434341430664,1.3600218296051025,0.7217471599578857,-0.6136956214904785,0.018981793895363808],\"left_gripper\":[0.7409415163777091]},\n    \"tcp\": {\"pose\":[-0.0379849155451224,0.09244707613302833,1.0006879962717734,0.0008205284096499977,-0.0008811871467054494,0.7084815094335674,0.7057283479024175],\"timestamp\":10.72},\n    \"file\": \"/tmp/kinex-observation-pxoki3te.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/af7f1e72-1.png"
+    ],
+    "timestamp": "2026-10-28T21:40:37.393Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Positioning for red-block grip**\n\n**Approaching red block**\nTool call: exec\n{\"description\": \"right handover pregrasp\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [0.085, 0.107, 0.923, 0, 0, 1, 0], \"duration_s\": 1.4, \"grip\": 1, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:40:50.677Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223652.453391092] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":12.36,\"waited_s\":1.6399999999999988},\n    \"measured\": {\"right_arm\":[0.12191265821456909,2.4633967876434326,1.7892687320709229,0.5763887166976929,-1.372113585472107,-0.07960568368434906],\"right_gripper\":[1.0065756440162659]},\n    \"tcp\": {\"pose\":[0.07980928558945952,0.10938215926913314,0.9291508280392765,-0.013642548475539562,0.007638461258456428,0.9996989541629445,0.018908617776310862],\"timestamp\":12.36},\n    \"file\": \"/tmp/kinex-observation-hwvop_qr.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/7853fc63-1.png"
+    ],
+    "timestamp": "2026-10-28T21:41:01.323Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Locating right-wrist camera views**\nTool call: devices\n{\"calls\": [{\"deviceID\": \"right_camera\", \"action\": \"image\"}, {\"deviceID\": \"left_camera\", \"action\": \"image\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:06.996Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-w35ecyf0.png\"},\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-ug63nito.png\"}\n  ]\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/72608f9f-1.png",
+      "experiments/robotwin-native/kinex_policy-1/observations/72608f9f-2.png"
+    ],
+    "timestamp": "2026-10-28T21:41:07.437Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Estimating red shape geometry**\nTool call: write\n{\"path\": \".kinex/tools/color_geometry.py\", \"content\": \"\\\"\\\"\\\"World-coordinate visible red/blue surface bounds from calibrated RGB-D.\\\"\\\"\\\"\\nimport asyncio\\nimport numpy as np\\nfrom scipy.spatial.transform import Rotation\\nfrom kinex import communication, helpers, args, finish, profile\\nasync def main():\\n    out={}\\n    async with communication() as io:\\n        for camera in args().get('cameras',['head_camera','right_camera']):\\n            im,depth=await helpers.camera_frame(io,camera)\\n            a=helpers.image_array(im); d=helpers.image_array(depth)*.001\\n            t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})\\n            pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']\\n            objects={}\\n            for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:\\n                v,u=np.where(mask&(d>0))\\n                if len(u)<5: continue\\n                z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T\\n                w=r.apply(xyz)+pose[:3]\\n                objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}\\n            out[camera]=objects\\n    finish(out)\\nasyncio.run(main())\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:16.372Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/tools/color_geometry.py",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:16.374Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"measure handover block position\", \"file\": \".kinex/tools/color_geometry.py\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:18.616Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223680.822701747] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.10322698296577604,-0.1875178935356495,1.0550687837932198],[-0.0966152118942117,-0.1785267883024514,1.0657978427499863]],\"median\":[-0.09990612428885612,-0.1834831016874116,1.0601774081920037],\"n\":25},\"blue\":{\"bounds\":[[0.06406859215696727,0.10104295310802192,0.742188561370138],[0.1609876156378477,0.1994037568596596,0.7463403147189582]],\"median\":[0.1124782631343093,0.14487610727830103,0.7460899470953907],\"n\":1351}},\n    \"right_camera\": {\"red\":{\"bounds\":[[-0.00525335309941832,0.0782190030395281,0.8791501126820411],[-0.00408828743245594,0.13725637290786735,0.9805794102735174]],\"median\":[-0.004694299697502735,0.1079008704839761,0.9400540789204228],\"n\":33545}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:21.488Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Estimating block grasp point**\nTool call: exec\n{\"description\": \"right handover insertion\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [-0.035, 0.108, 0.923, 0, 0, 1, 0], \"duration_s\": 1.2, \"grip\": 1, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:30.364Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223692.217737030] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.8,\"waited_s\":1.4400000000000013},\n    \"measured\": {\"right_arm\":[0.29595911502838135,2.612133264541626,2.009915828704834,0.6211190819740295,-1.2166216373443604,0.020030206069350243],\"right_gripper\":[1.002331251447851]},\n    \"tcp\": {\"pose\":[-0.019422260019086257,0.11349012460167912,0.9254862889018658,-0.000666584526104731,-0.00020513332498325504,0.999957096173557,0.009236849979105784],\"timestamp\":13.8},\n    \"file\": \"/tmp/kinex-observation-thj50dm8.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/0a0c9ded-1.png"
+    ],
+    "timestamp": "2026-10-28T21:41:42.617Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Checking right-camera clearance**\nTool call: exec\n{\"description\": \"secure receiving grasp\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [-0.035, 0.108, 0.923, 0, 0, 1, 0], \"duration_s\": 0.6, \"grip\": 0, \"settle_s\": 0.32}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:41:50.484Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223712.480365349] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":14.72,\"waited_s\":0.9199999999999999},\n    \"measured\": {\"right_arm\":[0.30162709951400757,2.6209945678710938,2.024981737136841,0.6180281639099121,-1.2083321809768677,0.0248067956417799],\"right_gripper\":[0.7636807560920715]},\n    \"tcp\": {\"pose\":[-0.02298989152961234,0.11449383778764294,0.9253460541083371,0.000013929186817855871,0.0007279375365004557,0.9999446098693109,0.01049986220064153],\"timestamp\":14.72},\n    \"file\": \"/tmp/kinex-observation-lm02c6ip.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/4bdccae4-1.png"
+    ],
+    "timestamp": "2026-10-28T21:42:01.052Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Releasing left grip**\nTool call: exec\n{\"description\": \"release left after receiving grasp\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"left_arm\", \"duration_s\": 0.32, \"grip\": 1, \"settle_s\": 0.16}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:04.793Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223726.748315164] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":15.200000000000001,\"waited_s\":0.4800000000000004},\n    \"measured\": {\"left_arm\":[-0.6601924300193787,2.072953224182129,1.3642902374267578,0.7160017490386963,-0.6606023907661438,0.03993125259876251],\"left_gripper\":[0.9805027436126362]},\n    \"tcp\": {\"pose\":[-0.04102687484682255,0.09136394694744493,0.9988741403592395,0.007356924120444551,0.00990022999458713,0.7224363383853434,0.6913274174325201],\"timestamp\":15.2},\n    \"file\": \"/tmp/kinex-observation-1ua02uy9.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/2b8fad37-1.png"
+    ],
+    "timestamp": "2026-10-28T21:42:10.326Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"withdraw left gripper\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"left_arm\", \"pose\": [-0.042, -0.07, 1.005, 0, 0, 0.70710678, 0.70710678], \"duration_s\": 1, \"grip\": 1, \"settle_s\": 0.16}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:14.457Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223736.491118883] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.36,\"waited_s\":1.1599999999999984},\n    \"measured\": {\"left_arm\":[-0.9022496938705444,1.686706781387329,0.8573427796363831,0.8449431657791138,-0.870881974697113,0.02716142125427723],\"left_gripper\":[0.9965842799706893]},\n    \"tcp\": {\"pose\":[-0.04167120487581144,-0.054905644949802546,1.0040063810483326,0.0009341890325687763,0.002662658693494903,0.7093984676486517,0.7048019946320231],\"timestamp\":16.36},\n    \"file\": \"/tmp/kinex-observation-oje0r48w.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/3e5c1e85-1.png"
+    ],
+    "timestamp": "2026-10-28T21:42:23.547Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Lift right block clear**\nTool call: exec\n{\"description\": \"carry block over blue pad\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [0.125, 0.154, 0.94, 0, 0, 1, 0], \"duration_s\": 1.6, \"grip\": 0, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:34.566Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223756.588721697] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":18.2,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"right_arm\":[0.045130740851163864,2.672109842300415,2.162466287612915,0.6279857158660889,-1.4890477657318115,0.1188364252448082],\"right_gripper\":[0.7427296218546955]},\n    \"tcp\": {\"pose\":[0.11606154007485855,0.1512896866479327,0.940862991457205,0.00028879132662097814,0.00016147017939373526,0.9999979661028987,-0.001989551963839931],\"timestamp\":18.2},\n    \"file\": \"/tmp/kinex-observation-5j68foux.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/483e6373-1.png"
+    ],
+    "timestamp": "2026-10-28T21:42:47.607Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: exec\n{\"description\": \"verify carried block geometry\", \"file\": \".kinex/tools/color_geometry.py\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:50.123Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223772.072162218] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.13724063398836486,-0.3166554951861015,0.8944537676754163],[0.11177929348432583,0.1656045185755665,1.07426368560249]],\"median\":[0.07330969700162036,0.11933055696114936,0.9206098896650587],\"n\":819},\"blue\":{\"bounds\":[[0.06864365355552057,0.10104295310802192,0.742188561370138],[0.16147779780023439,0.1994037568596596,0.7463403147189582]],\"median\":[0.13028152312770447,0.14159046303770684,0.7460899470953907],\"n\":869}},\n    \"right_camera\": {\"red\":{\"bounds\":[[-7.601372185174145,0.12024687340851872,-0.000058680332552514254],[-0.1395972028015418,0.5333425266464663,0.9311055369995853]],\"median\":[-0.46112398521004216,0.29721951103053645,0.7400177630068945],\"n\":38037}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:42:52.814Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Computing red mask bounds**\n\n**Adjusting block position**\nTool call: exec\n{\"description\": \"lower block toward pad center\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [0.137, 0.154, 0.803, 0, 0, 1, 0], \"duration_s\": 1.6, \"grip\": 0, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:07.670Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223789.794671718] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.04,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"right_arm\":[0.016218852251768112,2.961329698562622,2.207608699798584,0.8309350609779358,-1.5197806358337402,0.08028469234704971],\"right_gripper\":[0.7453555789860813]},\n    \"tcp\": {\"pose\":[0.13236401782215215,0.15271409951038523,0.8089243044156644,-0.00007593607348670083,0.0015164897783684615,0.9999928843330059,-0.0034533716597715527],\"timestamp\":20.04},\n    \"file\": \"/tmp/kinex-observation-gcosyphn.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/6d58b508-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:17.941Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Measuring segmented component**\nTool call: exec\n{\"description\": \"measure placement clearance\", \"file\": \".kinex/tools/color_geometry.py\", \"args\": {\"cameras\": [\"head_camera\"]}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:24.509Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223806.489186627] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.13485902623471432,-0.31561879628961304,0.7642904861201],[0.13118034378160748,0.17219147239129443,1.073136981197431]],\"median\":[0.09823286684515407,0.12062130422911532,0.8888485465711202],\"n\":2170},\"blue\":{\"bounds\":[[0.08274937023475108,0.10104295310802192,0.742188561370138],[0.16184595025649565,0.1373007236008467,0.7463403147189582]],\"median\":[0.13111940560664184,0.11310705874246474,0.7458174222645024],\"n\":332}}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:26.952Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Lowering red piece**\nTool call: exec\n{\"description\": \"seat block on pad\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"pose\": [0.14, 0.154, 0.787, 0, 0, 1, 0], \"duration_s\": 0.6, \"grip\": 0, \"settle_s\": 0.16}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:33.546Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223815.606444518] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.8,\"waited_s\":0.7600000000000016},\n    \"measured\": {\"right_arm\":[0.011729979887604713,3.007179021835327,2.2419755458831787,0.8377561569213867,-1.5260177850723267,0.06870773434638977],\"right_gripper\":[0.7484062869440425]},\n    \"tcp\": {\"pose\":[0.13483444468352754,0.1530063858574185,0.7941511626747358,-0.000009396803121347259,-0.001933277477839905,0.9999885811141735,-0.004370353664848614],\"timestamp\":20.8},\n    \"file\": \"/tmp/kinex-observation-hhpkiuue.png\"\n  }\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/b88d7c47-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:39.477Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Releasing bottom .749**\nTool call: exec\n{\"description\": \"open right gripper to release block\", \"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"arm\": \"right_arm\", \"duration_s\": 0.4, \"grip\": 1, \"settle_s\": 0.24}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:42.738Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "exec",
+    "text": "{\n  \"output\": \"[INFO] [1793223824.665814335] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"episode ended during wait\",\n  \"code\": \"operation_rejected\",\n  \"details\": {\n    \"operation\": \"world.wait\",\n    \"type\": \"kinex_interfaces/srv/Wait\",\n    \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":20.92,\"waited_s\":0.120000000000001}\n  }\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:45.811Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: device_stop\n{\"reason\": \"World wait reported terminal episode; hold devices and inspect final measurements.\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:49.079Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "device_stop",
+    "text": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:49.181Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"head_camera\", \"action\": \"image\"}, {\"deviceID\": \"right_gripper\", \"action\": \"joint_state\"}, {\"deviceID\": \"left_gripper\", \"action\": \"joint_state\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:52.322Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "devices",
+    "text": "{\n  \"results\": [\n    {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":20.92,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-327div3_.png\"},\n    {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.7629235739057715],\"velocities\":[0.789993865923448],\"joint_names\":[\"right_gripper\"],\"timestamp\":20.92}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9997891133481807],\"velocities\":[0.0002649718226166442],\"joint_names\":[\"left_gripper\"],\"timestamp\":20.92}}\n  ]\n}",
+    "images": [
+      "experiments/robotwin-native/kinex_policy-1/observations/e6fe50bf-1.png"
+    ],
+    "timestamp": "2026-10-28T21:43:52.481Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Setting right target open**\nTool call: device\n{\"deviceID\": \"right_gripper\", \"action\": \"target\", \"args\": {\"data\": 1}}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:59.525Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "device",
+    "text": "{\n  \"published\": true\n}",
+    "images": [],
+    "timestamp": "2026-10-28T21:43:59.565Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "**Building largest-component ROI**\nTool call: edit\n{\"path\": \".kinex/tools/color_geometry.py\", \"edits\": [{\"oldText\": \"from scipy.spatial.transform import Rotation\\n\", \"newText\": \"from scipy.spatial.transform import Rotation\\nfrom scipy.ndimage import label\\n\"}, {\"oldText\": \"                v,u=np.where(mask&(d>0))\\n                if len(u)<5: continue\\n\", \"newText\": \"                components,count=label(mask)\\n                if not count: continue\\n                sizes=np.bincount(components.ravel()); sizes[0]=0\\n                mask=components==int(sizes.argmax())\\n                # Near-gripper RGB-D can be inside the declared minimum range.\\n                v,u=np.where(mask&(d>=.1)&(d<2))\\n                if len(u)<5: continue\\n\"}]}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:08.423Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "edit",
+    "text": "Successfully replaced 2 block(s) in .kinex/tools/color_geometry.py.",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:08.431Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Tool call: write\n{\"path\": \".kinex/memos/handover-block.md\", \"content\": \"# ALOHA AgileX handover\\n\\nRead refreshed profiles and harness each episode. Native dt=.04 s; all waits sequential. No reset. Available tools: `handover_policy.py` bounded StarVLA chunk + measured feedback; `cartesian_move.py` world TCP interpolation/IK + feedback; `color_geometry.py` visible surface bounds, not object centers. Load devices through host. Inspect operation schemas.\\n\\nPolicy returned 16-action chunks, but first 80 frames did not grasp; geometric control worked more directly. Do not blindly reuse numerical scene positions: measure RGB-D each episode. Red object is a tall upright rectangular block, not a small cube. This episode front surface was x≈-.167,y≈.168, base z≈.740; blue pad center≈[.113,.150], top z≈.746. Block width/thickness≈.06 m. Wrist depth at near grasp is unreliable (<.1m), head views may occlude block; color geometry now filters largest component and valid range.\\n\\nGripper TCP +x approaches; fingers close along local y. World left orientation quat xyzw=[0,0,.707107,.707107] approaches +y, closes x. Left pregrasp [-.167,.09,.86], insertion [-.166,.19,.86], close 0; measured opening≈.74 with object. Lift to [-.163,.135,.98], stage [-.03,.09,1.0]. Block bottom then≈.879. Right orientation [0,0,1,0] approaches -x, closes y. Right pregrasp [.085,.107,.923], insertion [-.035,.108,.923], close; measured≈.76. Right grasps ~.077m below left; open left then withdraw along -y to -.07. Carry right [.125,.154,.94], lower [.137,.154,.803], seat [.140,.154,.787]. Actual TCP tracking lag 5–15 mm; block bottom≈TCP z-.046, center x≈TCP x-.02. Verify measurements instead of relying on targets. Right-held block visibly moved while left open and withdrawn.\\n\\nLatest episode: release target 1 at t20.8; world terminated during wait at20.92 (523 frames), not a full successful wait. Held devices, read final image/block over pad and right opening .763, left≈1.0. Restored right open target1 after hold, no further waits. Full-open arrival and native verdict were NOT available; owner finalizes. A terminal wait alone is never success evidence.\\n\"}",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:31.750Z"
+  },
+  {
+    "role": "toolResult",
+    "name": "write",
+    "text": "Successfully wrote to .kinex/memos/handover-block.md",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:31.753Z"
+  },
+  {
+    "role": "assistant",
+    "name": null,
+    "text": "Handed the block to the right arm and placed it on the blue pad. Commanded release, but the episode ended before full gripper opening could be verified.",
+    "images": [],
+    "timestamp": "2026-10-28T21:44:34.975Z"
+  }
+]
diff --git a/experiments/robotwin-native/kinex_policy-1/session.jsonl b/experiments/robotwin-native/kinex_policy-1/session.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2067452f9f2a694c84044b542f337f185f8e62dd
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/session.jsonl
@@ -0,0 +1,113 @@
+{"type":"session","version":3,"id":"01a18471-08d1-72aa-822f-7c775b2db8c7","timestamp":"2026-10-28T21:36:02.769Z","cwd":"/workspace"}
+{"type":"model_change","id":"2010a087","parentId":null,"timestamp":"2026-10-28T21:36:05.210Z","provider":"openai-codex","modelId":"gpt-6-astra"}
+{"type":"thinking_level_change","id":"57dc0623","parentId":"2010a087","timestamp":"2026-10-28T21:36:05.210Z","thinkingLevel":"high"}
+{"type":"message","id":"3add3304","parentId":"57dc0623","timestamp":"2026-10-28T21:36:05.503Z","message":{"role":"system","content":"","sections":{"preamble":"You are an expert coding assistant operating inside pi, a coding agent harness. You help users by reading files, executing commands, editing code, and writing new files.","tools":"<tools>\n- read: Read file contents\n- write: Create or overwrite files\n- edit: Make precise file edits with exact text replacement, including multiple disjoint edits in one call\n- bash: Execute bash commands (ls, grep, find, etc.)\n- grep: Search file contents for patterns (respects .gitignore)\n- find: Find files by glob pattern (respects .gitignore)\n- ls: List directory contents\n\nIn addition to the tools above, you may have access to other custom tools depending on the project.\n</tools>","rules":"<rules>\n- Use read to examine files instead of cat or sed.\n- Use write only for new files or complete rewrites.\n- Use edit for precise changes (edits[].oldText must match exactly)\n- When changing multiple separate locations in one file, use one edit call with multiple entries in edits[] instead of multiple edit calls\n- Each edits[].oldText is matched against the original file, not after earlier edits are applied. Do not emit overlapping or nested edits. Merge nearby changes into one edit.\n- Keep edits[].oldText as small as possible while still being unique in the file. Do not pad with large unchanged regions.\n- You can inspect PI_* environment variables for current model and session details.\n- Be concise in your responses\n- Show file paths clearly when working with files\n</rules>","docs":"<docs>\nPi documentation (read only when the user asks about pi itself, its SDK, extensions, themes, skills, or TUI):\n- Main documentation: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/README.md\n- Additional docs: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/docs\n- Examples: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/examples (extensions, custom tools, SDK)\n- When reading pi docs or examples, resolve docs/... under Additional docs and examples/... under Examples, not the current working directory\n- When asked about: extensions (docs/extensions.md, examples/extensions/), themes (docs/themes.md), skills (docs/skills.md), prompt templates (docs/prompt-templates.md), TUI components (docs/tui.md), keybindings (docs/keybindings.md), SDK integrations (docs/sdk.md), custom providers (docs/custom-provider.md), adding models (docs/models.md), pi packages (docs/packages.md), environment variables (docs/environment-variables.md), MCP servers (docs/mcp.md), codemode scripts and non-LLM models such as classifiers and image models (docs/codemode.md)\n- When working on pi topics, read the docs and examples, and follow .md cross-references before implementing\n- Always read pi .md files completely and follow links to related docs (e.g., tui.md for TUI API details)\n</docs>","skills":"<skills>\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>btw</name>\n    <description>Helps you use the /btw side-conversation workflow effectively. Use when you want to think in parallel, ask side questions without interrupting ongoing work, or inject a side thread back into the main agent.</description>\n    <location>/opt/kinex/node_modules/pi-btw/skills/btw/SKILL.md</location>\n  </skill>\n</available_skills>\n</skills>","cwd":"<cwd>\n/workspace\n</cwd>","kinex":"<kinex>\nYou are Kinex, a coding agent for robotics work. Use the coding tools normally and use Kinex tools for robot/device access. Direct camera reads include the image; no separate file read is needed.\n\nRuntime facts arrive in kinex_context messages; they are host state, not user requests, and the latest value of each section applies. Tools and skills written during a task become available after the current tool batch.\n\nAsk the user through the question tool.\n\n\n# Robotics\n\n## Workspace\n\nThe nearest `.kinex/` (here or an ancestor) holds the robot:\n- `workspace.yaml`: identity and safety limits.\n- `devices/<name>/`: `profile.yaml` plus node sources (`node.py`, URDF, ...).\n- `device/`, `tools/`, `skills/`: workspace guides, tools and skills; override builtins.\n- `lib/`: Python shared by exec, tools and nodes.\n- `kinex.json`, `python/`: config and the Kinex Python environment.\n- `recordings/`: recordings.\nWrite `profile.yaml` and `workspace.yaml` only with profile_write.\n\n## Devices\n\nA device is a profile plus the ROS topics and services its nodes serve. Its\ndescriptions are its contract; operation names come from the profile, not the type.\nOnly nodes touch SDKs, simulators and model services, and hold persistent resources.\nThe world device owns time and waits. Use the device tool to load, unload, read and command.\n\n## Python\n\nexec runs Kinex Python, separate from device and simulator environments; never patch sys.path.\nDiscover devices with `kinex.runtime` and return only what you need via `finish()`.\nROS resources a script creates end with it. Install missing ROS packages through setup.\n\n## Principles\n\nFollow profile units, limits and safety; never assume missing facts. Avoid unintended\ncollisions along the whole path. A sent command or arrival is not success; verify with\nmeasurements. On faults, unexpected contact or lost feedback, call device_stop first.\nWork efficiently: skip checks that add nothing, combine tool calls if necessary\n(e.g. perceive and return the results inside the same exec for motion),\nbut never at the cost of soundness and task completion.\n\n## Tools and the user\n\nTool descriptions define execution semantics; skills give procedures. Don't repeat\npermission prompts as questions. Ask only for missing facts, choices or physical\nfeedback.\n\n\n# Task-scoped setup\n\nOnboarding prepares an embodiment through an agent-authored DAG. Use setup_plan to\ninspect/save nodes and select only the current task's target nodes and prerequisites.\nMissing plans and incomplete profiles do not by themselves prevent task execution;\nexisting operation-specific validation and host permissions still apply. Global profile\ncompleteness is descriptive. Return to the task as soon as its required facts exist.\n\nReuse verified factory, model, simulator and calibration facts before measuring.\nKeep unknowns as ???. Split broad objectives into independently useful facts so a\npartial calibration can satisfy a narrower task. Optional work stays deferred.\nBind procedures to actual devices and frames; morphology does not imply an interface.\nThe frame graph and setup dependency DAG are different graphs. Plans live in\n`.kinex/setup/plan.json`, calibration attempts in `.kinex/calib/<run>/`.\n\nAvoid adding new tests unless necessary. Skip python tests during task execution or setup attempts.\n\nPrepare or repair devices with core's setup-device skill; keep existing working\nintegrations unless replacement is requested.\n\n</kinex>"},"timestamp":1793223365500,"toolsAdded":[{"name":"read","description":"Read the contents of a file. Supports text files and images (jpg, png, gif, webp, bmp). Images are sent as attachments. For text files, output is truncated to 2000 lines or 50KB (whichever is hit first). Use offset/limit for large files. When you need the full file, continue with offset until complete.","parameters":{"type":"object","required":["path"],"properties":{"path":{"type":"string","description":"Path to the file to read (relative or absolute)"},"offset":{"type":"number","description":"Line number to start reading from (1-indexed)"},"limit":{"type":"number","description":"Maximum number of lines to read"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"write","description":"Write content to a file. Creates the file if it doesn't exist, overwrites if it does. Automatically creates parent directories.","parameters":{"type":"object","required":["path","content"],"properties":{"path":{"type":"string","description":"Path to the file to write (relative or absolute)"},"content":{"type":"string","description":"Content to write to the file"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"edit","description":"Edit a single file using exact text replacement. Every edits[].oldText must match a unique, non-overlapping region of the original file. If two changes affect the same block or nearby lines, merge them into one edit instead of emitting overlapping edits. Do not include large unchanged regions just to connect distant changes.","parameters":{"type":"object","required":["path","edits"],"properties":{"path":{"type":"string","description":"Path to the file to edit (relative or absolute)"},"edits":{"type":"array","items":{"type":"object","required":["oldText","newText"],"properties":{"oldText":{"type":"string","description":"Exact text for one targeted replacement. It must be unique in the original file and must not overlap with any other edits[].oldText in the same call."},"newText":{"type":"string","description":"Replacement text for this targeted edit."}}},"description":"One or more targeted replacements. Each edit is matched against the original file, not incrementally. Do not include overlapping or nested edits. If two changes touch the same block or nearby lines, merge them into one edit instead."}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"bash","description":"Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last 2000 lines or 50KB (whichever is hit first). If truncated, full output is saved to a temp file. Optionally provide a timeout in seconds.","parameters":{"type":"object","required":["command"],"properties":{"command":{"type":"string","description":"Shell command to execute"},"timeout":{"type":"number","description":"Timeout in seconds (optional, no default timeout)"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"grep","description":"Search file contents for a pattern. Returns matching lines with file paths and line numbers. Respects .gitignore. Output is truncated to 100 matches or 50KB (whichever is hit first). Long lines are truncated to 500 chars.","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Search pattern (regex or literal string)"},"path":{"type":"string","description":"Directory or file to search (default: current directory)"},"glob":{"type":"string","description":"Filter files by glob pattern, e.g. '*.ts' or '**/*.spec.ts'"},"ignoreCase":{"type":"boolean","description":"Case-insensitive search (default: false)"},"literal":{"type":"boolean","description":"Treat pattern as literal string instead of regex (default: false)"},"context":{"type":"number","description":"Number of lines to show before and after each match (default: 0)"},"limit":{"type":"number","description":"Maximum number of matches to return (default: 100)"}}}},{"name":"find","description":"Search for files by glob pattern. Returns matching file paths relative to the search directory. Respects .gitignore. Output is truncated to 1000 results or 50KB (whichever is hit first).","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Glob pattern to match files, e.g. '*.ts', '**/*.json', or 'src/**/*.spec.ts'"},"path":{"type":"string","description":"Directory to search in (default: current directory)"},"limit":{"type":"number","description":"Maximum number of results (default: 1000)"}}}},{"name":"ls","description":"List directory contents. Returns entries sorted alphabetically, with '/' suffix for directories. Includes dotfiles. Output is truncated to 500 entries or 50KB (whichever is hit first).","parameters":{"type":"object","properties":{"path":{"type":"string","description":"Directory to list (default: current directory)"},"limit":{"type":"number","description":"Maximum number of entries to return (default: 500)"}}}},{"name":"web_search","description":"Search the web using the current supported provider (Google Gemini, xAI Grok, OpenAI, Anthropic, DeepSeek, Ollama, or OpenCode Zen/Go). Optionally include URLs to analyze alongside search results.","parameters":{"type":"object","required":["query"],"properties":{"query":{"type":"string","description":"The search query or question to answer"},"urls":{"type":"array","items":{"type":"string"},"description":"Additional URLs to analyze along with search (up to 20)","maxItems":20}}}},{"name":"calib_compute","description":"Run supported offline calibration/introspection using archived evidence. No device\nmotion or profile writes. Use an existing run ID, a plan node, or a procedure name\nwith concrete device-role bindings. First use creates an isolated attempt. Use the\nprocedure descriptor returned by setup_plan full:true for roles and computations.\nCustom procedure code runs through existing host tools, not this offline registry.\n\nResults distinguish computation, quality and exact resolved fields.\nValid computed results that miss accuracy targets may still be accepted within\nthe user's accuracy/time budget. Do not repeat calibration solely to meet those\ntargets when the user accepts the result. If the user explicitly keeps a result\nthat exceeds a limit, honor it without repeated confirmation or forced recollection;\nwhen requested, persist its computed fields with profile_write from the archived\nsolver data even if failed quality leaves proposedChanges empty, using the\nprocedure's frame conventions, and record the errors, limits and user decision in\nevidence. Always preserve measured errors and the original quality assessment,\nnever mark a failed-quality node done, and never treat missing or invalid results\nas approximate estimates.\nApply accepted proposedChanges with core profile_write, preserving unrelated facts.\nProposed patches never clear calibration requests automatically. A pivot solve\nmeasures translation only; RGB-D color calibration does not measure depth facts.\nURDF introspection of the bound arm or target is offline and does not require\nactivation. Base verification uses explicit measured checks and tolerances,\nwithout controller tuning. Sensor bias averages calib_sample readings of a static,\nunloaded imu or force_torque target and proposes the current bias plus each axis's\nmean residual; specific-force accelerometers need gravity_m_s2, the expected\nstatic reading in the IMU frame.\n\nArchive manual/factory facts or external solver results as evidence for setup_plan\nrecord. Reuse valid evidence and stop as soon as the current task is supported.\n\nBase measurement files contain required_checks:[name,...] and checks:[{name,\nexpected,measured,reference,...}]. reference names the archived source or independent\nmeasurement. Numerical expected/measured values need explicit tolerance and units.\nOther assertions compare exact values. Optional timestamps must contain at least\ntwo distinct increasing sample times. Only required_checks determine coverage;\nmissing checks fail quality. Do not claim untested axes or operating 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file path, relative to invocation directory or absolute"},"gravity_m_s2":{"description":"expected static specific force in the IMU frame, e.g. [0, 0, 9.80665] when level with +z up","type":"array","prefixItems":[{"type":"number"},{"type":"number"},{"type":"number"}],"items":false,"minItems":3,"maxItems":3}},"required":["operation","samples"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"calib_sample","description":"Read one sample into an isolated .kinex/calib/<run>/samples.json. Start with a plan\nnode or procedure plus bindings; subsequent samples use the returned run ID. The\ndevice must be one of the bound participants. Inputs and participants cannot change\nwithin an attempt. Returns the new sample, run ID and count; camera attachments are\ncopied into immutable attempt artifacts, including both RGB and depth files for\nRGBD readings. Both copies must succeed before a sample is appended.\nUse note to identify the collection step. Samples\ncontain measured state and timestamps; this tool cannot command or load devices.\n\nOptional settle waits for measured velocities to remain below the threshold over\ndistinct samples. Unsupported or malformed feedback is an error. Cached readings\ndo not establish stability. A timeout after a reading saves that reading with\nsettled:false; cancellation saves nothing. Without settle, the sample makes no\nclaim of stationary motion. Settling supports joint devices whose joint states\nmeasure every commanded joint's velocity, and locomotion devices whose odometry\nmeasures their declared velocity axes; their runtime.observation must name the\nodometry operation.\nThis blocking sampler does not advance simulator time; arrange world waits\noutside the sampler when collecting simulation evidence.\n\nFor joint devices, optional frame names a URDF link; FK from that exact sample's\npositions stores kinematics with frame and pose in the device base alongside the\nmeasured data. Use it for TCP calibration; joint-device reads contain no link\nposes. FK and artifact capture still run after a settle timeout, subject to caller\ncancellation.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","description":"device to sample, e.g. arm"},"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"args":{"description":"arguments for the observation operation","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"frame":{"description":"joint-device URDF link whose pose to compute through FK from this sample","type":"string","minLength":1},"settle":{"description":"wait for the device to stop moving before recording; omit to record immediately","type":"object","properties":{"velocity_threshold":{"default":0.005,"description":"max |joint velocity| that counts as settled","type":"number","minimum":0},"stable_reads":{"default":3,"description":"consecutive settled reads required","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"timeout_s":{"default":30,"type":"number","exclusiveMinimum":0}}},"note":{"description":"label stored with the sample, e.g. 'touch 3, rolled +45deg'","type":"string"}},"required":["deviceID"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device","description":"Call a named device's declared operation with action and args. Inspect the compact operation catalog for names, topic/service types and payload contracts. Prefer exec with async communication helpers for streams and composed operations so intermediate data stays outside model context.\nload/unload accept no arguments; status observes readiness without starting or restarting resources. stop invokes the declared stopping behavior and cancels running scripts that commanded the device. Runtime definition changes require unload, validation and explicit reload. Never terminate borrowed services.\nImage observations return selected image attachments and acquisition timestamps. A sent command is not measured arrival. The world device owns time and wait semantics; reads never advance time. relative_pose uses tf2 and ROS frame IDs.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"compact":{"default":false,"description":"single-line JSON output — use in polling/sampling loops","type":"boolean"}},"required":["deviceID","action"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device_stop","description":"Stop the named device, or every loaded device with a stop operation when deviceID\nis omitted, and cancel running scripts that commanded a stopped device. Stops use\neach node's declared behavior and affected-device scope. No approval is required.\nResults report each device's acknowledgment or failure; inspect failures before resuming.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"description":"device to stop; omit to stop every loaded device with a stop operation","type":"string"},"reason":{"description":"why the stop was triggered","type":"string"}},"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"devices","description":"Call devices concurrently with calls: [{deviceID, action, args?}]; action is an explicit declared operation. Returns {results: [...]} in input order, with deviceID, action, ok and\ndata/file or error per entry.\nImage observations may carry attachments; attached images follow call order. Permissions are\nchecked before dispatch. Sampling is not simultaneous and effects are not atomic.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"calls":{"minItems":1,"type":"array","items":{"type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}}},"required":["deviceID","action"],"additionalProperties":false}}},"required":["calls"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"exec","description":"Run Python source or a script in the workspace's Kinex Python. Device environments and the world clock never choose this interpreter. Python cannot load/unload devices or control recording; lifecycle remains host-controlled. One execution approval covers composed operations.\nDiscover interfaces on demand, without starting devices:\n  from kinex import runtime, finish\n  devices = runtime.devices()\n  # runtime.operations(device_name) lists declared operations, even if unloaded.\n  # runtime.describe(operation) resolves ROS payload schemas when needed.\n  finish({\"devices\": [{\"name\": d[\"name\"], \"loaded\": d[\"loaded\"]} for d in devices]})\nUse actual operation names from these queries, not names inferred from device types. Catalogs remain in Python until you explicitly return selected fields. Load required devices through the host before communication.\nFor async protocol work, import communication, args, finish from kinex. For example, where args supplies a discovered command topic and its ROS payload:\n  async def main():\n      a = args()\n      async with communication() as io:\n          await io.publish(a[\"operation\"], a[\"payload\"])\n      finish({\"sent\": True})\n  # Execute with asyncio.run(main()). Sending is not measured arrival.\nUse io.subscribe(operation, depth=1) and await stream.next(timeout_s=...) for output topics (with or async with closes the stream early); await io.request(operation, **fields) for services. World wait operations own scheduling, one wait at a time. Observations and tf2 queries never advance world time. Bound work, inspect fresh measured feedback, and stop on terminal task status. Service rejections raise kinex.OperationError with operation, response and structured details; inspect the response and relevant feedback before deciding how to recover.\nImport helpers with from kinex import helpers. Read kinex/helpers/helpers.txt under the Python library directory in runtime context for available functions and implementation paths. Builtin Python tools are interfaces to these helpers; call them directly to share this invocation's communication and cleanup. Put workspace-specific helpers in .kinex/lib, which exec can import.\nFailure, timeout or cancellation stops every device this invocation commanded; success leaves their motion running. finish(value, file=paths) returns selected summaries as JSON, converting ROS messages and NumPy values, and images only through file paths, after leaving the communication scope. Do not print entire catalogs or raw streams unless requested.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"description":{"description":"Short lowercase description of the task, for display","type":"string"},"python":{"description":"Python source, including multiline scripts; provide exactly one of python or file","type":"string","minLength":1},"file":{"description":"Script path, relative to the working directory or absolute","type":"string","minLength":1},"args":{"default":{},"description":"Values available through kinex.args()","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"argv":{"default":[],"description":"Script command-line arguments","type":"array","items":{"type":"string"}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":2147483}},"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"locate_object_3d","description":"[Python tool] Locate an object in the camera optical frame (+X right, +Y down, +Z forward) using a detection or segmentation device with RGBD or explicit optical depth. Samples only inside a supplied mask; otherwise uses the central half of the box. Returns a representative point in metres, pixel, detection_score, depth_samples, and reference (visible_surface or box_region). Box estimates may include background. rotation_xyzw is null. No mount calibration is needed; convert frames with relative_pose after constructing a full local pose with an explicit orientation.\n\nDevice input camera: rgb | rgbd (checked against the selected device).\nDevice input model: model (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"camera":{"type":"string","description":"rgb or rgbd camera with calibrated color intrinsics"},"model":{"type":"string","description":"model device whose Json detect takes an image path, prompt, min_score and max_results and returns results with box, score, label and an optional mask PNG path"},"prompt":{"type":"string","minLength":1,"description":"object description, e.g. red mug"},"depth":{"type":"number","exclusiveMinimum":0,"description":"optional optical Z in metres at the representative region pixel; otherwise use the camera's paired RGBD depth"},"min_score":{"type":"number","minimum":0,"maximum":1,"default":0.25,"description":"minimum detection score"},"instance":{"type":"integer","minimum":0,"default":0,"description":"zero-based index after filtering and sorting by descending score; ties keep model order"}},"required":["camera","model","prompt"]}},{"name":"profile_write","description":"Persist profile facts in the workspace:\n- robot: partial manifest; identity requires id, name and kind initially. identity.kind describes the whole robot's morphology and must be arm, mobile, quadruped, humanoid or other. Use other when no category fits; ??? is not accepted for this field.\n- device: partial profile; name is required, type and description on first write, plus frames.base for spatial devices. Use setup-device and the selected guide for new devices.\n- needed: {item,note?}; queue the reason without changing existing provenance.\n\nPatches deep-merge. To switch a strict variant, supply the complete top-level section and list it in replace (e.g. [\"model\"]). Arrays replace wholesale. null removes an optional section only when listed in replace. Changing device type replaces its profile. Invalid updates are not saved.\n\nOutstanding requests persist across reads and replacements. Clear a resolved request with top-level needs_calibration:null for profile:<device> (or in access for access:<device>); the store rejects clearing incomplete facts. Unknown required facts remain ???. Set source to the origin of the data; keep diagnostics in calibration evidence.\n\nNew devices use loaded:false; activation uses the device tool (action=\"load\") or /load, not profile edits. Profiles live at .kinex/devices/<name>/profile.yaml beside node sources, using the nearest .kinex/ in the current directory or its ancestors (the current directory's .kinex/ if none exists). Validation errors identify fields and the guide to consult.\n\nDevice description gives general identity, capabilities and limitations. Each operation requires its own description for payload conventions and behavior; both descriptions are required strings and may be empty. Typed templates guide setup, while actual operation names are device-specific.\n\nPhysical facts (model, limits, color, frames, etc.) are top-level. Every frame requires parent: device:frame or null for an independent root; there is no implicit parent. Root transform components are ignored. Non-root null components mean unknown, never identity. Mounts are frames.base.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"kind":{"type":"string","enum":["robot","device","needed"]},"replace":{"default":[],"description":"Supplied top-level sections to replace instead of merge; null removes optional sections","type":"array","items":{"type":"string"}},"data":{"type":"object","propertyNames":{"type":"string"},"additionalProperties":{},"description":"partial workspace manifest patch (identity required on first write) for kind=robot, partial device patch (must include name; type on first write) for kind=device, or {item, note?} for kind=needed"}},"required":["kind","data"],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"relative_pose","description":"[Python tool] Transform a frame-attached pose through tf2. Use device:frame references or ROS frame IDs and an optional world timestamp. Missing frame relationships and unavailable timestamps are errors.","parameters":{"type":"object","additionalProperties":false,"properties":{"child":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"parent":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"timestamp":{"type":"number","minimum":0}},"required":["child","parent"]}},{"name":"run_policy","description":"[Python tool] Infer once and submit the shared valid action prefix concurrently. Returns actions_submitted, duration_s and ik_failure. Use max_actions to cap the execution horizon for receding horizon control. IK rejection returns normally, including an empty prefix, with diagnostic ik_failure (device, zero-based action_index, error), or null. Joint-only submission is asynchronous. Chunks containing gripper or suction devices publish persistent targets paced by world waits before returning; the final target remains active.\n\nDevice input model: model (checked against the selected device).\nDevice input command[].device: joint | gripper | suction (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"model":{"type":"string","description":"model device serving an \"infer\" action (obs -> action)"},"observation":{"type":"object","description":"Observation object matching the model's infer obs contract."},"inference_args":{"type":"object","description":"Additional infer arguments validated by the model operation; cannot override obs or task."},"command":{"type":"array","minItems":1,"description":"Devices in flat-vector split order; named action maps use device names. Cartesian chunks are converted through IK before any write.","items":{"type":"object","additionalProperties":false,"properties":{"device":{"type":"string","description":"joint","gripper or suction device":null},"space":{"type":"string","enum":["device","pose","pose_delta"],"default":"device","description":"device: absolute joint values in URDF document order; gripper and suction values in [-1, 1] release at -1 and grip at +1: grippers interpolate from open to closed, proportional suction takes the vacuum level (v + 1) / 2, binary suction switches on above 0. pose: xyz + quaternion xyzw (7). pose_delta: translation metres + axis-angle rotation radians (6), accumulated from the starting pose. Cartesian modes solve URDF IK and require a joint device with model.tip_link."},"frame":{"type":"string","description":"Cartesian reference frame, frozen at planning start; defaults to the device canonical frame. Rotation deltas left-compose in these axes."},"target_frame":{"type":"string","description":"Controlled frame, rigidly attached to the arm's tip_link; defaults to that link."}},"required":["device"]}},"task":{"type":"string","description":"task or goal handed to the model, e.g. a language instruction"},"world":{"type":"string","default":"world","description":"World device providing relative wait for end-effector action pacing."},"action_hz":{"type":"number","exclusiveMinimum":0,"default":10,"description":"actions replayed per second of the world clock"},"max_actions":{"type":"integer","minimum":1,"description":"Execution horizon: submit at most the first N inferred actions, discarding the remainder before validation and IK. Omit to use the whole chunk. Each call infers anew from the supplied observation. IK rejection may shorten this prefix further."}},"required":["model","observation","command"]}},{"name":"setup_plan","description":"Inspect or maintain the agent-authored embodiment DAG. This is task-scoped planning,\nnever a gate on device operations. Read with full:true for the complete graph and\navailable procedure descriptors; the default shows only the current task's gaps.\nUse the returned revision for save/select/record; conflicting edits must be reread.\n\nSave version:1 nodes with IDs, kind prepare/calibrate/verify, skill, concrete device\nrole bindings, dependsOn IDs, input facts and expected output facts. A profile fact\nuses kind:profile, device (omit for workspace manifest), and JSON pointer; optional\nvalue requires an exact match. Use narrow profile pointers for both inputs and\noutputs, tracking only facts relevant to the node's conclusion. Keep volatile\nloaded/meta fields and live poses out of persistent calibration dependencies and\noutputs. File facts use paths relative to .kinex. Select a\ntask with objective, target node IDs and optional direct facts. Only the targets'\ndependency closure is required now; unrelated work stays deferred. task:null\ndefers all calibration. An empty targets list can describe a task requiring no\ncalibration. Skill descriptors list their exact roles, requirements and coverage.\n\nRecord done only after verifying persisted outputs and archiving supporting\nevidence files (paths relative to .kinex). Existing factory/model/calibration facts\ncan satisfy a node without new measurements. Partial results may satisfy a narrower\ntask: split broad nodes into independently useful outputs. Never mark deferred work\ndone or clear incomplete profile requests. Verification receipts describe evidence,\nnot safety certification. Revisions and stale evidence never prevent unrelated\noperations. Replan only relevant gaps and return to the task as soon as satisfied.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","oneOf":[{"type":"object","properties":{"action":{"type":"string","const":"read"},"full":{"default":false,"type":"boolean"}},"required":["action"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"save"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"plan":{"type":"object","properties":{"version":{"type":"number","const":1},"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"title":{"type":"string","minLength":1},"kind":{"type":"string","enum":["prepare","calibrate","verify"]},"skill":{"type":"string","minLength":1},"bindings":{"default":{},"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"dependsOn":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"outputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"optional":{"default":false,"type":"boolean"}},"required":["id","title","kind","skill"],"additionalProperties":false}},"task":{"default":null,"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["version","nodes"],"additionalProperties":false}},"required":["action","revision","plan"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"select"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"task":{"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["action","revision","task"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"record"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"outcome":{"type":"object","properties":{"status":{"type":"string","enum":["done","partial","failed","needs-input"]},"note":{"type":"string","minLength":1},"evidence":{"minItems":1,"type":"array","items":{"type":"string","minLength":1}}},"required":["status","note","evidence"],"additionalProperties":false}},"required":["action","revision","node","outcome"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"skill","description":"Loads a skill by name and returns its full instructions. Skills are listed in the\n<available_skills> block of the current context; load one when its description matches the\ncurrent task, then follow its instructions.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"name":{"type":"string","description":"name of the skill to load"}},"required":["name"],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"question","description":"Ask the user for missing facts or choices.","parameters":{"type":"object","required":["questions"],"properties":{"questions":{"type":"array","items":{"type":"object","required":["header","question","options"],"properties":{"header":{"type":"string"},"question":{"type":"string"},"options":{"type":"array","items":{"type":"object","required":["label"],"properties":{"label":{"type":"string"},"description":{"type":"string"}}}},"multiple":{"type":"boolean"}}}}}}}]}}
+{"type":"message","id":"3e107627","parentId":"3add3304","timestamp":"2026-10-28T21:36:05.503Z","message":{"role":"user","content":[{"type":"text","text":"# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}],"timestamp":1793223365499}}
+{"type":"custom_message","customType":"kinex-context","content":"<kinex_context>\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-d90921ac50b5423fa9995617751ad56e/config/device, /opt/kinex/packages/core/assets/builtin/device.\n\ncore.python:\n{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}\n\ncore.recording:\n(none)\n\ncore.skills:\n<available_skills>\nLoad a skill with the skill tool when its description matches the task.\n  - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n  - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n  - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n  - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n  - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n  - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n  - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n  - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n  - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n  - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>\n\nproblems:\n(none)\n</kinex_context>","display":false,"details":{"core.guides":"Device guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-d90921ac50b5423fa9995617751ad56e/config/device, /opt/kinex/packages/core/assets/builtin/device.","core.python":"{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}","core.recording":"","core.skills":"<available_skills>\nLoad a skill with the skill tool when its description matches the task.\n  - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n  - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n  - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n  - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n  - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n  - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n  - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n  - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n  - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n  - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>","problems":""},"id":"9473a4ca","parentId":"3e107627","timestamp":"2026-10-28T21:36:05.504Z"}
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+{"type":"message","id":"cc628236","parentId":"1d9a472f","timestamp":"2026-10-28T21:36:08.076Z","message":{"role":"toolResult","toolCallId":"call_b93e1b1d30bb49cb930b357edb6cfa08|fc_08ccae8539651651016ae26ac7a39081a2a97abc8fe896e84b","toolName":"ls","content":[{"type":"text","text":".kinex/\nharness.md\ninstruction.md"}],"isError":false,"durationMs":4,"timestamp":1793223368075}}
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+{"type":"message","id":"b808495d","parentId":"5674107c","timestamp":"2026-10-28T21:36:18.278Z","message":{"role":"toolResult","toolCallId":"call_816b5ca02a114a64b4420799465afe07|fc_08ccae8539651651016ae26ad1758c81a2b2b68b8ff51795ee","toolName":"bash","content":[{"type":"text","text":".kinex/devices:\nhead_camera\nleft_arm\nleft_camera\nleft_gripper\npolicy\nright_arm\nright_camera\nright_gripper\nworld\n\n.kinex/memos:\n\n.kinex/skills:\n\n.kinex/tools:\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/policy/profile.yaml\n.kinex/devices/world/profile.yaml\n"}],"isError":false,"durationMs":18,"timestamp":1793223378278}}
+{"type":"message","id":"1c4b5557","parentId":"b808495d","timestamp":"2026-10-28T21:36:22.892Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_a30019bfd91b48169cd7775acfc00bf5|fc_08ccae8539651651016ae26ad3f0d481a2828a63d83409b2b5","name":"read","arguments":{"path":".kinex/workspace.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_45af048065f64f73a1bbbdb48b85dc99|fc_08ccae8539651651016ae26ad6cd4481a293ec4db2e646860b","name":"read","arguments":{"path":".kinex/devices/world/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_5db8f7c6a60b43868668ed652f735d39|fc_08ccae8539651651016ae26ad6cd4481a2b747723e901c44aa","name":"read","arguments":{"path":".kinex/devices/left_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_8020a4743b7b43bf9facc60a7382a1e1|fc_08ccae8539651651016ae26ad6cd4481a28d26477c6e4a9d33","name":"read","arguments":{"path":".kinex/devices/right_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_361c4eaee00e4ed5b9e87e340a443899|fc_08ccae8539651651016ae26ad6cd4481a29299c5cde34e1586","name":"read","arguments":{"path":".kinex/devices/left_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_d6e04324c9c94e7e8022689c8acd9262|fc_08ccae8539651651016ae26ad6cd4481a28f9e18f500a40d91","name":"read","arguments":{"path":".kinex/devices/right_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_073226633db3436eaa004fd1b7f47bda|fc_08ccae8539651651016ae26ad6cd4481a294e04aa528646fdf","name":"read","arguments":{"path":".kinex/devices/policy/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_5de315338fcb4ddc86f68b19eec4c4d4|fc_08ccae8539651651016ae26ad6cd4481a29faa31a2d212e054","name":"read","arguments":{"path":".kinex/devices/head_camera/profile.yaml","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":267,"output":252,"cacheRead":15872,"cacheWrite":0,"reasoning":0,"totalTokens":16391,"cost":{"input":0.00267,"output":0.0126,"cacheRead":0.015872,"cacheWrite":0,"total":0.031142000000000003}},"stopReason":"toolUse","timestamp":1793223378302,"responseId":"resp_08ccae8539651651016ae26ad2bcb081a29f2e342e74de49b5","rawStopReason":"completed","durationMs":4589,"thinkingLevel":"high"}}
+{"type":"message","id":"fd0e8b70","parentId":"1c4b5557","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_a30019bfd91b48169cd7775acfc00bf5|fc_08ccae8539651651016ae26ad3f0d481a2828a63d83409b2b5","toolName":"read","content":[{"type":"text","text":"identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"b7e881af","parentId":"fd0e8b70","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_45af048065f64f73a1bbbdb48b85dc99|fc_08ccae8539651651016ae26ad6cd4481a293ec4db2e646860b","toolName":"read","content":[{"type":"text","text":"name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"d84c8910","parentId":"b7e881af","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_5db8f7c6a60b43868668ed652f735d39|fc_08ccae8539651651016ae26ad6cd4481a2b747723e901c44aa","toolName":"read","content":[{"type":"text","text":"name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"c389212e","parentId":"d84c8910","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_8020a4743b7b43bf9facc60a7382a1e1|fc_08ccae8539651651016ae26ad6cd4481a28d26477c6e4a9d33","toolName":"read","content":[{"type":"text","text":"name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"567ffac4","parentId":"c389212e","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_361c4eaee00e4ed5b9e87e340a443899|fc_08ccae8539651651016ae26ad6cd4481a29299c5cde34e1586","toolName":"read","content":[{"type":"text","text":"name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"c5262e90","parentId":"567ffac4","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_d6e04324c9c94e7e8022689c8acd9262|fc_08ccae8539651651016ae26ad6cd4481a28f9e18f500a40d91","toolName":"read","content":[{"type":"text","text":"name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"1cc5a774","parentId":"c5262e90","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_073226633db3436eaa004fd1b7f47bda|fc_08ccae8539651651016ae26ad6cd4481a294e04aa528646fdf","toolName":"read","content":[{"type":"text","text":"name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n  in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n  absolute ALOHA joint actions. Use the native run_policy tool with command order\n  left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n  one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n  created_at: '2026-10-28T21:34:56.980232+00:00'\n  updated_at: '2026-10-28T21:34:56.980232+00:00'\ncompute:\n  index: 0\n  kind: cuda\nartifacts: []\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: node.py\n    factory: create\n  overlays:\n  - env.sh\n  ready_timeout_s: 600\noperations:\n  infer:\n    description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n      RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n      left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n      +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n    transport: service\n    direction: input\n    endpoint: /kinex/policy/infer\n    type: kinex_interfaces/srv/Json\n    timeout_s: 120\n"}],"isError":false,"durationMs":5,"timestamp":1793223382899}}
+{"type":"message","id":"de5ae04f","parentId":"1cc5a774","timestamp":"2026-10-28T21:36:22.899Z","message":{"role":"toolResult","toolCallId":"call_5de315338fcb4ddc86f68b19eec4c4d4|fc_08ccae8539651651016ae26ad6cd4481a29faa31a2d212e054","toolName":"read","content":[{"type":"text","text":"name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n"}],"isError":false,"durationMs":6,"timestamp":1793223382899}}
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+{"type":"message","id":"0e79c345","parentId":"d3b213b8","timestamp":"2026-10-28T21:36:26.260Z","message":{"role":"toolResult","toolCallId":"call_3111b0ce2713498aa7c3483ac787e961|fc_08ccae8539651651016ae26ad8d82081a299ea927a6b786ed2","toolName":"read","content":[{"type":"text","text":"name: left_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /left_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.07000001149\n    - 0.031999994336\n    - 0.065000047158\n    rotation_xyzw:\n    - -0.589367966393\n    - 0.589367919412\n    - -0.390698641271\n    - 0.39069867358\n    parent: left_arm:flange\n"}],"isError":false,"durationMs":3,"timestamp":1793223386260}}
+{"type":"message","id":"5cf30400","parentId":"0e79c345","timestamp":"2026-10-28T21:36:26.260Z","message":{"role":"toolResult","toolCallId":"call_9e1350636dfd4eca9c4f22161e8f732c|fc_08ccae8539651651016ae26ada1c4481a2b75c657b65c6b0a9","toolName":"read","content":[{"type":"text","text":"name: right_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /right_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.069999999887\n    - 0.031999976227\n    - 0.064999980711\n    rotation_xyzw:\n    - -0.58936798274\n    - 0.589367967612\n    - -0.390698619841\n    - 0.390698640561\n    parent: right_arm:flange\n"}],"isError":false,"durationMs":3,"timestamp":1793223386260}}
+{"type":"message","id":"95760425","parentId":"5cf30400","timestamp":"2026-10-28T21:36:26.260Z","message":{"role":"toolResult","toolCallId":"call_4c203ba69fc94f5caf87774cdc7d09b7|fc_08ccae8539651651016ae26ada1c4481a283438ce4917c1430","toolName":"read","content":[{"type":"text","text":"Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n"}],"isError":false,"durationMs":3,"timestamp":1793223386260}}
+{"type":"message","id":"d27fcb64","parentId":"95760425","timestamp":"2026-10-28T21:36:29.297Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_c4ef1e437938419b8b011e47c8d77b99|fc_08ccae8539651651016ae26adc2f5081a2b50a3fa296d64391","name":"devices","arguments":{"calls":[{"deviceID":"world","action":"load"},{"deviceID":"left_arm","action":"load"},{"deviceID":"right_arm","action":"load"},{"deviceID":"left_gripper","action":"load"},{"deviceID":"right_gripper","action":"load"},{"deviceID":"head_camera","action":"load"},{"deviceID":"left_camera","action":"load"},{"deviceID":"right_camera","action":"load"},{"deviceID":"policy","action":"load"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":2165,"output":107,"cacheRead":21248,"cacheWrite":0,"reasoning":0,"totalTokens":23520,"cost":{"input":0.021650000000000003,"output":0.005350000000000001,"cacheRead":0.021248,"cacheWrite":0,"total":0.048248}},"stopReason":"toolUse","timestamp":1793223386287,"responseId":"resp_08ccae8539651651016ae26ada9fe481a297d976b93b2818d3","rawStopReason":"completed","durationMs":3010,"thinkingLevel":"high"}}
+{"type":"message","id":"332ddba8","parentId":"d27fcb64","timestamp":"2026-10-28T21:37:17.921Z","message":{"role":"toolResult","toolCallId":"call_c4ef1e437938419b8b011e47c8d77b99|fc_08ccae8539651651016ae26adc2f5081a2b50a3fa296d64391","toolName":"devices","content":[{"type":"text","text":"{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}"}],"details":{"kinex":{"title":"devices: 9 calls","isError":false}},"isError":false,"durationMs":48622,"timestamp":1793223437921}}
+{"type":"message","id":"723cf79e","parentId":"332ddba8","timestamp":"2026-10-28T21:37:21.075Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_5b430d8f32364bbfb42a8c2fe430e603|fc_08ccae8539651651016ae26b0f996c81a2a3b0299e58773167","name":"exec","arguments":{"description":"inspect operation schemas","python":"from kinex import runtime, finish\nfinish({x:runtime.describe(x) for x in ['policy.infer','left_arm.joint_target','left_gripper.target','world.wait','head_camera.image']})"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":313,"output":65,"cacheRead":23424,"cacheWrite":0,"reasoning":0,"totalTokens":23802,"cost":{"input":0.0031300000000000004,"output":0.0032500000000000003,"cacheRead":0.023424,"cacheWrite":0,"total":0.029804}},"stopReason":"toolUse","timestamp":1793223437961,"responseId":"resp_08ccae8539651651016ae26b0e6d0481a2b6966d03bd7d6684","rawStopReason":"completed","durationMs":3114,"thinkingLevel":"high"}}
+{"type":"message","id":"0368b316","parentId":"723cf79e","timestamp":"2026-10-28T21:37:22.513Z","message":{"role":"toolResult","toolCallId":"call_5b430d8f32364bbfb42a8c2fe430e603|fc_08ccae8539651651016ae26b0f996c81a2a3b0299e58773167","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"\",\n  \"result\": {\n    \"policy.infer\": {\"description\":\"Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/kinex/policy/infer\",\"type\":\"kinex_interfaces/srv/Json\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"request\":\"string\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"response\":\"string\"},\"definitions\":{}},\n    \"left_arm.joint_target\": {\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\n    \"left_gripper.target\": {\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\n    \"world.wait\": {\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}},\n    \"head_camera.image\": {\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"height\":\"uint32\",\"width\":\"uint32\",\"encoding\":\"string\",\"is_bigendian\":\"uint8\",\"step\":\"uint32\",\"data\":\"sequence<uint8>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}}\n  }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":1437,"timestamp":1793223442513}}
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+{"type":"message","id":"63212478","parentId":"d795cfed","timestamp":"2026-10-28T21:37:26.679Z","message":{"role":"toolResult","toolCallId":"call_aafa6eda56b0431db5445c532842c4aa|fc_08ccae8539651651016ae26b15215881a2bad7cde781ccdd84","toolName":"read","content":[{"type":"text","text":"\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n                     max_actions=None, inference_args=None):\n    \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n    model serves a Json \"infer\" operation taking obs (and task) and returning\n    actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n    entries in flat-vector split order; named action maps use device names.\n    space \"device\" takes absolute joint values in URDF order, or end-effector\n    values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n    \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n    tip_link, frozen at the observation stamp. max_actions caps the horizon and\n    action_hz paces actions on the world clock. Joint trajectories submit\n    asynchronously; end-effector targets are paced by world waits before\n    returning. Returns actions_submitted, duration_s and ik_failure\n    (device, action_index, error) or None.\n\n    Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n    \"\"\"\n    import asyncio\n    from kinex.communication import duration\n    from kinex.helpers.frames import IDENTITY, transform_pose\n    from kinex.lib.geometry import invert_rt, rt_to_pose\n    plans = [_commanded(entry) for entry in command]\n    if len({entry[\"name\"] for entry in plans}) != len(plans):\n        raise ValueError(\"command must name each device once\")\n    wait = f\"{world}.wait\"\n    if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n        raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n    options = inference_args or {}\n    if set(options) & {\"obs\", \"task\"}:\n        raise ValueError(\"inference_args cannot override obs or task\")\n    data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n    actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n    if not actions:\n        raise ValueError(\"infer must return nonempty actions\")\n    actions = actions[:max_actions or len(actions)]\n    split = [_share(action, plans) for action in actions]\n    count, failure, sequences = len(split), None, {}\n    for entry in plans:\n        name, spec = entry[\"name\"], entry[\"spec\"]\n        samples = [item[name] for item in split[:count]]\n        if spec.get(\"space\", \"device\") != \"device\":\n            stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n            try:\n                state = await stream.next(timeout_s=10)\n            finally:\n                stream.close()\n            joints = entry[\"model\"]\n            seed = measured_positions(state, joints.joints)\n            base = f\"{name}/base\"\n            ik_frame = f\"{name}/{joints.tip}\"\n            target_frame = spec.get(\"target_frame\", ik_frame)\n            reference = spec.get(\"frame\", base)\n            # target_frame is an attached tool frame. Freeze its offset at the\n            # observation stamp while resolving the policy's Cartesian chunk;\n            # URDF FK of the measured joints places the tip.\n            offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n            tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n            current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n            converted = []\n            for sample in samples:\n                if spec[\"space\"] == \"pose_delta\":\n                    value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n                    current = value[\"translation\"] + value[\"rotation\"]\n                else:\n                    current = sample\n                ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n                goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n                try:\n                    seed = joints.ik(goal, seed)\n                except ValueError as error:\n                    failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n                    break\n                converted.append(seed)\n            samples = converted\n            count = min(count, len(samples))\n        sequences[name] = samples\n    if count == 0:\n        return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n    seconds = count / number(action_hz, \"action_hz\")\n    prepared, scalars = [], []\n    for entry in plans:\n        name, prof = entry[\"name\"], entry[\"profile\"]\n        samples = sequences[name][:count]\n        if entry[\"model\"]:\n            joint_command(prof, samples, entry[\"model\"].joints)\n            fields = {\"joint_names\": entry[\"model\"].names,\n                      \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n                                 for index, value in enumerate(samples)]}\n            operation = f\"{name}.joint_target\"\n        else:\n            target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n            targets = [target(prof, value[0]) for value in samples]\n            fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n            scalars.append((operation, targets))\n        prepared.append((operation, fields))\n\n    await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n    # End-effector targets persist. Timing belongs to this workflow and the world,\n    # never a device-side duration or a background worker.\n    if scalars:\n        for index in range(count):\n            if index:\n                await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n                                       for operation, values in scalars))\n            await io.request(wait, duration_s=seconds / count)\n    return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n    \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n    name = entry[\"device\"]\n    prof = profile(name)\n    kind = prof.get(\"type\")\n    if kind not in END_EFFECTORS | {\"joint\"}:\n        raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n    model = None if kind in END_EFFECTORS else JointModel(prof)\n    # A gripper or suction tool takes one command value whatever it reports back.\n    width = _cartesian_width(entry, prof)\n    return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n            \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n    \"\"\"One action, split across the commanded devices in order.\"\"\"\n    if isinstance(action, dict):\n        missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n        if missing:\n            raise ValueError(f\"action names no value for {', '.join(missing)}\")\n        return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n    vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n    shares, cursor = {}, 0\n    for entry in plan:\n        shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n        cursor += entry[\"width\"]\n    return shares\n\n\ndef _cartesian_width(entry, prof):\n    space = entry.get(\"space\", \"device\")\n    if space not in {\"device\", \"pose\", \"pose_delta\"}:\n        raise ValueError(f\"unknown policy space: {space}\")\n    if space == \"device\":\n        if \"frame\" in entry or \"target_frame\" in entry:\n            raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n        return None\n    if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n        raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n    return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n    \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n    action = vector(action, 6, \"pose_delta\")\n    angle = math.hypot(*action[3:])\n    scale = math.sin(angle / 2) / angle if angle else 0.5\n    rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n    return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n    value = number(value, \"action\")\n    if not -1 <= value <= 1:\n        raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n    return value\n\n\ndef _grip(profile, value):\n    \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n    grip = profile[\"grip\"]\n    if not isinstance(grip, dict):\n        raise ValueError(\"the gripper has no declared grip joint\")\n    return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n    \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n    value = _action(value)\n    if profile[\"vacuum\"] == \"binary\":\n        return 1.0 if value > 0 else 0.0\n    if profile[\"vacuum\"] != \"proportional\":\n        raise ValueError(\"the suction device declares no vacuum control\")\n    return (value + 1) / 2\n"}],"isError":false,"durationMs":4,"timestamp":1793223446679}}
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"}],"details":{"kinex":{"title":"image on head_camera"}},"isError":false,"durationMs":133,"timestamp":1793223449293}}
+{"type":"message","id":"346e94ec","parentId":"0d916554","timestamp":"2026-10-28T21:37:36.926Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_97f69f82def545d69f64000042e044a8|fc_08ccae8539651651016ae26b1aea6081a2b7e8af07602854ff","name":"write","arguments":{"path":".kinex/tools/handover_policy.py","content":"\"\"\"One bounded policy chunk followed by fresh measured feedback. Devices must be loaded.\"\"\"\nimport asyncio\nfrom kinex import communication, helpers, args, finish\n\nTASK = 'use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open'\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def main():\n    opts = args()\n    async with communication() as io:\n        images = []\n        for camera in ['head_camera', 'left_camera', 'right_camera']:\n            with io.subscribe(camera + '.image', depth=1) as stream:\n                image = await stream.next(timeout_s=10)\n            images.append(helpers.save_image(image))\n        result = await helpers.run_policy(io, 'policy', {'images': images},\n            [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n            action_hz=25, max_actions=opts.get('max_actions', 32))\n        states = {}\n        for device in DEVICES:\n            with io.subscribe(device + '.joint_state', depth=1) as stream:\n                state = await stream.next(timeout_s=10)\n            states[device] = {'position': list(state.position), 'stamp': state.header.stamp}\n        with io.subscribe('head_camera.image', depth=1) as stream:\n            image = await stream.next(timeout_s=10)\n        path = helpers.save_image(image)\n    finish({'execution': result, 'measured': states}, file=path)\n\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":301,"output":412,"cacheRead":27008,"cacheWrite":0,"reasoning":0,"totalTokens":27721,"cost":{"input":0.00301,"output":0.0206,"cacheRead":0.027007999999999997,"cacheWrite":0,"total":0.050617999999999996}},"stopReason":"toolUse","timestamp":1793223449323,"responseId":"resp_08ccae8539651651016ae26b19d71881a292100605483102d9","rawStopReason":"completed","durationMs":7603,"thinkingLevel":"high"}}
+{"type":"message","id":"d97ec9b0","parentId":"346e94ec","timestamp":"2026-10-28T21:37:36.929Z","message":{"role":"toolResult","toolCallId":"call_97f69f82def545d69f64000042e044a8|fc_08ccae8539651651016ae26b1aea6081a2b7e8af07602854ff","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/tools/handover_policy.py"}],"isError":false,"durationMs":2,"timestamp":1793223456929}}
+{"type":"message","id":"99102907","parentId":"d97ec9b0","timestamp":"2026-10-28T21:37:39.608Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_dbfd26603ee74ce5a556516cd3d58bf0|fc_08ccae8539651651016ae26b22d8b881a2a920f8a417752f5d","name":"exec","arguments":{"description":"grasp approach policy","file":".kinex/tools/handover_policy.py","timeout_s":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":223,"output":37,"cacheRead":27520,"cacheWrite":0,"reasoning":0,"totalTokens":27780,"cost":{"input":0.00223,"output":0.00185,"cacheRead":0.02752,"cacheWrite":0,"total":0.0316}},"stopReason":"toolUse","timestamp":1793223456961,"responseId":"resp_08ccae8539651651016ae26b21817c81a2806c069e4f8cb53b","rawStopReason":"completed","durationMs":2647,"thinkingLevel":"high"}}
+{"type":"message","id":"b5bff240","parentId":"99102907","timestamp":"2026-10-28T21:37:45.442Z","message":{"role":"toolResult","toolCallId":"call_dbfd26603ee74ce5a556516cd3d58bf0|fc_08ccae8539651651016ae26b22d8b881a2a920f8a417752f5d","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223460.918079201] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.000841967121232301,0.5760459899902344,0.5073635578155518,0.11193818598985672,0.38478362560272217,0.011398900300264359],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"left_gripper\":{\"position\":[1.0020332282239741],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_arm\":{\"position\":[-0.003331375541165471,0.00015323919069487602,0.004788832738995552,-0.0013015313306823373,-0.0026872919406741858,-0.002288535935804248],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_gripper\":{\"position\":[0.9922204071825201],\"stamp\":{\"sec\":0,\"nanosec\":640000000}}},\n    \"file\": \"/tmp/kinex-observation-vu2mzt27.png\"\n  }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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+{"type":"message","id":"94f007e4","parentId":"7cb874b5","timestamp":"2026-10-28T21:37:53.297Z","message":{"role":"toolResult","toolCallId":"call_66e5d7cd0a344460acc9e062f1d21b7c|fc_08ccae8539651651016ae26b2b4f5c81a2821f711cfcbf299e","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223469.135563916] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": 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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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+{"type":"message","id":"366115a6","parentId":"ac58b192","timestamp":"2026-10-28T21:38:01.032Z","message":{"role":"toolResult","toolCallId":"call_e58dedea80a245418706e48d32f83f5b|fc_08ccae8539651651016ae26b338de081a2a7775d59f40d5e10","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223477.284126808] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": 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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"devices: 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+{"type":"message","id":"e3468321","parentId":"1be3155b","timestamp":"2026-10-28T21:38:23.371Z","message":{"role":"toolResult","toolCallId":"call_ab5510d8cc4d4cfa9f91130a9b4e475c|fc_08ccae8539651651016ae26b4a016881a2a586a44823029688","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223500.049809102] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": 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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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+{"type":"message","id":"aaec1966","parentId":"480babf2","timestamp":"2026-10-28T21:38:30.307Z","message":{"role":"toolResult","toolCallId":"call_0adbc53793574001b52d8b79bcec99b3|fc_08ccae8539651651016ae26b562ef881a29716ef254f27abd6","toolName":"read","content":[{"type":"text","text":"\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n"}],"isError":false,"durationMs":3,"timestamp":1793223510307}}
+{"type":"message","id":"49d594c7","parentId":"aaec1966","timestamp":"2026-10-28T21:38:30.308Z","message":{"role":"toolResult","toolCallId":"call_4cc45f89363340c7aa0d9f58b8f06be6|fc_08ccae8539651651016ae26b56318081a294f16725444a70f3","toolName":"read","content":[{"type":"text","text":"<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->\n<robot name=\"aloha_left_arm\">\n  <link name=\"base_link\"/>\n  <link name=\"link1\"/>\n  <link name=\"link2\"/>\n  <link name=\"link3\"/>\n  <link name=\"link4\"/>\n  <link name=\"link5\"/>\n  <link name=\"flange\"/>\n  <joint name=\"fl_joint1\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"link1\"/>\n    <origin xyz=\"0 0 0.058\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint2\" type=\"revolute\">\n    <parent link=\"link1\"/>\n    <child link=\"link2\"/>\n    <origin xyz=\"0.025013 0.00060169 0.042\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint3\" type=\"revolute\">\n    <parent link=\"link2\"/>\n    <child link=\"link3\"/>\n    <origin xyz=\"-0.26396 0.0044548 0\" rpy=\"3.14158530718 0 -0.015928\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint4\" type=\"revolute\">\n    <parent link=\"link3\"/>\n    <child link=\"link4\"/>\n    <origin xyz=\"0.246 -0.00025 -0.06\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint5\" type=\"revolute\">\n    <parent link=\"link4\"/>\n    <child link=\"link5\"/>\n    <origin xyz=\"0.06775 0.0015 -0.0855\" rpy=\"0 0 -0.015928\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint6\" type=\"revolute\">\n    <parent link=\"link5\"/>\n    <child link=\"flange\"/>\n    <origin xyz=\"0.03095 0 0.0855\" rpy=\"3.14158530718 0 0\"/>\n    <axis xyz=\"1 0 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n</robot>\n"}],"isError":false,"durationMs":3,"timestamp":1793223510308}}
+{"type":"message","id":"22f2f198","parentId":"49d594c7","timestamp":"2026-10-28T21:38:30.308Z","message":{"role":"toolResult","toolCallId":"call_64a1b5c11fcd454ab5594f92b8c9f74b|fc_08ccae8539651651016ae26b56318081a2924df69d48a139b1","toolName":"read","content":[{"type":"text","text":"<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n"}],"isError":false,"durationMs":3,"timestamp":1793223510308}}
+{"type":"message","id":"0d6f892f","parentId":"22f2f198","timestamp":"2026-10-28T21:38:37.550Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Reconstructing red-block depth**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_08ccae8539651651016ae26b58266881a296f78725085a0fa7\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Reconstructing red-block depth**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mtdt41UEweewpxD4k79oao-BjCwazHnirLI8zI3jzSHyf4Tdw9bF4aVNdEueR99yq1lAKEyamU4_a8DKiUIFUFJRwYELiFGaDHAvLJsD_skxMPMBaB1iCEJz7B2OJu643LS2TLvkR8Df2RAOltTg6fOuHvz30a1yZ381w8plLvwJ8Uo3VxhbfLeLs41fR1BvxIL4dFlHg26WQRpOcXI6wdoOQy1mi_XeUF64Xxt0MPFtez4B71clQ9spc2j8w1oZ3DBwpWw2vm8N0jdQmurCqfkWDsCptInH40MxQc7zK8g3N5PNp88avDIGTr5h306GEcO2eFMFaxSwix6AhPOAHe40gsMzNs9zAd9TVDATtAdD-fk5WQ2_amFC_qwnR4e4_-CN55YTLm9IX7uqACkirlq11vouNbTNW6KVZsBP-fSxeDRQsPp9Mlv5x1w15idMyQw3uiQhEkfD2J5mzY0IpjHUKKNVI0j_GTFyFY3o9Fy7PBDRN38b92vSLNmnMOR7B4_nfTN9DDhzDyE9Q8g6hblV-DgQDYf_pZDGWYHsGgk36JONz7cww1CpOOgCSOENmy3n3a26wXffA40ooJwiifuLCABaFHGsAh632LbLHCpaLvoTq_jtu2fVzsblbVnUW1uht8bepHWLiQf1RLTpfnzqNaxsrzFkW6wzmI6UZrzWzrS-LyHW4qs1LGFi2Dd4375AeLldALU9WSE_9UCwwiw9JmBjfV1NraKmlNUILTEluX4m3_YgKCahgs5yilH7u6Ny5TuaA_xjoSayTuYFTibJbn6Y4cKbv3uoeW2FmqTHb4Tlkk0btYQiKsuM2oKeYQGcSyxAsp4rVgqjPSo5lS8dBDJgw9oZ69CUBdYgTV54Nl6UwAf6q4U1zyn9CMf0yr2edYZ_W_eitjDJymmopDjx918pTvEmpxzuUC6JL7BOWaN6gsM4vRyUhnhaB4bPDnGshm8vgErkNzupRgSRYruIdggPxwb_2zEyaetaQxps9jC_6i3Kj3JEFVPCdUOHVHcP2ATAcPSRr1lkYBqnax0b2TggPvulhRrFuZyF4JZlUG9irqxArpb_DExlQbWQprd-8Lgfu7St5THe1ed0HgNDBtsrE5SgceCXOLjuKwByELpjpKouzDuvE4Ok3kOcSZGDz5uodQ-Bq3Iyfs1it-82bMUZwqIdGMQKhPlVDQasOBh5n5CS9osPckjcBITzsgH189NeTCiaq1C4wnhxSzrx9Q-H9ZN3RAEX4Y03-uYOMZBL-ABaYGNXw_6DFMt2k9BPvChbmNf6ammDaQkxlHTQMaEsQCxvYxwVQ1la8XU5mff-hRygM5rJ9w7TmUP8PN8QHRHCaj15wCVB__T3WghklTHAQDCwH4lXcKepfgSoY3MTmvLXwctql2fUoQJM2mEakqOMKWpHdggAcwjQe5JQMyTOYnN6m7x4PgTbjKLYfL5nHCwas0e0mLy7ncnlYptbak7WZ0FUZV9wQBjdiJI5dJpSA4CLzOA_Rm6fpYKeDbbPREWCU3yjqzdiQ_LcMS-987zG_7Ygbx0v6GlpVK6UvZC01DkugaXLl-vwB0GJsqR9JNXLL__zCK6us0_VT8u6GAxDEeFs_0F9m2eeJVKcd9sDqBNlE9QzBUqB7TxyR8oLMNxWZuNeFM3MhzO392SGehpGy_AssLtcMSPbMV3O0cHi6jeBp6wHrOYi2ylVhuxj4lBWezR8-vxlq6VTaw0EOQQrWcA1J6PuNPvK36vaSJaC30cmyMj8pqBI8lf85ia4IPNWrbHjD8xu7WLElQt9vmAyxSQ2iVJ6mjEd3ed_M0fQNLdf42yt7QdaI3vFnOWJDCcv_eDq-0y9b5tsEV2Dgxn_J9TPxq4neR7ve-_ORXn7pbf_fCGzOHKSK1PC50NRixn7El1GVHjkG9QJdWncFOJ-Ix7Rcsddti-HnUTVcH-qDec8eSiMjJ5oowAem--VxGCZ9AELcCJ-VR3A58bjTbBBwmcmBeClTdNTuPBm8vH5MIaKDr6yGsQN7QoM8VzrCUscJR-oJstUyS5alZgOD3OAyOVF2eSd7aBGoeqGDllr-9JY7y-uYC9d2WuV95QqFlsKMMvUE8JUHdTgI3MnhFvtHc5oCGF8Aq8UuFh28fs-h8wPm0jpb2u4-mBLQmRmAwJcWoxT9sqcU21q9B_bwuzk3au6Ldz7ERElpiqCMG485i416qwumwt3FQVR47gtBg7BKrdzWHITsBW2AzipoBBdmV464Ap_yI5Yg9qPqddhhZ1lPalT1qjkLG4HtDAd8YSVJJ7K0gQ7dDP3bmLlAsDNXGbYL7ULNqf2FPV3o_3voFSgv_j7BaaMRZCASeOljUJLZaXL8WOFKAsr8i-hEFQIeK6CeS2eb_4eK26O6WuZ-qumSUobmkxUWQ3-tMwNzT7CL1Udmn07y7oZzDh\"}"},{"type":"toolCall","id":"call_ad0c092304d045caae710eef68ffc64e|fc_08ccae8539651651016ae26b5d730c81a2a3f06ca5ed26ef49","name":"exec","arguments":{"description":"measure scene geometry","python":"import asyncio, numpy as np\nfrom kinex import communication, helpers, finish\nasync def main():\n async with communication() as io:\n  f=await helpers.camera_frame(io,'head_camera')\n  finish({'frame_type':str(type(f)), 'keys':list(f) if isinstance(f,dict) else None})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1457,"output":171,"cacheRead":30464,"cacheWrite":0,"reasoning":70,"totalTokens":32092,"cost":{"input":0.014570000000000001,"output":0.00855,"cacheRead":0.030463999999999998,"cacheWrite":0,"total":0.053584}},"stopReason":"toolUse","timestamp":1793223510334,"responseId":"resp_08ccae8539651651016ae26b56c12c81a2b054447b457bf845","rawStopReason":"completed","durationMs":7215,"thinkingLevel":"high"}}
+{"type":"message","id":"9b35b133","parentId":"0d6f892f","timestamp":"2026-10-28T21:38:39.626Z","message":{"role":"toolResult","toolCallId":"call_ad0c092304d045caae710eef68ffc64e|fc_08ccae8539651651016ae26b5d730c81a2a3f06ca5ed26ef49","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223518.853304393] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"frame_type\": \"<class 'tuple'>\",\n    \"keys\": null\n  }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2075,"timestamp":1793223519626}}
+{"type":"message","id":"426b5575","parentId":"9b35b133","timestamp":"2026-10-28T21:38:41.954Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_01b5f2780dbc4b868019ad4c2dfe1f40|fc_08ccae8539651651016ae26b615b4081a29291e6a02e944ba4","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py","offset":1,"limit":170}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":167,"output":39,"cacheRead":32000,"cacheWrite":0,"reasoning":0,"totalTokens":32206,"cost":{"input":0.00167,"output":0.0019500000000000001,"cacheRead":0.032,"cacheWrite":0,"total":0.03562}},"stopReason":"toolUse","timestamp":1793223519659,"responseId":"resp_08ccae8539651651016ae26b6011fc81a284c2104ccc7003fa","rawStopReason":"completed","durationMs":2294,"thinkingLevel":"high"}}
+{"type":"message","id":"e35b8613","parentId":"426b5575","timestamp":"2026-10-28T21:38:41.959Z","message":{"role":"toolResult","toolCallId":"call_01b5f2780dbc4b868019ad4c2dfe1f40|fc_08ccae8539651651016ae26b615b4081a29291e6a02e944ba4","toolName":"read","content":[{"type":"text","text":"\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n"}],"isError":false,"durationMs":4,"timestamp":1793223521959}}
+{"type":"message","id":"b5e57b81","parentId":"e35b8613","timestamp":"2026-10-28T21:38:49.163Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_e3e9014848e04057871cd9d4d50054f4|fc_08ccae8539651651016ae26b63b7b881a289f3ed6fbd8a41e4","name":"exec","arguments":{"description":"locate block and pad","python":"import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import communication, helpers,finish,profile\nasync def main():\n async with communication() as io:\n  im,dep=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); d=helpers.image_array(dep)*.001\n  p=profile('head_camera'); fr=p['frames']['base']; r=Rotation.from_quat(fr['rotation_xyzw']); out={}\n  for name,mask in [('red',(a[:,:,0]>150)&(a[:,:,1]<80)&(a[:,:,2]<80)),('blue',(a[:,:,2]>150)&(a[:,:,0]<80)&(a[:,:,1]<80))]:\n   v,u=np.where(mask & (d>0)); z=d[v,u]; xyz=np.array([(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z]).T; w=r.apply(xyz)+fr['translation_m']\n   out[name]={'bounds': [w.min(0),w.max(0)],'median':np.median(w,0),'pixels':[u.min(),v.min(),u.max(),v.max()]}\n  for arm in ['left','right']:\n   out[arm]=await helpers.relative_pose(io,{'frame':arm+'_gripper:tcp'},{'frame':'world:base'})\n finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":438,"output":342,"cacheRead":32000,"cacheWrite":0,"reasoning":0,"totalTokens":32780,"cost":{"input":0.00438,"output":0.0171,"cacheRead":0.032,"cacheWrite":0,"total":0.05348}},"stopReason":"toolUse","timestamp":1793223521998,"responseId":"resp_08ccae8539651651016ae26b6270c481a2864036ba36c5463e","rawStopReason":"completed","durationMs":7164,"thinkingLevel":"high"}}
+{"type":"message","id":"337226d1","parentId":"b5e57b81","timestamp":"2026-10-28T21:38:51.990Z","message":{"role":"toolResult","toolCallId":"call_e3e9014848e04057871cd9d4d50054f4|fc_08ccae8539651651016ae26b63b7b881a289f3ed6fbd8a41e4","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223531.315992288] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"red\": {\"bounds\":[[-0.1951807511227014,0.1619458755140133,0.7403946746887228],[-0.13826190259151744,0.17530309445014303,0.9257439697592391]],\"median\":[-0.16744837463208706,0.16852240987786182,0.8441180625125884],\"pixels\":[77,0,115,67]},\n    \"blue\": {\"bounds\":[[0.0630836271454373,0.10104295310740785,0.7421885613696891],[0.16190775280083636,0.19940375685842826,0.746340314718351]],\"median\":[0.11238624941640478,0.14487610727741018,0.7460899470946761],\"pixels\":[199,55,244,86]},\n    \"left\": {\"pose\":[-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\"timestamp\":3.2},\n    \"right\": {\"pose\":[0.15039597241518135,-0.04177619542231542,0.9706620944772414,-0.5838925600381027,0.20359696955881887,0.7134881688183462,0.32947289004538605],\"timestamp\":3.2}\n  }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2826,"timestamp":1793223531989}}
+{"type":"message","id":"faf0d4fd","parentId":"337226d1","timestamp":"2026-10-28T21:39:20.244Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Positioning for tall block**\n\n**Aligning tool for tall block**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_08ccae8539651651016ae26b6f151c81a2adb5880e51ef748f\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Positioning for tall block**\"},{\"type\":\"summary_text\",\"text\":\"**Aligning tool for tall 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using the camera geometry to guide the grasp and handoff, checking the measured pose after each move.","textSignature":"{\"v\":1,\"id\":\"msg_08ccae8539651651016ae26b7cf40881a2b5d69d58eeb0b3e0\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_71bc1611296b4b78a0d238e63f5a7b31|fc_08ccae8539651651016ae26b7ee55c81a2afd7dc07a563eb35","name":"write","arguments":{"path":".kinex/tools/cartesian_move.py","content":"\"\"\"Bounded world-frame TCP motion with interpolated IK and measured endpoint reporting.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish, profile\nfrom kinex.communication import duration\n\nasync def main():\n    o=args(); arm=o.get('arm','left_arm'); side=arm.split('_')[0]; grip=side+'_gripper'\n    async with communication() as io:\n        model=helpers.joint_model(arm)\n        with io.subscribe(arm+'.joint_state',depth=1) as stream:\n            state=await stream.next(timeout_s=10)\n        q=list(state.position)\n        tcp=(await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'}))['pose']\n        base=profile(arm)['frames']['base']; rb=Rotation.from_quat(base['rotation_xyzw']); tb=np.array(base['translation_m'])\n        target=o.get('pose',tcp); seconds=o.get('duration_s',1.6)\n        n=max(1,int(seconds/.04)); rots=Slerp([0,1],Rotation.from_quat([tcp[3:],target[3:]])); points=[]\n        for i in range(1,n+1):\n            f=i/n; r=rots(f); pos=np.array(tcp[:3])*(1-f)+np.array(target[:3])*f\n            flange=pos-r.apply([.12,0,0]); local=rb.inv().apply(flange-tb); rot=rb.inv()*r\n            q=model.ik([*local,*rot.as_quat()],q)\n            points.append({'positions':q,'time_from_start':duration(i*seconds/n)})\n        if o.get('dry_run'):\n            finish({'start':tcp,'target':target,'q':q}); return\n        await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n        if 'grip' in o: await io.publish(grip+'.target',{'data':o['grip']})\n        wait=await io.request('world.wait',duration_s=seconds+o.get('settle_s',.16))\n        measured={}\n        for device in [arm,grip]:\n            with io.subscribe(device+'.joint_state',depth=1) as stream: s=await stream.next(timeout_s=10)\n            measured[device]=list(s.position)\n        actual=await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'})\n        with io.subscribe('head_camera.image',depth=1) as stream: im=await stream.next(timeout_s=10)\n        image=helpers.save_image(im)\n    finish({'wait':wait,'measured':measured,'tcp':actual},file=image)\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":555,"output":954,"cacheRead":32640,"cacheWrite":0,"reasoning":252,"totalTokens":34149,"cost":{"input":0.00555,"output":0.0477,"cacheRead":0.032639999999999995,"cacheWrite":0,"total":0.08589}},"stopReason":"toolUse","timestamp":1793223532016,"responseId":"resp_08ccae8539651651016ae26b6d259881a2869954831c3bf7bf","rawStopReason":"completed","durationMs":28228,"thinkingLevel":"high"}}
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"}],"details":{"kinex":{"title":"exec 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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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}\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"devices: 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}\n}"},{"type":"image","mimeType":"image/png","data":"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+{"type":"message","id":"2b8fad37","parentId":"66714a6f","timestamp":"2026-10-28T21:42:10.326Z","message":{"role":"toolResult","toolCallId":"call_c13de87615dd46518a94714d3242b769|fc_08ccae8539651651016ae26c2c975481a2a918167911900999","toolName":"exec","content":[{"type":"text","text":"{\n  \"output\": \"[INFO] [1793223726.748315164] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":15.200000000000001,\"waited_s\":0.4800000000000004},\n    \"measured\": {\"left_arm\":[-0.6601924300193787,2.072953224182129,1.3642902374267578,0.7160017490386963,-0.6606023907661438,0.03993125259876251],\"left_gripper\":[0.9805027436126362]},\n    \"tcp\": {\"pose\":[-0.04102687484682255,0.09136394694744493,0.9988741403592395,0.007356924120444551,0.00990022999458713,0.7224363383853434,0.6913274174325201],\"timestamp\":15.2},\n    \"file\": \"/tmp/kinex-observation-1ua02uy9.png\"\n  }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"devices: 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+{"type":"message","id":"2169b215","parentId":"7e4777ba","timestamp":"2026-10-28T21:44:08.423Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Building largest-component ROI**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_08ccae8539651651016ae26ca1aa8481a2b739dc56ecfecdaf\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Building largest-component ROI**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4myoYns2ZZBjq4C9yWAQ2YCT1EWFf_sEyLvpDlghcUiOQopoy1QJ9YmM0RRz8CxNPB1liYE9K7sbnhfZj5coD-cNNtjH3_szR51CyobxWR7N0zr7IZtmf4JRutgKVm2p6uIZn2VNlgHcW7UhqiWxZZ42NaJW5lRYBMSGVbIDYQjQ-pFinNYAnGW0XMWf_wk3sfPGnbec8uDwKv8c0dta5-RczBqpf3c-BbOzBqkxcPc6SvKHXybpZ2Ru8l0EM9I0I-AW0LkQDsMeC62d7bxORL8cH-Ie_CnMmAYL2b5OoON09gVr4XjFyE6Wf0PD0KfAel83M1u7nd8HLoP5RteYlz9w05IVtG3cz780E8maWiQNAaCc8lVG3uhyPuA2Z7FfgEzg5Gs-7W-_3EJqQtlM0b0zKrjt0lexQFyx4HgvGh56UYYUGprIfEvutLxRR7rsqOy9welw9ilOT-fCSJNnJJ56ADk_UzGRpVNc4uiLg0g5K2HN3jXK2j6g3PwGToc4HRbEgGRHHPXpP3evxdNmQLss7TEv6GFuHOlEPw4jHo4aZ22oETt5230olsjP39u2Yfx4TT75y-fze8N7l69RzC1VxC2RUL9tVnCo3s8YKkIf6NSu6J7oIE1JfeQkVuwuQIiuuyjS1XtDpFLvDWDu0e-X_atJMcRmbJpj46G7HDnCp6z04rHk1TSKfzJeEoC1utk6pzF_4ZWpgecRDLBmTAGtHxgf02URY8AJwT__Y9BtODMlxEEZ-Qkx1p8LTAmIPSvozoCxFjxQ1Ts21DK3HGv8mdUdDaKIUuzhFa86KBKj9tqJdkUqbE0c6WnxcUbIu0wwDJ_qRttsAjc7zcgt8sVMdej0nbep0mVLpj6eIRX23v96cTB6NilaPMz3schmMkZjcsX_9iMJ94NLawOsWBl2Rh1s-kywUsdQsNmx0FDDApN_riy9ozCTGgJWIIC4995oCF5vTGC2MhqpCUNLytSA9DXr5XYKMLtDMvsYqRbBaqWox39BKvRbbmuBFUUNEJEtpSu3xXk5oy8wL0LVpMCHC9__SvTGFppnw0-Bff5K1U2Jb3RcqJRF1yldkf3nrMtlC2p6V9Jx6BjcaauvHEAnQqmfx0La5DLIq9e5Xau8V85Q-3XCaWKxO96dQlyIfdiENkvMqPdu4Wm6SEqqINqzuU0tlCmzwvezLrm-p8KuVnrdqTGrh20CK7kj5685h3N4jETNr04abjgsUMCHs-alibfJzuZ06L4qQ3TO4O4o5R12kPnVmP0di8j_fTaZUxMhkSkwepNJgD5nONRM6s8Di0g9yidHVbbX6UShWBH5YfI0W_Og0qRkqZiRjY_eTM99711WYqVz10HZ5_UgncmSk0Vi_P_y-asrkoiS_5ZBEQCYUFyQdWzCyNoD16T8URphLIbxmZ-pabu6U2M36qr36C30_tJvH4UIHLL9-bPUW1Uv-Wnmf3-AvDqTgCmQT14VOC-yx0dAb0HPaKrmph_V0Bb6RfmzH8r01Xt06eh5IfYlR8lUFjRyCjrctn1pA9l3ypjf2EBlThwVgk-RgVvTkH2it_jFQfWF38a61DGftcIfh1I-WvoyNSxVFmbbqbkiiBNwLKCyeBduJFOSw4dTt21VK0WVnC8ie69jkBembXjtr4mqaYj5b0lQWngVPwyBNNI5k6aDU_WvziFq18u4s2oHkt8NbLCYdoVSRQtCN0yg5vpcR5AcMGNXYDBI-Y9_KgjF0z0MofVd_XwCKtBxauGgb_gvXSpSfLO9Nwrj-zpcSkiAi7yvkZbhmtTm6BackiuyTwOgcxayBjHX_jE1_UCAK7HTkKUZQSeAn4cuqNtUXsmuC-2LIzIDhdO3bMhDwP0eI9hTnaRFrfwLdVE-_pgwMqa1X9pNlafPVcvSaBGosRWNRMoFJDjt19bTh5h0Mnha5TBhcnc7G6By8SegmH2TZfoS7pD5PiW6ZvhrqHRtuRJTjYXTWGxKcdxoTWTIBvDNhVcT7J0rENYXrxmN1Pz5xJF1g3QhGc_hG5T9x_Y0XaGmNd4OS2r55AuzdMaZf3ApfjBiCdu37xdW7M7bEksm-7lvBEVpMzC2cORcIR7nQ7hEImbi7zgHx8M-L4glcbCI_SYSx3b_D8nPtvyYmMUVZ5RPnQQ3LfEmjSbjpM2PUUKyOn-R_5OJQHVS7xyx4PEoUxfv8hrA93ltok8olw==\"}"},{"type":"toolCall","id":"call_7601c444d1924c5d8b54cae42375943f|fc_08ccae8539651651016ae26ca49e8c81a2935fd086d8f4b25c","name":"edit","arguments":{"path":".kinex/tools/color_geometry.py","edits":[{"oldText":"from scipy.spatial.transform import Rotation\n","newText":"from scipy.spatial.transform import Rotation\nfrom scipy.ndimage import label\n"},{"oldText":"                v,u=np.where(mask&(d>0))\n                if len(u)<5: continue\n","newText":"                components,count=label(mask)\n                if not count: continue\n                sizes=np.bincount(components.ravel()); sizes[0]=0\n                mask=components==int(sizes.argmax())\n                # Near-gripper RGB-D can be inside the declared minimum range.\n                v,u=np.where(mask&(d>=.1)&(d<2))\n                if len(u)<5: continue\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":125,"output":229,"cacheRead":43776,"cacheWrite":0,"reasoning":50,"totalTokens":44130,"cost":{"input":0.00125,"output":0.01145,"cacheRead":0.043775999999999995,"cacheWrite":0,"total":0.056476}},"stopReason":"toolUse","timestamp":1793223839593,"responseId":"resp_08ccae8539651651016ae26ca0382881a281c517a74cb91964","rawStopReason":"completed","durationMs":8830,"thinkingLevel":"high"}}
+{"type":"message","id":"7bb995dc","parentId":"2169b215","timestamp":"2026-10-28T21:44:08.431Z","message":{"role":"toolResult","toolCallId":"call_7601c444d1924c5d8b54cae42375943f|fc_08ccae8539651651016ae26ca49e8c81a2935fd086d8f4b25c","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 2 block(s) in .kinex/tools/color_geometry.py."}],"details":{"diff":"  1 \"\"\"World-coordinate visible red/blue surface bounds from calibrated RGB-D.\"\"\"\n  2 import asyncio\n  3 import numpy as np\n  4 from scipy.spatial.transform import Rotation\n+ 5 from scipy.ndimage import label\n  5 from kinex import communication, helpers, args, finish, profile\n  6 async def main():\n  7     out={}\n  8     async with communication() as io:\n    ...\n 12             t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})\n 13             pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']\n 14             objects={}\n 15             for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:\n-16                 v,u=np.where(mask&(d>0))\n+17                 components,count=label(mask)\n+18                 if not count: continue\n+19                 sizes=np.bincount(components.ravel()); sizes[0]=0\n+20                 mask=components==int(sizes.argmax())\n+21                 # Near-gripper RGB-D can be inside the declared minimum range.\n+22                 v,u=np.where(mask&(d>=.1)&(d<2))\n 17                 if len(u)<5: continue\n 18                 z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T\n 19                 w=r.apply(xyz)+pose[:3]\n 20                 objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}\n    ...","patch":"--- .kinex/tools/color_geometry.py\n+++ .kinex/tools/color_geometry.py\n@@ -1,8 +1,9 @@\n \"\"\"World-coordinate visible red/blue surface bounds from calibrated RGB-D.\"\"\"\n import asyncio\n import numpy as np\n from scipy.spatial.transform import Rotation\n+from scipy.ndimage import label\n from kinex import communication, helpers, args, finish, profile\n async def main():\n     out={}\n     async with communication() as io:\n@@ -12,9 +13,14 @@\n             t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})\n             pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']\n             objects={}\n             for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:\n-                v,u=np.where(mask&(d>0))\n+                components,count=label(mask)\n+                if not count: continue\n+                sizes=np.bincount(components.ravel()); sizes[0]=0\n+                mask=components==int(sizes.argmax())\n+                # Near-gripper RGB-D can be inside the declared minimum range.\n+                v,u=np.where(mask&(d>=.1)&(d<2))\n                 if len(u)<5: continue\n                 z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T\n                 w=r.apply(xyz)+pose[:3]\n                 objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}\n","firstChangedLine":5},"isError":false,"durationMs":6,"timestamp":1793223848431}}
+{"type":"message","id":"fb15e3f6","parentId":"7bb995dc","timestamp":"2026-10-28T21:44:31.750Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_4312b178a5234e2b8111a2a90077805c|fc_08ccae8539651651016ae26caab28481a28e9d7cf3496c0212","name":"write","arguments":{"path":".kinex/memos/handover-block.md","content":"# ALOHA AgileX handover\n\nRead refreshed profiles and harness each episode. Native dt=.04 s; all waits sequential. No reset. Available tools: `handover_policy.py` bounded StarVLA chunk + measured feedback; `cartesian_move.py` world TCP interpolation/IK + feedback; `color_geometry.py` visible surface bounds, not object centers. Load devices through host. Inspect operation schemas.\n\nPolicy returned 16-action chunks, but first 80 frames did not grasp; geometric control worked more directly. Do not blindly reuse numerical scene positions: measure RGB-D each episode. Red object is a tall upright rectangular block, not a small cube. This episode front surface was x≈-.167,y≈.168, base z≈.740; blue pad center≈[.113,.150], top z≈.746. Block width/thickness≈.06 m. Wrist depth at near grasp is unreliable (<.1m), head views may occlude block; color geometry now filters largest component and valid range.\n\nGripper TCP +x approaches; fingers close along local y. World left orientation quat xyzw=[0,0,.707107,.707107] approaches +y, closes x. Left pregrasp [-.167,.09,.86], insertion [-.166,.19,.86], close 0; measured opening≈.74 with object. Lift to [-.163,.135,.98], stage [-.03,.09,1.0]. Block bottom then≈.879. Right orientation [0,0,1,0] approaches -x, closes y. Right pregrasp [.085,.107,.923], insertion [-.035,.108,.923], close; measured≈.76. Right grasps ~.077m below left; open left then withdraw along -y to -.07. Carry right [.125,.154,.94], lower [.137,.154,.803], seat [.140,.154,.787]. Actual TCP tracking lag 5–15 mm; block bottom≈TCP z-.046, center x≈TCP x-.02. Verify measurements instead of relying on targets. Right-held block visibly moved while left open and withdrawn.\n\nLatest episode: release target 1 at t20.8; world terminated during wait at20.92 (523 frames), not a full successful wait. Held devices, read final image/block over pad and right opening .763, left≈1.0. Restored right open target1 after hold, no further waits. Full-open arrival and native verdict were NOT available; owner finalizes. A terminal wait alone is never success evidence.\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":124,"output":567,"cacheRead":44032,"cacheWrite":0,"reasoning":0,"totalTokens":44723,"cost":{"input":0.00124,"output":0.02835,"cacheRead":0.044031999999999995,"cacheWrite":0,"total":0.07362199999999999}},"stopReason":"toolUse","timestamp":1793223848452,"responseId":"resp_08ccae8539651651016ae26ca8f93881a2b81b0cb282e1e03a","rawStopReason":"completed","durationMs":23298,"thinkingLevel":"high"}}
+{"type":"message","id":"5e715242","parentId":"fb15e3f6","timestamp":"2026-10-28T21:44:31.753Z","message":{"role":"toolResult","toolCallId":"call_4312b178a5234e2b8111a2a90077805c|fc_08ccae8539651651016ae26caab28481a28e9d7cf3496c0212","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/memos/handover-block.md"}],"isError":false,"durationMs":2,"timestamp":1793223871753}}
+{"type":"message","id":"7ec0985d","parentId":"5e715242","timestamp":"2026-10-28T21:44:34.975Z","message":{"role":"assistant","content":[{"type":"text","text":"Handed the block to the right arm and placed it on the blue pad. Commanded release, but the episode ended before full gripper opening could be verified.","textSignature":"{\"v\":1,\"id\":\"msg_08ccae8539651651016ae26cc1f00481a29048bc0605ab1e17\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":202,"output":37,"cacheRead":44544,"cacheWrite":0,"reasoning":0,"totalTokens":44783,"cost":{"input":0.00202,"output":0.00185,"cacheRead":0.044544,"cacheWrite":0,"total":0.048414}},"stopReason":"stop","timestamp":1793223871775,"responseId":"resp_08ccae8539651651016ae26cc0518081a2a3fcf56661bf06ab","rawStopReason":"completed","durationMs":3200,"thinkingLevel":"high"}}
+{"type":"custom","customType":"kinex-active-time","data":{"milliseconds":509673.148265},"id":"10f3bea0","parentId":"7ec0985d","timestamp":"2026-10-28T21:44:35.176Z"}
diff --git a/experiments/robotwin-native/kinex_policy-1/terminal.jpg b/experiments/robotwin-native/kinex_policy-1/terminal.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cd214025cb238af34919972b1314b1ba3ac2893a
Binary files /dev/null and b/experiments/robotwin-native/kinex_policy-1/terminal.jpg differ
diff --git a/experiments/robotwin-native/kinex_policy-1/tool-calls.json b/experiments/robotwin-native/kinex_policy-1/tool-calls.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab621a71833ca1737f716a0624fe85c53dc85b7d
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/tool-calls.json
@@ -0,0 +1,874 @@
+[
+  {
+    "name": "ls",
+    "input": {
+      "path": "/workspace"
+    },
+    "output": ".kinex/\nharness.md\ninstruction.md",
+    "call_id": "call_b93e1b1d30bb49cb930b357edb6cfa08|fc_08ccae8539651651016ae26ac7a39081a2a97abc8fe896e84b",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/workspace/harness.md",
+      "offset": null,
+      "limit": null
+    },
+    "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n",
+    "call_id": "call_130da6a804ab47dd804cdd0d8f9c2d35|fc_08ccae8539651651016ae26acc024881a2b27bf65eb04ae0af",
+    "is_error": false
+  },
+  {
+    "name": "find",
+    "input": {
+      "pattern": "**/*",
+      "path": "/workspace/.kinex",
+      "limit": 200
+    },
+    "output": "kinex.json\npython/\npython/.pixi/\npython/pixi.lock\npython/pixi.toml\npython/ros/\npython/ros/build/\npython/ros/build/.built_by\npython/ros/build/COLCON_IGNORE\npython/ros/build/kinex_interfaces/\npython/ros/build/kinex_interfaces/AMENT_IGNORE\npython/ros/build/kinex_interfaces/CTestCustom.cmake\npython/ros/build/kinex_interfaces/ament_cmake_export_targets/\npython/ros/build/kinex_interfaces/ament_cmake_export_targets/ament_cmake_export_targets-extras.cmake\npython/ros/build/kinex_interfaces/cmake_args.last\npython/ros/build/kinex_interfaces/colcon_build.rc\npython/ros/build/kinex_interfaces/kinex_interfaces__py/\npython/ros/build/kinex_interfaces/kinex_interfaces__py/CMakeFiles/\npython/ros/build/kinex_interfaces/kinex_interfaces__py/cmake_install.cmake\npython/ros/build/kinex_interfaces/rosidl_adapter__arguments__kinex_interfaces.json\npython/ros/build/kinex_interfaces/rosidl_cmake/\npython/ros/build/kinex_interfaces/rosidl_cmake/rosidl_cmake-extras.cmake\npython/ros/build/kinex_interfaces/rosidl_cmake/rosidl_cmake_aggregate_target-extras.cmake\npython/ros/build/kinex_interfaces/rosidl_cmake/rosidl_cmake_export_typesupport_libraries-extras.cmake\npython/ros/build/kinex_interfaces/rosidl_cmake/rosidl_cmake_export_typesupport_targets-extras.cmake\npython/ros/build/kinex_interfaces/rosidl_generator_c/\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/msg/\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/msg/rosidl_generator_c__visibility_control.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__description.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__functions.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__functions.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__struct.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__type_support.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/json__type_support.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__description.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__functions.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__functions.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__struct.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__type_support.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/lookup_transform__type_support.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__description.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__functions.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__functions.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__struct.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__type_support.c\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/detail/wait__type_support.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/json.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/lookup_transform.h\npython/ros/build/kinex_interfaces/rosidl_generator_c/kinex_interfaces/srv/wait.h\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/msg/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/msg/rosidl_generator_cpp__visibility_control.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/json.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/lookup_transform.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/wait.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_generator_type_description__arguments.json\npython/ros/build/kinex_interfaces/rosidl_typesupport_cpp__arguments.json\npython/ros/install/\npython/ros/install/.colcon_install_layout\npython/ros/install/COLCON_IGNORE\npython/ros/install/_local_setup_util_ps1.py\npython/ros/install/_local_setup_util_sh.py\npython/ros/install/kinex_interfaces/\npython/ros/install/kinex_interfaces/lib/\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_py.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_cpp.so\npython/ros/install/kinex_interfaces/lib/python3.12/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_fastrtps_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_introspection_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_json.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_lookup_transform.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_wait.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/PKG-INFO\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/SOURCES.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/dependency_links.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/top_level.txt\npython/ros/install/kinex_interfaces/share/\npython/ros/install/kinex_interfaces/share/ament_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/kinex_interfaces\npython/ros/install/kinex_interfaces/share/colcon-core/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/kinex_interfaces/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_dependencies-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_include_directories-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_libraries-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/ament_cmake_export_targets-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_pyExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_generator_pyExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/export_kinex_interfaces__rosidl_typesupport_fastrtps_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfacesConfig-version.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfacesConfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_generator_py-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cppExport-noconfig.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/kinex_interfaces__rosidl_typesupport_introspection_cppExport.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_aggregate_target-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_export_typesupport_libraries-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/cmake/rosidl_cmake_export_typesupport_targets-extras.cmake\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/ament_prefix_path.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/ament_prefix_path.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/path.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/path.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/environment/pythonpath.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.ps1\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.xml\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.srv\npython/ros/install/local_setup.bash\npython/ros/install/local_setup.sh\npython/ros/install/local_setup.zsh\npython/ros/install/setup.ps1\npython/ros/install/setup.sh\npython/ros-ready.json\nskills/\ntools/\n\n[200 results limit reached. Use limit=400 for more, or refine pattern]",
+    "call_id": "call_bf1481b3f06b424eb980c7bdd2b27d72|fc_08ccae8539651651016ae26ace7a3481a2b997a3d225505793",
+    "is_error": false
+  },
+  {
+    "name": "bash",
+    "input": {
+      "command": "ls .kinex/tools .kinex/skills .kinex/memos .kinex/devices; find .kinex/devices -name profile.yaml",
+      "timeout": null
+    },
+    "output": ".kinex/devices:\nhead_camera\nleft_arm\nleft_camera\nleft_gripper\npolicy\nright_arm\nright_camera\nright_gripper\nworld\n\n.kinex/memos:\n\n.kinex/skills:\n\n.kinex/tools:\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/policy/profile.yaml\n.kinex/devices/world/profile.yaml\n",
+    "call_id": "call_816b5ca02a114a64b4420799465afe07|fc_08ccae8539651651016ae26ad1758c81a2b2b68b8ff51795ee",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/workspace.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n",
+    "call_id": "call_a30019bfd91b48169cd7775acfc00bf5|fc_08ccae8539651651016ae26ad3f0d481a2828a63d83409b2b5",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/world/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n",
+    "call_id": "call_45af048065f64f73a1bbbdb48b85dc99|fc_08ccae8539651651016ae26ad6cd4481a293ec4db2e646860b",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_arm/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n",
+    "call_id": "call_5db8f7c6a60b43868668ed652f735d39|fc_08ccae8539651651016ae26ad6cd4481a2b747723e901c44aa",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/right_arm/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n",
+    "call_id": "call_8020a4743b7b43bf9facc60a7382a1e1|fc_08ccae8539651651016ae26ad6cd4481a28d26477c6e4a9d33",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_gripper/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "call_id": "call_361c4eaee00e4ed5b9e87e340a443899|fc_08ccae8539651651016ae26ad6cd4481a29299c5cde34e1586",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/right_gripper/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n",
+    "call_id": "call_d6e04324c9c94e7e8022689c8acd9262|fc_08ccae8539651651016ae26ad6cd4481a28f9e18f500a40d91",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/policy/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n  in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n  absolute ALOHA joint actions. Use the native run_policy tool with command order\n  left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n  one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n  created_at: '2026-10-28T21:34:56.980232+00:00'\n  updated_at: '2026-10-28T21:34:56.980232+00:00'\ncompute:\n  index: 0\n  kind: cuda\nartifacts: []\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: node.py\n    factory: create\n  overlays:\n  - env.sh\n  ready_timeout_s: 600\noperations:\n  infer:\n    description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n      RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n      left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n      +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n    transport: service\n    direction: input\n    endpoint: /kinex/policy/infer\n    type: kinex_interfaces/srv/Json\n    timeout_s: 120\n",
+    "call_id": "call_073226633db3436eaa004fd1b7f47bda|fc_08ccae8539651651016ae26ad6cd4481a294e04aa528646fdf",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/head_camera/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n",
+    "call_id": "call_5de315338fcb4ddc86f68b19eec4c4d4|fc_08ccae8539651651016ae26ad6cd4481a29faa31a2d212e054",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_camera/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: left_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /left_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.07000001149\n    - 0.031999994336\n    - 0.065000047158\n    rotation_xyzw:\n    - -0.589367966393\n    - 0.589367919412\n    - -0.390698641271\n    - 0.39069867358\n    parent: left_arm:flange\n",
+    "call_id": "call_3111b0ce2713498aa7c3483ac787e961|fc_08ccae8539651651016ae26ad8d82081a299ea927a6b786ed2",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/right_camera/profile.yaml",
+      "offset": null,
+      "limit": null
+    },
+    "output": "name: right_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /right_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.069999999887\n    - 0.031999976227\n    - 0.064999980711\n    rotation_xyzw:\n    - -0.58936798274\n    - 0.589367967612\n    - -0.390698619841\n    - 0.390698640561\n    parent: right_arm:flange\n",
+    "call_id": "call_9e1350636dfd4eca9c4f22161e8f732c|fc_08ccae8539651651016ae26ada1c4481a2b75c657b65c6b0a9",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt",
+      "offset": null,
+      "limit": null
+    },
+    "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n",
+    "call_id": "call_4c203ba69fc94f5caf87774cdc7d09b7|fc_08ccae8539651651016ae26ada1c4481a283438ce4917c1430",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "world",
+          "action": "load"
+        },
+        {
+          "deviceID": "left_arm",
+          "action": "load"
+        },
+        {
+          "deviceID": "right_arm",
+          "action": "load"
+        },
+        {
+          "deviceID": "left_gripper",
+          "action": "load"
+        },
+        {
+          "deviceID": "right_gripper",
+          "action": "load"
+        },
+        {
+          "deviceID": "head_camera",
+          "action": "load"
+        },
+        {
+          "deviceID": "left_camera",
+          "action": "load"
+        },
+        {
+          "deviceID": "right_camera",
+          "action": "load"
+        },
+        {
+          "deviceID": "policy",
+          "action": "load"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}",
+    "call_id": "call_c4ef1e437938419b8b011e47c8d77b99|fc_08ccae8539651651016ae26adc2f5081a2b50a3fa296d64391",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "inspect operation schemas",
+      "python": "from kinex import runtime, finish\nfinish({x:runtime.describe(x) for x in ['policy.infer','left_arm.joint_target','left_gripper.target','world.wait','head_camera.image']})"
+    },
+    "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"policy.infer\": {\"description\":\"Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/kinex/policy/infer\",\"type\":\"kinex_interfaces/srv/Json\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"request\":\"string\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"response\":\"string\"},\"definitions\":{}},\n    \"left_arm.joint_target\": {\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\n    \"left_gripper.target\": {\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\n    \"world.wait\": {\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}},\n    \"head_camera.image\": {\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"height\":\"uint32\",\"width\":\"uint32\",\"encoding\":\"string\",\"is_bigendian\":\"uint8\",\"step\":\"uint32\",\"data\":\"sequence<uint8>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}}\n  }\n}",
+    "call_id": "call_5b430d8f32364bbfb42a8c2fe430e603|fc_08ccae8539651651016ae26b0f996c81a2a3b0299e58773167",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py",
+      "offset": 1,
+      "limit": 240
+    },
+    "output": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n                     max_actions=None, inference_args=None):\n    \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n    model serves a Json \"infer\" operation taking obs (and task) and returning\n    actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n    entries in flat-vector split order; named action maps use device names.\n    space \"device\" takes absolute joint values in URDF order, or end-effector\n    values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n    \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n    tip_link, frozen at the observation stamp. max_actions caps the horizon and\n    action_hz paces actions on the world clock. Joint trajectories submit\n    asynchronously; end-effector targets are paced by world waits before\n    returning. Returns actions_submitted, duration_s and ik_failure\n    (device, action_index, error) or None.\n\n    Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n    \"\"\"\n    import asyncio\n    from kinex.communication import duration\n    from kinex.helpers.frames import IDENTITY, transform_pose\n    from kinex.lib.geometry import invert_rt, rt_to_pose\n    plans = [_commanded(entry) for entry in command]\n    if len({entry[\"name\"] for entry in plans}) != len(plans):\n        raise ValueError(\"command must name each device once\")\n    wait = f\"{world}.wait\"\n    if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n        raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n    options = inference_args or {}\n    if set(options) & {\"obs\", \"task\"}:\n        raise ValueError(\"inference_args cannot override obs or task\")\n    data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n    actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n    if not actions:\n        raise ValueError(\"infer must return nonempty actions\")\n    actions = actions[:max_actions or len(actions)]\n    split = [_share(action, plans) for action in actions]\n    count, failure, sequences = len(split), None, {}\n    for entry in plans:\n        name, spec = entry[\"name\"], entry[\"spec\"]\n        samples = [item[name] for item in split[:count]]\n        if spec.get(\"space\", \"device\") != \"device\":\n            stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n            try:\n                state = await stream.next(timeout_s=10)\n            finally:\n                stream.close()\n            joints = entry[\"model\"]\n            seed = measured_positions(state, joints.joints)\n            base = f\"{name}/base\"\n            ik_frame = f\"{name}/{joints.tip}\"\n            target_frame = spec.get(\"target_frame\", ik_frame)\n            reference = spec.get(\"frame\", base)\n            # target_frame is an attached tool frame. Freeze its offset at the\n            # observation stamp while resolving the policy's Cartesian chunk;\n            # URDF FK of the measured joints places the tip.\n            offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n            tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n            current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n            converted = []\n            for sample in samples:\n                if spec[\"space\"] == \"pose_delta\":\n                    value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n                    current = value[\"translation\"] + value[\"rotation\"]\n                else:\n                    current = sample\n                ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n                goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n                try:\n                    seed = joints.ik(goal, seed)\n                except ValueError as error:\n                    failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n                    break\n                converted.append(seed)\n            samples = converted\n            count = min(count, len(samples))\n        sequences[name] = samples\n    if count == 0:\n        return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n    seconds = count / number(action_hz, \"action_hz\")\n    prepared, scalars = [], []\n    for entry in plans:\n        name, prof = entry[\"name\"], entry[\"profile\"]\n        samples = sequences[name][:count]\n        if entry[\"model\"]:\n            joint_command(prof, samples, entry[\"model\"].joints)\n            fields = {\"joint_names\": entry[\"model\"].names,\n                      \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n                                 for index, value in enumerate(samples)]}\n            operation = f\"{name}.joint_target\"\n        else:\n            target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n            targets = [target(prof, value[0]) for value in samples]\n            fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n            scalars.append((operation, targets))\n        prepared.append((operation, fields))\n\n    await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n    # End-effector targets persist. Timing belongs to this workflow and the world,\n    # never a device-side duration or a background worker.\n    if scalars:\n        for index in range(count):\n            if index:\n                await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n                                       for operation, values in scalars))\n            await io.request(wait, duration_s=seconds / count)\n    return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n    \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n    name = entry[\"device\"]\n    prof = profile(name)\n    kind = prof.get(\"type\")\n    if kind not in END_EFFECTORS | {\"joint\"}:\n        raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n    model = None if kind in END_EFFECTORS else JointModel(prof)\n    # A gripper or suction tool takes one command value whatever it reports back.\n    width = _cartesian_width(entry, prof)\n    return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n            \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n    \"\"\"One action, split across the commanded devices in order.\"\"\"\n    if isinstance(action, dict):\n        missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n        if missing:\n            raise ValueError(f\"action names no value for {', '.join(missing)}\")\n        return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n    vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n    shares, cursor = {}, 0\n    for entry in plan:\n        shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n        cursor += entry[\"width\"]\n    return shares\n\n\ndef _cartesian_width(entry, prof):\n    space = entry.get(\"space\", \"device\")\n    if space not in {\"device\", \"pose\", \"pose_delta\"}:\n        raise ValueError(f\"unknown policy space: {space}\")\n    if space == \"device\":\n        if \"frame\" in entry or \"target_frame\" in entry:\n            raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n        return None\n    if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n        raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n    return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n    \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n    action = vector(action, 6, \"pose_delta\")\n    angle = math.hypot(*action[3:])\n    scale = math.sin(angle / 2) / angle if angle else 0.5\n    rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n    return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n    value = number(value, \"action\")\n    if not -1 <= value <= 1:\n        raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n    return value\n\n\ndef _grip(profile, value):\n    \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n    grip = profile[\"grip\"]\n    if not isinstance(grip, dict):\n        raise ValueError(\"the gripper has no declared grip joint\")\n    return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n    \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n    value = _action(value)\n    if profile[\"vacuum\"] == \"binary\":\n        return 1.0 if value > 0 else 0.0\n    if profile[\"vacuum\"] != \"proportional\":\n        raise ValueError(\"the suction device declares no vacuum control\")\n    return (value + 1) / 2\n",
+    "call_id": "call_aafa6eda56b0431db5445c532842c4aa|fc_08ccae8539651651016ae26b15215881a2bad7cde781ccdd84",
+    "is_error": false
+  },
+  {
+    "name": "device",
+    "input": {
+      "deviceID": "head_camera",
+      "action": "image"
+    },
+    "output": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-orrg2pv4.png\"\n}",
+    "call_id": "call_6d05b38858304b3bb75a6d40e1fb933a|fc_08ccae8539651651016ae26b18c8ac81a2990f177ed78f48db",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/tools/handover_policy.py",
+      "content": "\"\"\"One bounded policy chunk followed by fresh measured feedback. Devices must be loaded.\"\"\"\nimport asyncio\nfrom kinex import communication, helpers, args, finish\n\nTASK = 'use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open'\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def main():\n    opts = args()\n    async with communication() as io:\n        images = []\n        for camera in ['head_camera', 'left_camera', 'right_camera']:\n            with io.subscribe(camera + '.image', depth=1) as stream:\n                image = await stream.next(timeout_s=10)\n            images.append(helpers.save_image(image))\n        result = await helpers.run_policy(io, 'policy', {'images': images},\n            [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n            action_hz=25, max_actions=opts.get('max_actions', 32))\n        states = {}\n        for device in DEVICES:\n            with io.subscribe(device + '.joint_state', depth=1) as stream:\n                state = await stream.next(timeout_s=10)\n            states[device] = {'position': list(state.position), 'stamp': state.header.stamp}\n        with io.subscribe('head_camera.image', depth=1) as stream:\n            image = await stream.next(timeout_s=10)\n        path = helpers.save_image(image)\n    finish({'execution': result, 'measured': states}, file=path)\n\nasyncio.run(main())\n"
+    },
+    "output": "Successfully wrote to .kinex/tools/handover_policy.py",
+    "call_id": "call_97f69f82def545d69f64000042e044a8|fc_08ccae8539651651016ae26b1aea6081a2b7e8af07602854ff",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "grasp approach policy",
+      "file": ".kinex/tools/handover_policy.py",
+      "timeout_s": 180
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223460.918079201] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.000841967121232301,0.5760459899902344,0.5073635578155518,0.11193818598985672,0.38478362560272217,0.011398900300264359],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"left_gripper\":{\"position\":[1.0020332282239741],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_arm\":{\"position\":[-0.003331375541165471,0.00015323919069487602,0.004788832738995552,-0.0013015313306823373,-0.0026872919406741858,-0.002288535935804248],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_gripper\":{\"position\":[0.9922204071825201],\"stamp\":{\"sec\":0,\"nanosec\":640000000}}},\n    \"file\": \"/tmp/kinex-observation-vu2mzt27.png\"\n  }\n}",
+    "call_id": "call_dbfd26603ee74ce5a556516cd3d58bf0|fc_08ccae8539651651016ae26b22d8b881a2a920f8a417752f5d",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "approach red block",
+      "file": ".kinex/tools/handover_policy.py",
+      "timeout_s": 180
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223469.135563916] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04086500033736229,1.74478018283844,1.4346884489059448,0.307611346244812,1.0182968378067017,0.03287385031580925],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"left_gripper\":{\"position\":[0.9985552294687792],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_arm\":{\"position\":[0.012346736155450344,0.08589094132184982,0.07706005871295929,-0.05403941124677658,-0.005238042213022709,-0.005991658661514521],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_gripper\":{\"position\":[0.9958593384786086],\"stamp\":{\"sec\":1,\"nanosec\":280000000}}},\n    \"file\": \"/tmp/kinex-observation-n_o6e3b9.png\"\n  }\n}",
+    "call_id": "call_66e5d7cd0a344460acc9e062f1d21b7c|fc_08ccae8539651651016ae26b2b4f5c81a2821f711cfcbf299e",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "align left grasp",
+      "file": ".kinex/tools/handover_policy.py",
+      "timeout_s": 180
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223477.284126808] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.058308765292167664,1.929189920425415,1.599496841430664,0.3592958450317383,1.0783706903457642,-0.016372166574001312],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"left_gripper\":{\"position\":[1.0109648487784646],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_arm\":{\"position\":[0.09248543530702591,0.4811040759086609,0.44249579310417175,-0.3714327812194824,0.0018695585895329714,-0.09739844501018524],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_gripper\":{\"position\":[0.9959367567842657],\"stamp\":{\"sec\":1,\"nanosec\":920000000}}},\n    \"file\": \"/tmp/kinex-observation-u4ddnv07.png\"\n  }\n}",
+    "call_id": "call_e58dedea80a245418706e48d32f83f5b|fc_08ccae8539651651016ae26b338de081a2a7775d59f40d5e10",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "close left grasp",
+      "file": ".kinex/tools/handover_policy.py",
+      "timeout_s": 180
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223486.950859814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04435889795422554,1.231294870376587,1.0781995058059692,0.17081087827682495,0.6730543971061707,-0.069943368434906],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"left_gripper\":{\"position\":[1.0099254927851937],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_arm\":{\"position\":[0.2739063501358032,1.267305612564087,1.2323172092437744,-0.9213956594467163,0.01157537754625082,-0.31220439076423645],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_gripper\":{\"position\":[0.9980137077244846],\"stamp\":{\"sec\":2,\"nanosec\":560000000}}},\n    \"file\": \"/tmp/kinex-observation-2otm1ktx.png\"\n  }\n}",
+    "call_id": "call_7bf474a854ed4138ac527b4a04154d94|fc_08ccae8539651651016ae26b3d71f881a287628609600ac816",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "left_camera",
+          "action": "image"
+        },
+        {
+          "deviceID": "right_camera",
+          "action": "image"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-z3rv78oi.png\"},\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dch2bguz.png\"}\n  ]\n}",
+    "call_id": "call_03526e81f05043ccafaed3fb41137c8a|fc_08ccae8539651651016ae26b47d61c81a29f746a86d366c4aa",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "reobserve and infer handover",
+      "file": ".kinex/tools/handover_policy.py",
+      "timeout_s": 180
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223500.049809102] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.0294959656894207,0.6043052077293396,0.5100125670433044,0.10033784061670303,0.33127984404563904,-0.05462042614817619],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"left_gripper\":{\"position\":[1.004523279992017],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_arm\":{\"position\":[0.36748456954956055,1.8742890357971191,1.8310723304748535,-1.2713841199874878,-0.005831398069858551,-0.4040740430355072],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_gripper\":{\"position\":[1.0009333870627664],\"stamp\":{\"sec\":3,\"nanosec\":200000000}}},\n    \"file\": \"/tmp/kinex-observation-mpnweq9k.png\"\n  }\n}",
+    "call_id": "call_ab5510d8cc4d4cfa9f91130a9b4e475c|fc_08ccae8539651651016ae26b4a016881a2a586a44823029688",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py",
+      "offset": null,
+      "limit": null
+    },
+    "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n",
+    "call_id": "call_0adbc53793574001b52d8b79bcec99b3|fc_08ccae8539651651016ae26b562ef881a29716ef254f27abd6",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_arm/arm.urdf",
+      "offset": null,
+      "limit": null
+    },
+    "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->\n<robot name=\"aloha_left_arm\">\n  <link name=\"base_link\"/>\n  <link name=\"link1\"/>\n  <link name=\"link2\"/>\n  <link name=\"link3\"/>\n  <link name=\"link4\"/>\n  <link name=\"link5\"/>\n  <link name=\"flange\"/>\n  <joint name=\"fl_joint1\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"link1\"/>\n    <origin xyz=\"0 0 0.058\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint2\" type=\"revolute\">\n    <parent link=\"link1\"/>\n    <child link=\"link2\"/>\n    <origin xyz=\"0.025013 0.00060169 0.042\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint3\" type=\"revolute\">\n    <parent link=\"link2\"/>\n    <child link=\"link3\"/>\n    <origin xyz=\"-0.26396 0.0044548 0\" rpy=\"3.14158530718 0 -0.015928\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint4\" type=\"revolute\">\n    <parent link=\"link3\"/>\n    <child link=\"link4\"/>\n    <origin xyz=\"0.246 -0.00025 -0.06\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint5\" type=\"revolute\">\n    <parent link=\"link4\"/>\n    <child link=\"link5\"/>\n    <origin xyz=\"0.06775 0.0015 -0.0855\" rpy=\"0 0 -0.015928\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint6\" type=\"revolute\">\n    <parent link=\"link5\"/>\n    <child link=\"flange\"/>\n    <origin xyz=\"0.03095 0 0.0855\" rpy=\"3.14158530718 0 0\"/>\n    <axis xyz=\"1 0 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n</robot>\n",
+    "call_id": "call_4cc45f89363340c7aa0d9f58b8f06be6|fc_08ccae8539651651016ae26b56318081a294f16725444a70f3",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": ".kinex/devices/left_gripper/gripper.urdf",
+      "offset": null,
+      "limit": null
+    },
+    "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n",
+    "call_id": "call_64a1b5c11fcd454ab5594f92b8c9f74b|fc_08ccae8539651651016ae26b56318081a2924df69d48a139b1",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "measure scene geometry",
+      "python": "import asyncio, numpy as np\nfrom kinex import communication, helpers, finish\nasync def main():\n async with communication() as io:\n  f=await helpers.camera_frame(io,'head_camera')\n  finish({'frame_type':str(type(f)), 'keys':list(f) if isinstance(f,dict) else None})\nasyncio.run(main())"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223518.853304393] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"frame_type\": \"<class 'tuple'>\",\n    \"keys\": null\n  }\n}",
+    "call_id": "call_ad0c092304d045caae710eef68ffc64e|fc_08ccae8539651651016ae26b5d730c81a2a3f06ca5ed26ef49",
+    "is_error": false
+  },
+  {
+    "name": "read",
+    "input": {
+      "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py",
+      "offset": 1,
+      "limit": 170
+    },
+    "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n",
+    "call_id": "call_01b5f2780dbc4b868019ad4c2dfe1f40|fc_08ccae8539651651016ae26b615b4081a29291e6a02e944ba4",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "locate block and pad",
+      "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import communication, helpers,finish,profile\nasync def main():\n async with communication() as io:\n  im,dep=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); d=helpers.image_array(dep)*.001\n  p=profile('head_camera'); fr=p['frames']['base']; r=Rotation.from_quat(fr['rotation_xyzw']); out={}\n  for name,mask in [('red',(a[:,:,0]>150)&(a[:,:,1]<80)&(a[:,:,2]<80)),('blue',(a[:,:,2]>150)&(a[:,:,0]<80)&(a[:,:,1]<80))]:\n   v,u=np.where(mask & (d>0)); z=d[v,u]; xyz=np.array([(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z]).T; w=r.apply(xyz)+fr['translation_m']\n   out[name]={'bounds': [w.min(0),w.max(0)],'median':np.median(w,0),'pixels':[u.min(),v.min(),u.max(),v.max()]}\n  for arm in ['left','right']:\n   out[arm]=await helpers.relative_pose(io,{'frame':arm+'_gripper:tcp'},{'frame':'world:base'})\n finish(out)\nasyncio.run(main())"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223531.315992288] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"red\": {\"bounds\":[[-0.1951807511227014,0.1619458755140133,0.7403946746887228],[-0.13826190259151744,0.17530309445014303,0.9257439697592391]],\"median\":[-0.16744837463208706,0.16852240987786182,0.8441180625125884],\"pixels\":[77,0,115,67]},\n    \"blue\": {\"bounds\":[[0.0630836271454373,0.10104295310740785,0.7421885613696891],[0.16190775280083636,0.19940375685842826,0.746340314718351]],\"median\":[0.11238624941640478,0.14487610727741018,0.7460899470946761],\"pixels\":[199,55,244,86]},\n    \"left\": {\"pose\":[-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\"timestamp\":3.2},\n    \"right\": {\"pose\":[0.15039597241518135,-0.04177619542231542,0.9706620944772414,-0.5838925600381027,0.20359696955881887,0.7134881688183462,0.32947289004538605],\"timestamp\":3.2}\n  }\n}",
+    "call_id": "call_e3e9014848e04057871cd9d4d50054f4|fc_08ccae8539651651016ae26b63b7b881a289f3ed6fbd8a41e4",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/tools/cartesian_move.py",
+      "content": "\"\"\"Bounded world-frame TCP motion with interpolated IK and measured endpoint reporting.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish, profile\nfrom kinex.communication import duration\n\nasync def main():\n    o=args(); arm=o.get('arm','left_arm'); side=arm.split('_')[0]; grip=side+'_gripper'\n    async with communication() as io:\n        model=helpers.joint_model(arm)\n        with io.subscribe(arm+'.joint_state',depth=1) as stream:\n            state=await stream.next(timeout_s=10)\n        q=list(state.position)\n        tcp=(await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'}))['pose']\n        base=profile(arm)['frames']['base']; rb=Rotation.from_quat(base['rotation_xyzw']); tb=np.array(base['translation_m'])\n        target=o.get('pose',tcp); seconds=o.get('duration_s',1.6)\n        n=max(1,int(seconds/.04)); rots=Slerp([0,1],Rotation.from_quat([tcp[3:],target[3:]])); points=[]\n        for i in range(1,n+1):\n            f=i/n; r=rots(f); pos=np.array(tcp[:3])*(1-f)+np.array(target[:3])*f\n            flange=pos-r.apply([.12,0,0]); local=rb.inv().apply(flange-tb); rot=rb.inv()*r\n            q=model.ik([*local,*rot.as_quat()],q)\n            points.append({'positions':q,'time_from_start':duration(i*seconds/n)})\n        if o.get('dry_run'):\n            finish({'start':tcp,'target':target,'q':q}); return\n        await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n        if 'grip' in o: await io.publish(grip+'.target',{'data':o['grip']})\n        wait=await io.request('world.wait',duration_s=seconds+o.get('settle_s',.16))\n        measured={}\n        for device in [arm,grip]:\n            with io.subscribe(device+'.joint_state',depth=1) as stream: s=await stream.next(timeout_s=10)\n            measured[device]=list(s.position)\n        actual=await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'})\n        with io.subscribe('head_camera.image',depth=1) as stream: im=await stream.next(timeout_s=10)\n        image=helpers.save_image(im)\n    finish({'wait':wait,'measured':measured,'tcp':actual},file=image)\nasyncio.run(main())\n"
+    },
+    "output": "Successfully wrote to .kinex/tools/cartesian_move.py",
+    "call_id": "call_71bc1611296b4b78a0d238e63f5a7b31|fc_08ccae8539651651016ae26b7ee55c81a2afd7dc07a563eb35",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "plan left block approach",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "pose": [
+          -0.167,
+          0.09,
+          0.86,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 2,
+        "dry_run": true
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223565.977362546] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"start\": [-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\n    \"target\": [-0.167,0.09,0.86,0,0,0.70710678,0.70710678],\n    \"q\": [-0.37572560008508377,2.272537219262441,0.9478505586577836,1.3291237834325125,-0.32947784190081975,0.012933085784625174]\n  }\n}",
+    "call_id": "call_9952cf89eb744af2b8cbffe657bed045|fc_08ccae8539651651016ae26b8afccc81a2afcac521bdbff710",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "left pregrasp motion",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "pose": [
+          -0.167,
+          0.09,
+          0.86,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 2,
+        "grip": 1
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223571.307316769] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":5.36,\"waited_s\":2.16},\n    \"measured\": {\"left_arm\":[-0.36332982778549194,2.2126855850219727,0.8944761157035828,1.319221019744873,-0.30946946144104004,0.010132407769560814],\"left_gripper\":[1.0015521916476162]},\n    \"tcp\": {\"pose\":[-0.1736376085877378,0.08114210634963587,0.8698353295464993,-0.0019216401989559503,0.00033106205038283427,0.7041296202769071,0.7100687822637771],\"timestamp\":5.36},\n    \"file\": \"/tmp/kinex-observation-isoz5fmq.png\"\n  }\n}",
+    "call_id": "call_9d081ed3d01c42deb851ceb545a56207|fc_08ccae8539651651016ae26b9084f881a28c72b274a17a9916",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "left grasp insertion",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "pose": [
+          -0.166,
+          0.19,
+          0.86,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 1.2,
+        "grip": 1,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223586.167183748] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.8,\"waited_s\":1.4399999999999995},\n    \"measured\": {\"left_arm\":[-0.3176051676273346,2.466261625289917,1.4066729545593262,1.0674532651901245,-0.2630546987056732,0.010167332366108894],\"left_gripper\":[1.0017503770914944]},\n    \"tcp\": {\"pose\":[-0.16256753019454037,0.1836445965947115,0.8630782542882497,0.0011261512119247997,-0.002236338043285821,0.7049367356530278,0.7092657677503246],\"timestamp\":6.8},\n    \"file\": \"/tmp/kinex-observation-1254cd4e.png\"\n  }\n}",
+    "call_id": "call_bcb95f3488544f8492645067724488a5|fc_08ccae8539651651016ae26b9ea01c81a29c4cde48f7e394a7",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "secure left grasp",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "duration_s": 0.4,
+        "grip": 0,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223596.492892278] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":7.44,\"waited_s\":0.6400000000000006},\n    \"measured\": {\"left_arm\":[-0.3172675371170044,2.46722412109375,1.408494234085083,1.067131757736206,-0.2634282112121582,0.011745059862732887],\"left_gripper\":[0.7420821664008228]},\n    \"tcp\": {\"pose\":[-0.16274491039040148,0.18399577706677667,0.8631423899018195,0.0018189100348115054,-0.0019111422555692988,0.7051953688926306,0.70900813168394],\"timestamp\":7.44},\n    \"file\": \"/tmp/kinex-observation-oh4rbjpz.png\"\n  }\n}",
+    "call_id": "call_d2f4b8faec684d8b95827a648459195d|fc_08ccae8539651651016ae26ba9b98c81a2873c9b4344894296",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "lift red block with left arm",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "pose": [
+          -0.163,
+          0.135,
+          0.98,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 1.2,
+        "grip": 0,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223606.394584128] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.88,\"waited_s\":1.4400000000000004},\n    \"measured\": {\"left_arm\":[-0.341536283493042,2.0650742053985596,1.2379626035690308,0.8357267379760742,-0.3042803704738617,0.016574207693338394],\"left_gripper\":[0.7436837025664069]},\n    \"tcp\": {\"pose\":[-0.1662269352506661,0.13941764966077147,0.975097409892858,0.0015167229424354568,-0.0011079411215326478,0.7093387285919696,0.7048652638182104],\"timestamp\":8.88},\n    \"file\": \"/tmp/kinex-observation-1azh43uv.png\"\n  }\n}",
+    "call_id": "call_923ed3d471104e4091e9fc844c6ec712|fc_08ccae8539651651016ae26bb4490081a2970d81c5b693ebc2",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "stage handover",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "pose": [
+          -0.03,
+          0.09,
+          1,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 1.6,
+        "grip": 0,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223626.588877790] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"left_arm\":[-0.6534572243690491,2.0685434341430664,1.3600218296051025,0.7217471599578857,-0.6136956214904785,0.018981793895363808],\"left_gripper\":[0.7409415163777091]},\n    \"tcp\": {\"pose\":[-0.0379849155451224,0.09244707613302833,1.0006879962717734,0.0008205284096499977,-0.0008811871467054494,0.7084815094335674,0.7057283479024175],\"timestamp\":10.72},\n    \"file\": \"/tmp/kinex-observation-pxoki3te.png\"\n  }\n}",
+    "call_id": "call_eca887a7292d4d8db29797a9431520e6|fc_08ccae8539651651016ae26bc833e881a2ad7c20b1a10f9702",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "right handover pregrasp",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          0.085,
+          0.107,
+          0.923,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 1.4,
+        "grip": 1,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223652.453391092] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":12.36,\"waited_s\":1.6399999999999988},\n    \"measured\": {\"right_arm\":[0.12191265821456909,2.4633967876434326,1.7892687320709229,0.5763887166976929,-1.372113585472107,-0.07960568368434906],\"right_gripper\":[1.0065756440162659]},\n    \"tcp\": {\"pose\":[0.07980928558945952,0.10938215926913314,0.9291508280392765,-0.013642548475539562,0.007638461258456428,0.9996989541629445,0.018908617776310862],\"timestamp\":12.36},\n    \"file\": \"/tmp/kinex-observation-hwvop_qr.png\"\n  }\n}",
+    "call_id": "call_783bebdc6b0e4bccb4e89f3766032706|fc_08ccae8539651651016ae26bdf9eec81a284e406b220f38aa8",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "right_camera",
+          "action": "image"
+        },
+        {
+          "deviceID": "left_camera",
+          "action": "image"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-w35ecyf0.png\"},\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-ug63nito.png\"}\n  ]\n}",
+    "call_id": "call_8ec9a450baad4f0e858e77f64c9f558a|fc_08ccae8539651651016ae26befd69481a283efddb27db63e42",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/tools/color_geometry.py",
+      "content": "\"\"\"World-coordinate visible red/blue surface bounds from calibrated RGB-D.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import communication, helpers, args, finish, profile\nasync def main():\n    out={}\n    async with communication() as io:\n        for camera in args().get('cameras',['head_camera','right_camera']):\n            im,depth=await helpers.camera_frame(io,camera)\n            a=helpers.image_array(im); d=helpers.image_array(depth)*.001\n            t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})\n            pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']\n            objects={}\n            for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:\n                v,u=np.where(mask&(d>0))\n                if len(u)<5: continue\n                z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T\n                w=r.apply(xyz)+pose[:3]\n                objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}\n            out[camera]=objects\n    finish(out)\nasyncio.run(main())\n"
+    },
+    "output": "Successfully wrote to .kinex/tools/color_geometry.py",
+    "call_id": "call_3173ea39534e4bcea668577f8fbf32b1|fc_08ccae8539651651016ae26bf84b8081a2a6ac741a3914dead",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "measure handover block position",
+      "file": ".kinex/tools/color_geometry.py"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223680.822701747] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.10322698296577604,-0.1875178935356495,1.0550687837932198],[-0.0966152118942117,-0.1785267883024514,1.0657978427499863]],\"median\":[-0.09990612428885612,-0.1834831016874116,1.0601774081920037],\"n\":25},\"blue\":{\"bounds\":[[0.06406859215696727,0.10104295310802192,0.742188561370138],[0.1609876156378477,0.1994037568596596,0.7463403147189582]],\"median\":[0.1124782631343093,0.14487610727830103,0.7460899470953907],\"n\":1351}},\n    \"right_camera\": {\"red\":{\"bounds\":[[-0.00525335309941832,0.0782190030395281,0.8791501126820411],[-0.00408828743245594,0.13725637290786735,0.9805794102735174]],\"median\":[-0.004694299697502735,0.1079008704839761,0.9400540789204228],\"n\":33545}}\n  }\n}",
+    "call_id": "call_8920028376e5431e9985bc22610f3344|fc_08ccae8539651651016ae26bfe11cc81a2a2a26f80f37dd092",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "right handover insertion",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          -0.035,
+          0.108,
+          0.923,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 1.2,
+        "grip": 1,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223692.217737030] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.8,\"waited_s\":1.4400000000000013},\n    \"measured\": {\"right_arm\":[0.29595911502838135,2.612133264541626,2.009915828704834,0.6211190819740295,-1.2166216373443604,0.020030206069350243],\"right_gripper\":[1.002331251447851]},\n    \"tcp\": {\"pose\":[-0.019422260019086257,0.11349012460167912,0.9254862889018658,-0.000666584526104731,-0.00020513332498325504,0.999957096173557,0.009236849979105784],\"timestamp\":13.8},\n    \"file\": \"/tmp/kinex-observation-thj50dm8.png\"\n  }\n}",
+    "call_id": "call_c80cea8d862a41fe88c573cd12d3c3c9|fc_08ccae8539651651016ae26c0a36d081a2a84fb18c1deff59b",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "secure receiving grasp",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          -0.035,
+          0.108,
+          0.923,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 0.6,
+        "grip": 0,
+        "settle_s": 0.32
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223712.480365349] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":14.72,\"waited_s\":0.9199999999999999},\n    \"measured\": {\"right_arm\":[0.30162709951400757,2.6209945678710938,2.024981737136841,0.6180281639099121,-1.2083321809768677,0.0248067956417799],\"right_gripper\":[0.7636807560920715]},\n    \"tcp\": {\"pose\":[-0.02298989152961234,0.11449383778764294,0.9253460541083371,0.000013929186817855871,0.0007279375365004557,0.9999446098693109,0.01049986220064153],\"timestamp\":14.72},\n    \"file\": \"/tmp/kinex-observation-lm02c6ip.png\"\n  }\n}",
+    "call_id": "call_b32eff816fc947f9bf76d2d970210ec1|fc_08ccae8539651651016ae26c1e4b7081a288194dd047334aea",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "release left after receiving grasp",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "left_arm",
+        "duration_s": 0.32,
+        "grip": 1,
+        "settle_s": 0.16
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223726.748315164] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":15.200000000000001,\"waited_s\":0.4800000000000004},\n    \"measured\": {\"left_arm\":[-0.6601924300193787,2.072953224182129,1.3642902374267578,0.7160017490386963,-0.6606023907661438,0.03993125259876251],\"left_gripper\":[0.9805027436126362]},\n    \"tcp\": {\"pose\":[-0.04102687484682255,0.09136394694744493,0.9988741403592395,0.007356924120444551,0.00990022999458713,0.7224363383853434,0.6913274174325201],\"timestamp\":15.2},\n    \"file\": \"/tmp/kinex-observation-1ua02uy9.png\"\n  }\n}",
+    "call_id": "call_c13de87615dd46518a94714d3242b769|fc_08ccae8539651651016ae26c2c975481a2a918167911900999",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "withdraw left gripper",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "left_arm",
+        "pose": [
+          -0.042,
+          -0.07,
+          1.005,
+          0,
+          0,
+          0.70710678,
+          0.70710678
+        ],
+        "duration_s": 1,
+        "grip": 1,
+        "settle_s": 0.16
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223736.491118883] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.36,\"waited_s\":1.1599999999999984},\n    \"measured\": {\"left_arm\":[-0.9022496938705444,1.686706781387329,0.8573427796363831,0.8449431657791138,-0.870881974697113,0.02716142125427723],\"left_gripper\":[0.9965842799706893]},\n    \"tcp\": {\"pose\":[-0.04167120487581144,-0.054905644949802546,1.0040063810483326,0.0009341890325687763,0.002662658693494903,0.7093984676486517,0.7048019946320231],\"timestamp\":16.36},\n    \"file\": \"/tmp/kinex-observation-oje0r48w.png\"\n  }\n}",
+    "call_id": "call_27ac356b480645daa92e2847df2a5613|fc_08ccae8539651651016ae26c34b4e881a2a9efa11be22aa170",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "carry block over blue pad",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          0.125,
+          0.154,
+          0.94,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 1.6,
+        "grip": 0,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223756.588721697] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":18.2,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"right_arm\":[0.045130740851163864,2.672109842300415,2.162466287612915,0.6279857158660889,-1.4890477657318115,0.1188364252448082],\"right_gripper\":[0.7427296218546955]},\n    \"tcp\": {\"pose\":[0.11606154007485855,0.1512896866479327,0.940862991457205,0.00028879132662097814,0.00016147017939373526,0.9999979661028987,-0.001989551963839931],\"timestamp\":18.2},\n    \"file\": \"/tmp/kinex-observation-5j68foux.png\"\n  }\n}",
+    "call_id": "call_5175e1629f124f059f994cee22176663|fc_08ccae8539651651016ae26c48598c81a2aae7bb9300bc24ef",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "verify carried block geometry",
+      "file": ".kinex/tools/color_geometry.py"
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223772.072162218] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.13724063398836486,-0.3166554951861015,0.8944537676754163],[0.11177929348432583,0.1656045185755665,1.07426368560249]],\"median\":[0.07330969700162036,0.11933055696114936,0.9206098896650587],\"n\":819},\"blue\":{\"bounds\":[[0.06864365355552057,0.10104295310802192,0.742188561370138],[0.16147779780023439,0.1994037568596596,0.7463403147189582]],\"median\":[0.13028152312770447,0.14159046303770684,0.7460899470953907],\"n\":869}},\n    \"right_camera\": {\"red\":{\"bounds\":[[-7.601372185174145,0.12024687340851872,-0.000058680332552514254],[-0.1395972028015418,0.5333425266464663,0.9311055369995853]],\"median\":[-0.46112398521004216,0.29721951103053645,0.7400177630068945],\"n\":38037}}\n  }\n}",
+    "call_id": "call_1230db139f854e85a5fa06d3dd3efd8e|fc_08ccae8539651651016ae26c59ce0481a2af463f71386fd128",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "lower block toward pad center",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          0.137,
+          0.154,
+          0.803,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 1.6,
+        "grip": 0,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223789.794671718] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.04,\"waited_s\":1.8399999999999999},\n    \"measured\": {\"right_arm\":[0.016218852251768112,2.961329698562622,2.207608699798584,0.8309350609779358,-1.5197806358337402,0.08028469234704971],\"right_gripper\":[0.7453555789860813]},\n    \"tcp\": {\"pose\":[0.13236401782215215,0.15271409951038523,0.8089243044156644,-0.00007593607348670083,0.0015164897783684615,0.9999928843330059,-0.0034533716597715527],\"timestamp\":20.04},\n    \"file\": \"/tmp/kinex-observation-gcosyphn.png\"\n  }\n}",
+    "call_id": "call_d372ab04106e492591ee31c5aa0f43b7|fc_08ccae8539651651016ae26c6a1e2881a28efe08b102b6c59d",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "measure placement clearance",
+      "file": ".kinex/tools/color_geometry.py",
+      "args": {
+        "cameras": [
+          "head_camera"
+        ]
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223806.489186627] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"head_camera\": {\"red\":{\"bounds\":[[-0.13485902623471432,-0.31561879628961304,0.7642904861201],[0.13118034378160748,0.17219147239129443,1.073136981197431]],\"median\":[0.09823286684515407,0.12062130422911532,0.8888485465711202],\"n\":2170},\"blue\":{\"bounds\":[[0.08274937023475108,0.10104295310802192,0.742188561370138],[0.16184595025649565,0.1373007236008467,0.7463403147189582]],\"median\":[0.13111940560664184,0.11310705874246474,0.7458174222645024],\"n\":332}}\n  }\n}",
+    "call_id": "call_c551f99649234a2ba6cdc204d43d197a|fc_08ccae8539651651016ae26c7c5df881a2a6ee139f9dbbfe8c",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "seat block on pad",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "pose": [
+          0.14,
+          0.154,
+          0.787,
+          0,
+          0,
+          1,
+          0
+        ],
+        "duration_s": 0.6,
+        "grip": 0,
+        "settle_s": 0.16
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223815.606444518] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.8,\"waited_s\":0.7600000000000016},\n    \"measured\": {\"right_arm\":[0.011729979887604713,3.007179021835327,2.2419755458831787,0.8377561569213867,-1.5260177850723267,0.06870773434638977],\"right_gripper\":[0.7484062869440425]},\n    \"tcp\": {\"pose\":[0.13483444468352754,0.1530063858574185,0.7941511626747358,-0.000009396803121347259,-0.001933277477839905,0.9999885811141735,-0.004370353664848614],\"timestamp\":20.8},\n    \"file\": \"/tmp/kinex-observation-hhpkiuue.png\"\n  }\n}",
+    "call_id": "call_d3ab1d9742274c1aa900a79991a26593|fc_08ccae8539651651016ae26c85639481a2be2fa38e418060f2",
+    "is_error": false
+  },
+  {
+    "name": "exec",
+    "input": {
+      "description": "open right gripper to release block",
+      "file": ".kinex/tools/cartesian_move.py",
+      "args": {
+        "arm": "right_arm",
+        "duration_s": 0.4,
+        "grip": 1,
+        "settle_s": 0.24
+      }
+    },
+    "output": "{\n  \"output\": \"[INFO] [1793223824.665814335] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"error\": \"episode ended during wait\",\n  \"code\": \"operation_rejected\",\n  \"details\": {\n    \"operation\": \"world.wait\",\n    \"type\": \"kinex_interfaces/srv/Wait\",\n    \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":20.92,\"waited_s\":0.120000000000001}\n  }\n}",
+    "call_id": "call_7e9286d204e446ecb2ef5c100f58ea7a|fc_08ccae8539651651016ae26c8e9a1481a28ea75d1cb33c4f5b",
+    "is_error": true
+  },
+  {
+    "name": "device_stop",
+    "input": {
+      "reason": "World wait reported terminal episode; hold devices and inspect final measurements."
+    },
+    "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped",
+    "call_id": "call_5e5797bcb87d4136b16e5612d808b0f6|fc_08ccae8539651651016ae26c941bd081a2a40082c8164e7ad1",
+    "is_error": false
+  },
+  {
+    "name": "devices",
+    "input": {
+      "calls": [
+        {
+          "deviceID": "head_camera",
+          "action": "image"
+        },
+        {
+          "deviceID": "right_gripper",
+          "action": "joint_state"
+        },
+        {
+          "deviceID": "left_gripper",
+          "action": "joint_state"
+        }
+      ]
+    },
+    "output": "{\n  \"results\": [\n    {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":20.92,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-327div3_.png\"},\n    {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.7629235739057715],\"velocities\":[0.789993865923448],\"joint_names\":[\"right_gripper\"],\"timestamp\":20.92}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.9997891133481807],\"velocities\":[0.0002649718226166442],\"joint_names\":[\"left_gripper\"],\"timestamp\":20.92}}\n  ]\n}",
+    "call_id": "call_2119df98c1784b2bbd875f3221d54448|fc_08ccae8539651651016ae26c97b1d481a2b9cd6c5352432d99",
+    "is_error": false
+  },
+  {
+    "name": "device",
+    "input": {
+      "deviceID": "right_gripper",
+      "action": "target",
+      "args": {
+        "data": 1
+      }
+    },
+    "output": "{\n  \"published\": true\n}",
+    "call_id": "call_70ae02b3dea14604a1a2de07a8a2eb93|fc_08ccae8539651651016ae26c9e0a0081a2b4ea17fa1083202c",
+    "is_error": false
+  },
+  {
+    "name": "edit",
+    "input": {
+      "path": ".kinex/tools/color_geometry.py",
+      "edits": [
+        {
+          "oldText": "from scipy.spatial.transform import Rotation\n",
+          "newText": "from scipy.spatial.transform import Rotation\nfrom scipy.ndimage import label\n"
+        },
+        {
+          "oldText": "                v,u=np.where(mask&(d>0))\n                if len(u)<5: continue\n",
+          "newText": "                components,count=label(mask)\n                if not count: continue\n                sizes=np.bincount(components.ravel()); sizes[0]=0\n                mask=components==int(sizes.argmax())\n                # Near-gripper RGB-D can be inside the declared minimum range.\n                v,u=np.where(mask&(d>=.1)&(d<2))\n                if len(u)<5: continue\n"
+        }
+      ]
+    },
+    "output": "Successfully replaced 2 block(s) in .kinex/tools/color_geometry.py.",
+    "call_id": "call_7601c444d1924c5d8b54cae42375943f|fc_08ccae8539651651016ae26ca49e8c81a2935fd086d8f4b25c",
+    "is_error": false
+  },
+  {
+    "name": "write",
+    "input": {
+      "path": ".kinex/memos/handover-block.md",
+      "content": "# ALOHA AgileX handover\n\nRead refreshed profiles and harness each episode. Native dt=.04 s; all waits sequential. No reset. Available tools: `handover_policy.py` bounded StarVLA chunk + measured feedback; `cartesian_move.py` world TCP interpolation/IK + feedback; `color_geometry.py` visible surface bounds, not object centers. Load devices through host. Inspect operation schemas.\n\nPolicy returned 16-action chunks, but first 80 frames did not grasp; geometric control worked more directly. Do not blindly reuse numerical scene positions: measure RGB-D each episode. Red object is a tall upright rectangular block, not a small cube. This episode front surface was x≈-.167,y≈.168, base z≈.740; blue pad center≈[.113,.150], top z≈.746. Block width/thickness≈.06 m. Wrist depth at near grasp is unreliable (<.1m), head views may occlude block; color geometry now filters largest component and valid range.\n\nGripper TCP +x approaches; fingers close along local y. World left orientation quat xyzw=[0,0,.707107,.707107] approaches +y, closes x. Left pregrasp [-.167,.09,.86], insertion [-.166,.19,.86], close 0; measured opening≈.74 with object. Lift to [-.163,.135,.98], stage [-.03,.09,1.0]. Block bottom then≈.879. Right orientation [0,0,1,0] approaches -x, closes y. Right pregrasp [.085,.107,.923], insertion [-.035,.108,.923], close; measured≈.76. Right grasps ~.077m below left; open left then withdraw along -y to -.07. Carry right [.125,.154,.94], lower [.137,.154,.803], seat [.140,.154,.787]. Actual TCP tracking lag 5–15 mm; block bottom≈TCP z-.046, center x≈TCP x-.02. Verify measurements instead of relying on targets. Right-held block visibly moved while left open and withdrawn.\n\nLatest episode: release target 1 at t20.8; world terminated during wait at20.92 (523 frames), not a full successful wait. Held devices, read final image/block over pad and right opening .763, left≈1.0. Restored right open target1 after hold, no further waits. Full-open arrival and native verdict were NOT available; owner finalizes. A terminal wait alone is never success evidence.\n"
+    },
+    "output": "Successfully wrote to .kinex/memos/handover-block.md",
+    "call_id": "call_4312b178a5234e2b8111a2a90077805c|fc_08ccae8539651651016ae26caab28481a28e9d7cf3496c0212",
+    "is_error": false
+  }
+]
diff --git a/experiments/robotwin-native/kinex_policy-1/trace.json b/experiments/robotwin-native/kinex_policy-1/trace.json
new file mode 100644
index 0000000000000000000000000000000000000000..1b0edefdca6a9c1248c5490ef26ecb7a60b8f6d8
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/trace.json
@@ -0,0 +1 @@
+{"schema": "rlebench/kinex-trace/1", "source": "Pi native session", "usage_source": "pi-normalized", "cache_coverage": "normalized-values", "reasoning_coverage": "normalized-values", "info": {"type": "session", "version": 3, "id": "01a18471-08d1-72aa-822f-7c775b2db8c7", "timestamp": "2026-10-28T21:36:02.769Z", "cwd": "/workspace"}, "messages": [{"id": "3add3304", "role": "system", "timestamp": "2026-10-28T21:36:05.503Z", "parts": [{"type": "text", "text": ""}]}, {"id": "3e107627", "role": "user", "timestamp": "2026-10-28T21:36:05.503Z", "parts": [{"type": "text", "text": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}]}, {"id": "9473a4ca", "role": "context", "timestamp": "2026-10-28T21:36:05.504Z", "parts": [{"type": "text", "text": "<kinex_context>\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-d90921ac50b5423fa9995617751ad56e/config/device, /opt/kinex/packages/core/assets/builtin/device.\n\ncore.python:\n{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}\n\ncore.recording:\n(none)\n\ncore.skills:\n<available_skills>\nLoad a skill with the skill tool when its description matches the task.\n  - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n  - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n  - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n  - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n  - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n  - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n  - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n  - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n  - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n  - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n  - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n  - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n  - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n</available_skills>\n\nproblems:\n(none)\n</kinex_context>"}], "context": true}, {"id": "1d9a472f", "role": "assistant", "timestamp": "2026-10-28T21:36:08.068Z", "parts": [{"type": "tool", "callID": "call_b93e1b1d30bb49cb930b357edb6cfa08|fc_08ccae8539651651016ae26ac7a39081a2a97abc8fe896e84b", "name": "ls", "input": {"path": "/workspace"}, "output": ".kinex/\nharness.md\ninstruction.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11317, "outputTokens": 19, "cachedInputTokens": 0, "cacheReportedInputTokens": 11317, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11317, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 19}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "cc628236", "role": "toolResult", "timestamp": "2026-10-28T21:36:08.076Z", "parts": [{"type": "text", "text": ".kinex/\nharness.md\ninstruction.md"}]}, {"id": "2af5f111", "role": "assistant", "timestamp": "2026-10-28T21:36:12.659Z", "parts": [{"type": "tool", "callID": "call_130da6a804ab47dd804cdd0d8f9c2d35|fc_08ccae8539651651016ae26acc024881a2b27bf65eb04ae0af", "name": "read", "input": {"path": "/workspace/harness.md", "offset": null, "limit": null}, "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11357, "outputTokens": 29, "cachedInputTokens": 11136, "cacheReportedInputTokens": 11357, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11357, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 29}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "c1aea678", "role": "toolResult", "timestamp": "2026-10-28T21:36:12.669Z", "parts": [{"type": "text", "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n"}]}, {"id": "75216b29", "role": "assistant", "timestamp": "2026-10-28T21:36:14.751Z", "parts": [{"type": "tool", "callID": "call_bf1481b3f06b424eb980c7bdd2b27d72|fc_08ccae8539651651016ae26ace7a3481a2b997a3d225505793", "name": "find", "input": {"pattern": "**/*", "path": "/workspace/.kinex", "limit": 200}, "output": 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results limit reached. Use limit=400 for more, or refine pattern]"}]}, {"id": "5674107c", "role": "assistant", "timestamp": "2026-10-28T21:36:18.258Z", "parts": [{"type": "tool", "callID": "call_816b5ca02a114a64b4420799465afe07|fc_08ccae8539651651016ae26ad1758c81a2b2b68b8ff51795ee", "name": "bash", "input": {"command": "ls .kinex/tools .kinex/skills .kinex/memos .kinex/devices; find .kinex/devices -name profile.yaml", "timeout": null}, "output": ".kinex/devices:\nhead_camera\nleft_arm\nleft_camera\nleft_gripper\npolicy\nright_arm\nright_camera\nright_gripper\nworld\n\n.kinex/memos:\n\n.kinex/skills:\n\n.kinex/tools:\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/policy/profile.yaml\n.kinex/devices/world/profile.yaml\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 15932, "outputTokens": 52, "cachedInputTokens": 11392, "cacheReportedInputTokens": 15932, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 15932, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 52}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "b808495d", "role": "toolResult", "timestamp": "2026-10-28T21:36:18.278Z", "parts": [{"type": "text", "text": ".kinex/devices:\nhead_camera\nleft_arm\nleft_camera\nleft_gripper\npolicy\nright_arm\nright_camera\nright_gripper\nworld\n\n.kinex/memos:\n\n.kinex/skills:\n\n.kinex/tools:\n.kinex/devices/left_arm/profile.yaml\n.kinex/devices/right_camera/profile.yaml\n.kinex/devices/left_gripper/profile.yaml\n.kinex/devices/right_arm/profile.yaml\n.kinex/devices/left_camera/profile.yaml\n.kinex/devices/head_camera/profile.yaml\n.kinex/devices/right_gripper/profile.yaml\n.kinex/devices/policy/profile.yaml\n.kinex/devices/world/profile.yaml\n"}]}, {"id": "1c4b5557", "role": "assistant", "timestamp": "2026-10-28T21:36:22.892Z", "parts": [{"type": "tool", "callID": "call_a30019bfd91b48169cd7775acfc00bf5|fc_08ccae8539651651016ae26ad3f0d481a2828a63d83409b2b5", "name": "read", "input": {"path": ".kinex/workspace.yaml", "offset": null, "limit": null}, "output": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_45af048065f64f73a1bbbdb48b85dc99|fc_08ccae8539651651016ae26ad6cd4481a293ec4db2e646860b", "name": "read", "input": {"path": ".kinex/devices/world/profile.yaml", "offset": null, "limit": null}, "output": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_5db8f7c6a60b43868668ed652f735d39|fc_08ccae8539651651016ae26ad6cd4481a2b747723e901c44aa", "name": "read", "input": {"path": ".kinex/devices/left_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_8020a4743b7b43bf9facc60a7382a1e1|fc_08ccae8539651651016ae26ad6cd4481a28d26477c6e4a9d33", "name": "read", "input": {"path": ".kinex/devices/right_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_361c4eaee00e4ed5b9e87e340a443899|fc_08ccae8539651651016ae26ad6cd4481a29299c5cde34e1586", "name": "read", "input": {"path": ".kinex/devices/left_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_d6e04324c9c94e7e8022689c8acd9262|fc_08ccae8539651651016ae26ad6cd4481a28f9e18f500a40d91", "name": "read", "input": {"path": ".kinex/devices/right_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_073226633db3436eaa004fd1b7f47bda|fc_08ccae8539651651016ae26ad6cd4481a294e04aa528646fdf", "name": "read", "input": {"path": ".kinex/devices/policy/profile.yaml", "offset": null, "limit": null}, "output": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n  in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n  absolute ALOHA joint actions. Use the native run_policy tool with command order\n  left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n  one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n  created_at: '2026-10-28T21:34:56.980232+00:00'\n  updated_at: '2026-10-28T21:34:56.980232+00:00'\ncompute:\n  index: 0\n  kind: cuda\nartifacts: []\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: node.py\n    factory: create\n  overlays:\n  - env.sh\n  ready_timeout_s: 600\noperations:\n  infer:\n    description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n      RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n      left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n      +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n    transport: service\n    direction: input\n    endpoint: /kinex/policy/infer\n    type: kinex_interfaces/srv/Json\n    timeout_s: 120\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_5de315338fcb4ddc86f68b19eec4c4d4|fc_08ccae8539651651016ae26ad6cd4481a29faa31a2d212e054", "name": "read", "input": {"path": ".kinex/devices/head_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 16139, "outputTokens": 252, "cachedInputTokens": 15872, "cacheReportedInputTokens": 16139, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 16139, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 252}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "fd0e8b70", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "identity:\n  id: robotwin-aloha-agilex\n  name: RoboTwin ALOHA AgileX\n  kind: arm\n  model: ALOHA AgileX (dual ARX5)\n  source: user\ndevices:\n  source: user\nsafety:\n  source: user\n  max_joint_velocity: null\n  max_cartesian_velocity: null\n  keep_out_zones: []\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\n"}]}, {"id": "b7e881af", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: world\nruntime:\n  domain_id: 83\n  observation: time\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  time:\n    description: Authoritative world clock\n    transport: topic\n    direction: output\n    endpoint: /clock\n    type: rosgraph_msgs/msg/Clock\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  wait:\n    description: Advance or await duration_s of world time (finite and nonnegative).\n      Zero is an observation barrier. Waits are sequential; one requested while another\n      is pending is rejected. A terminal episode rejects further progression without\n      exposing benchmark results.\n    transport: service\n    direction: input\n    endpoint: /world/wait\n    type: kinex_interfaces/srv/Wait\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 120\n  stop:\n    description: Cancel the pending wait.\n    transport: service\n    direction: input\n    endpoint: /world/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n  Kinex supplies frame transforms independently. This device exposes no task instructions\n  or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n  base:\n    parent: null\n    translation_m: null\n    rotation_xyzw: null\n"}]}, {"id": "d84c8910", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: left_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n      fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /left_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /left_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n  as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fl_joint1: 0.0\n  fl_joint2: 0.0\n  fl_joint3: 0.0\n  fl_joint4: 0.0\n  fl_joint5: 0.0\n  fl_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - left_arm\nframes:\n  base:\n    translation_m:\n    - -0.296999871731\n    - -0.419500023127\n    - 0.782000243664\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 0.714142563298\n    - 0.700000389985\n    parent: world:base\n"}]}, {"id": "c389212e", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: right_arm\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n      fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n      stamp; do not infer motion completion from a sent command.\n    transport: topic\n    direction: output\n    endpoint: /right_arm/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  joint_target:\n    description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n      by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n      accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n      stamp must not start in the past. Points are absolute native joint positions,\n      linearly interpolated at the native control period. Replacement clears pending\n      references; invalid trajectories are ignored, so verify measured joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_arm/joint_target\n    type: trajectory_msgs/msg/JointTrajectory\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Stop arm motion and hold the measured posture.\n    transport: service\n    direction: input\n    endpoint: /right_arm/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n  sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n  as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it.\n  Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n  action), applied as native drive targets once per 0.04 s frame. The native URDF\n  limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n  format: urdf\n  path: arm.urdf\n  base_link: base_link\n  tip_link: flange\nlimits: native\nhome:\n  fr_joint1: 0.0\n  fr_joint2: 0.0\n  fr_joint3: 0.0\n  fr_joint4: 0.0\n  fr_joint5: 0.0\n  fr_joint6: 0.0\nstop:\n  behavior: hold\n  affected_devices:\n  - right_arm\nframes:\n  base:\n    translation_m:\n    - 0.306300014257\n    - -0.418499916792\n    - 0.78100001812\n    rotation_xyzw:\n    - -2.9802e-08\n    - 5.9605e-08\n    - 0.71063355368\n    - 0.703562409721\n    parent: world:base\n"}]}, {"id": "567ffac4", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: left_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured left_gripper, the native normalized gripper opening (0 closed,\n      1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n      it.\n    transport: topic\n    direction: output\n    endpoint: /left_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /left_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /left_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n  The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n  open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - left_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: left_arm:flange\n  tcp:\n    translation_m:\n    - 0.1199999989\n    - 7.305e-09\n    - -7.152e-09\n    rotation_xyzw:\n    - 0.0\n    - 2.9802e-08\n    - 3.2247e-08\n    - 0.999999998609\n    parent: left_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: left_gripper\n  open: 1.0\n  closed: 0.0\n"}]}, {"id": "c5262e90", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: right_gripper\nruntime:\n  domain_id: 83\n  observation: joint_state\n  nodes:\n  - entry: links.py\n    factory: create\n  ready_timeout_s: 30\noperations:\n  joint_state:\n    description: Measured right_gripper, the native normalized gripper opening (0\n      closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n      from it.\n    transport: topic\n    direction: output\n    endpoint: /right_gripper/joint_state\n    type: sensor_msgs/msg/JointState\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\n  target:\n    description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n      gripper entry of upstream's 14-D qpos action. The native setter runs on every\n      250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n      target persists until replaced or stopped; out-of-range targets are ignored,\n      so verify joint_state.\n    transport: topic\n    direction: input\n    endpoint: /right_gripper/target\n    type: std_msgs/msg/Float64\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  stop:\n    description: Hold the measured opening; fingers may settle briefly after the stop.\n    transport: service\n    direction: input\n    endpoint: /right_gripper/stop\n    type: std_srvs/srv/Trigger\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n  The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n  1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n  past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n  wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n  successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n  behavior: hold\n  affected_devices:\n  - right_gripper\nframes:\n  base:\n    translation_m:\n    - 0.0\n    - 0.0\n    - 0.0\n    rotation_xyzw:\n    - 0.0\n    - 0.0\n    - 0.0\n    - 1.0\n    parent: right_arm:flange\n  tcp:\n    translation_m:\n    - 0.119999992883\n    - -9.757e-09\n    - -7.153e-09\n    rotation_xyzw:\n    - 1.0e-12\n    - 2.9802e-08\n    - -6.4393e-08\n    - 0.999999973161\n    parent: right_gripper:base\nmodel:\n  format: urdf\n  path: gripper.urdf\n  base_link: base_link\ngrip:\n  joint: right_gripper\n  open: 1.0\n  closed: 0.0\n"}]}, {"id": "1cc5a774", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n  in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n  absolute ALOHA joint actions. Use the native run_policy tool with command order\n  left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n  one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n  created_at: '2026-10-28T21:34:56.980232+00:00'\n  updated_at: '2026-10-28T21:34:56.980232+00:00'\ncompute:\n  index: 0\n  kind: cuda\nartifacts: []\nruntime:\n  domain_id: 83\n  nodes:\n  - entry: node.py\n    factory: create\n  overlays:\n  - env.sh\n  ready_timeout_s: 600\noperations:\n  infer:\n    description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n      RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n      left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n      +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n    transport: service\n    direction: input\n    endpoint: /kinex/policy/infer\n    type: kinex_interfaces/srv/Json\n    timeout_s: 120\n"}]}, {"id": "de5ae04f", "role": "toolResult", "timestamp": "2026-10-28T21:36:22.899Z", "parts": [{"type": "text", "text": "name: head_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /head_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /head_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n  RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n  mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - -0.03200000152\n    - -0.449999988079\n    - 1.350000023842\n    rotation_xyzw:\n    - -0.948683301192\n    - 0.0\n    - 0.0\n    - 0.316227769682\n    parent: world:base\n"}]}, {"id": "d3b213b8", "role": "assistant", "timestamp": "2026-10-28T21:36:26.256Z", "parts": [{"type": "tool", "callID": "call_3111b0ce2713498aa7c3483ac787e961|fc_08ccae8539651651016ae26ad8d82081a299ea927a6b786ed2", "name": "read", "input": {"path": ".kinex/devices/left_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: left_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /left_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.07000001149\n    - 0.031999994336\n    - 0.065000047158\n    rotation_xyzw:\n    - -0.589367966393\n    - 0.589367919412\n    - -0.390698641271\n    - 0.39069867358\n    parent: left_arm:flange\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_9e1350636dfd4eca9c4f22161e8f732c|fc_08ccae8539651651016ae26ada1c4481a2b75c657b65c6b0a9", "name": "read", "input": {"path": ".kinex/devices/right_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: right_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /right_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.069999999887\n    - 0.031999976227\n    - 0.064999980711\n    rotation_xyzw:\n    - -0.58936798274\n    - 0.589367967612\n    - -0.390698619841\n    - 0.390698640561\n    parent: right_arm:flange\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_4c203ba69fc94f5caf87774cdc7d09b7|fc_08ccae8539651651016ae26ada1c4481a283438ce4917c1430", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt", "offset": null, "limit": null}, "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 21248, "outputTokens": 114, "cachedInputTokens": 16256, "cacheReportedInputTokens": 21248, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21248, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 114}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0e79c345", "role": "toolResult", "timestamp": "2026-10-28T21:36:26.260Z", "parts": [{"type": "text", "text": "name: left_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /left_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /left_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.07000001149\n    - 0.031999994336\n    - 0.065000047158\n    rotation_xyzw:\n    - -0.589367966393\n    - 0.589367919412\n    - -0.390698641271\n    - 0.39069867358\n    parent: left_arm:flange\n"}]}, {"id": "5cf30400", "role": "toolResult", "timestamp": "2026-10-28T21:36:26.260Z", "parts": [{"type": "text", "text": "name: right_camera\nruntime:\n  domain_id: 83\n  nodes: []\n  ready_timeout_s: 30\noperations:\n  image:\n    description: RGB image measurements with encoding, dimensions, row stride and\n      acquisition timestamp in the Image header. The frame_id identifies the calibrated\n      optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n      frame at once, also while the world is paused, then one per camera sample; reading\n      never advances world time.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/image\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  depth:\n    description: '16UC1 depth registered to image in millimetres along the optical\n      axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n      stamp and frame_id, so pair the two by stamp.'\n    transport: topic\n    direction: output\n    endpoint: /right_camera/depth\n    type: sensor_msgs/msg/Image\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: volatile\n    timeout_s: 10\n  camera_info:\n    description: Projection and distortion calibration for the image stream, including\n      K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n      image space when projecting pixels; intrinsics are not TF transforms.\n    transport: topic\n    direction: output\n    endpoint: /right_camera/camera_info\n    type: sensor_msgs/msg/CameraInfo\n    qos:\n      depth: 1\n      reliability: reliable\n      durability: transient_local\n    timeout_s: 10\nloaded: false\nmeta:\n  created_at: '2026-10-24T00:00:00Z'\n  updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n  pixel-center depth from a raster twin; calibration and the optical-frame mount on\n  the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n  width: 320\n  height: 240\n  fps: null\n  projection:\n    model: pinhole\n    fx: 358.642181396484\n    fy: 358.642181396484\n    cx: 159.5\n    cy: 119.5\n    skew: 0\n  distortion:\n    model: none\n  fov:\n    horizontal_deg: 48.085891406656\n    vertical_deg: 37.000001358777\ndepth:\n  width: 320\n  height: 240\n  fps: null\n  quantity: z\n  scale_m_per_unit: 0.001\n  invalid_value: 0\n  valid_range_m:\n    min: 0.1\n    max: 65.535\n  reliable_range_m:\n    min: 0.1\n    max: 65.535\n  registration: color\nframes:\n  base:\n    translation_m:\n    - 0.069999999887\n    - 0.031999976227\n    - 0.064999980711\n    rotation_xyzw:\n    - -0.58936798274\n    - 0.589367967612\n    - -0.390698619841\n    - 0.390698640561\n    parent: right_arm:flange\n"}]}, {"id": "95760425", "role": "toolResult", "timestamp": "2026-10-28T21:36:26.260Z", "parts": [{"type": "text", "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n  await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n    Return a stamped source-to-target transform using Kinex frame history.\n  await helpers.transform_pose(io, pose, source, target, stamp=None)\n    Express a pose given in one frame in another.\n  await helpers.relative_pose(io, child, parent, timestamp=None)\n    Express a frame-attached child pose relative to a frame-attached parent pose.\n    Implementation: frames.py\n\nKinematics\n  helpers.joint_model(device)\n    Load a joint device's commanded joints, link FK and IK from its URDF.\n    Implementation: kinematics.py\n\nMotion\n  helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n    Plan joint references and duration without device I/O.\n    Implementation: motion.py\n\nPolicy\n  await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n    Infer once and submit the valid action prefix to the commanded devices.\n    Implementation: policy.py\n\nImages\n  await helpers.camera_frame(io, camera, timeout_s=None)\n    The current image and, for an rgbd camera, its depth from the same acquisition.\n  helpers.image_array(image)\n    Native-order pixels of a sensor_msgs/Image as numpy for computation.\n  helpers.save_image(image)\n    Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n    Implementation: images.py\n\nPerception\n  await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n    Locate a detected object in a stamped camera optical frame.\n    Implementation: perception.py\n"}]}, {"id": "d27fcb64", "role": "assistant", "timestamp": "2026-10-28T21:36:29.297Z", "parts": [{"type": "tool", "callID": "call_c4ef1e437938419b8b011e47c8d77b99|fc_08ccae8539651651016ae26adc2f5081a2b50a3fa296d64391", "name": "devices", "input": {"calls": [{"deviceID": "world", "action": "load"}, {"deviceID": "left_arm", "action": "load"}, {"deviceID": "right_arm", "action": "load"}, {"deviceID": "left_gripper", "action": "load"}, {"deviceID": "right_gripper", "action": "load"}, {"deviceID": "head_camera", "action": "load"}, {"deviceID": "left_camera", "action": "load"}, {"deviceID": "right_camera", "action": "load"}, {"deviceID": "policy", "action": "load"}]}, "output": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23413, "outputTokens": 107, "cachedInputTokens": 21248, "cacheReportedInputTokens": 23413, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23413, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 107}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "332ddba8", "role": "toolResult", "timestamp": "2026-10-28T21:37:17.921Z", "parts": [{"type": "text", "text": "{\n  \"results\": [\n    {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n    {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n  ]\n}"}]}, {"id": "723cf79e", "role": "assistant", "timestamp": "2026-10-28T21:37:21.075Z", "parts": [{"type": "tool", "callID": "call_5b430d8f32364bbfb42a8c2fe430e603|fc_08ccae8539651651016ae26b0f996c81a2a3b0299e58773167", "name": "exec", "input": {"description": "inspect operation schemas", "python": "from kinex import runtime, finish\nfinish({x:runtime.describe(x) for x in ['policy.infer','left_arm.joint_target','left_gripper.target','world.wait','head_camera.image']})"}, "output": "{\n  \"output\": \"\",\n  \"result\": {\n    \"policy.infer\": {\"description\":\"Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/kinex/policy/infer\",\"type\":\"kinex_interfaces/srv/Json\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"request\":\"string\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"response\":\"string\"},\"definitions\":{}},\n    \"left_arm.joint_target\": {\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\n    \"left_gripper.target\": {\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\n    \"world.wait\": {\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}},\n    \"head_camera.image\": {\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"height\":\"uint32\",\"width\":\"uint32\",\"encoding\":\"string\",\"is_bigendian\":\"uint8\",\"step\":\"uint32\",\"data\":\"sequence<uint8>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}}\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23737, "outputTokens": 65, "cachedInputTokens": 23424, "cacheReportedInputTokens": 23737, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23737, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 65}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0368b316", "role": "toolResult", "timestamp": "2026-10-28T21:37:22.513Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"\",\n  \"result\": {\n    \"policy.infer\": {\"description\":\"Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/kinex/policy/infer\",\"type\":\"kinex_interfaces/srv/Json\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"request\":\"string\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"response\":\"string\"},\"definitions\":{}},\n    \"left_arm.joint_target\": {\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence<string>\",\"points\":\"sequence<trajectory_msgs/JointTrajectoryPoint>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence<double>\",\"velocities\":\"sequence<double>\",\"accelerations\":\"sequence<double>\",\"effort\":\"sequence<double>\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\n    \"left_gripper.target\": {\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\n    \"world.wait\": {\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}},\n    \"head_camera.image\": {\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"height\":\"uint32\",\"width\":\"uint32\",\"encoding\":\"string\",\"is_bigendian\":\"uint8\",\"step\":\"uint32\",\"data\":\"sequence<uint8>\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}}\n  }\n}"}]}, {"id": "d795cfed", "role": "assistant", "timestamp": "2026-10-28T21:37:26.674Z", "parts": [{"type": "reasoning", "text": "**Planning reusable script**"}, {"type": "tool", "callID": "call_aafa6eda56b0431db5445c532842c4aa|fc_08ccae8539651651016ae26b15215881a2bad7cde781ccdd84", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py", "offset": 1, "limit": 240}, "output": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n                     max_actions=None, inference_args=None):\n    \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n    model serves a Json \"infer\" operation taking obs (and task) and returning\n    actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n    entries in flat-vector split order; named action maps use device names.\n    space \"device\" takes absolute joint values in URDF order, or end-effector\n    values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n    \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n    tip_link, frozen at the observation stamp. max_actions caps the horizon and\n    action_hz paces actions on the world clock. Joint trajectories submit\n    asynchronously; end-effector targets are paced by world waits before\n    returning. Returns actions_submitted, duration_s and ik_failure\n    (device, action_index, error) or None.\n\n    Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n    \"\"\"\n    import asyncio\n    from kinex.communication import duration\n    from kinex.helpers.frames import IDENTITY, transform_pose\n    from kinex.lib.geometry import invert_rt, rt_to_pose\n    plans = [_commanded(entry) for entry in command]\n    if len({entry[\"name\"] for entry in plans}) != len(plans):\n        raise ValueError(\"command must name each device once\")\n    wait = f\"{world}.wait\"\n    if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n        raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n    options = inference_args or {}\n    if set(options) & {\"obs\", \"task\"}:\n        raise ValueError(\"inference_args cannot override obs or task\")\n    data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n    actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n    if not actions:\n        raise ValueError(\"infer must return nonempty actions\")\n    actions = actions[:max_actions or len(actions)]\n    split = [_share(action, plans) for action in actions]\n    count, failure, sequences = len(split), None, {}\n    for entry in plans:\n        name, spec = entry[\"name\"], entry[\"spec\"]\n        samples = [item[name] for item in split[:count]]\n        if spec.get(\"space\", \"device\") != \"device\":\n            stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n            try:\n                state = await stream.next(timeout_s=10)\n            finally:\n                stream.close()\n            joints = entry[\"model\"]\n            seed = measured_positions(state, joints.joints)\n            base = f\"{name}/base\"\n            ik_frame = f\"{name}/{joints.tip}\"\n            target_frame = spec.get(\"target_frame\", ik_frame)\n            reference = spec.get(\"frame\", base)\n            # target_frame is an attached tool frame. Freeze its offset at the\n            # observation stamp while resolving the policy's Cartesian chunk;\n            # URDF FK of the measured joints places the tip.\n            offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n            tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n            current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n            converted = []\n            for sample in samples:\n                if spec[\"space\"] == \"pose_delta\":\n                    value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n                    current = value[\"translation\"] + value[\"rotation\"]\n                else:\n                    current = sample\n                ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n                goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n                try:\n                    seed = joints.ik(goal, seed)\n                except ValueError as error:\n                    failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n                    break\n                converted.append(seed)\n            samples = converted\n            count = min(count, len(samples))\n        sequences[name] = samples\n    if count == 0:\n        return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n    seconds = count / number(action_hz, \"action_hz\")\n    prepared, scalars = [], []\n    for entry in plans:\n        name, prof = entry[\"name\"], entry[\"profile\"]\n        samples = sequences[name][:count]\n        if entry[\"model\"]:\n            joint_command(prof, samples, entry[\"model\"].joints)\n            fields = {\"joint_names\": entry[\"model\"].names,\n                      \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n                                 for index, value in enumerate(samples)]}\n            operation = f\"{name}.joint_target\"\n        else:\n            target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n            targets = [target(prof, value[0]) for value in samples]\n            fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n            scalars.append((operation, targets))\n        prepared.append((operation, fields))\n\n    await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n    # End-effector targets persist. Timing belongs to this workflow and the world,\n    # never a device-side duration or a background worker.\n    if scalars:\n        for index in range(count):\n            if index:\n                await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n                                       for operation, values in scalars))\n            await io.request(wait, duration_s=seconds / count)\n    return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n    \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n    name = entry[\"device\"]\n    prof = profile(name)\n    kind = prof.get(\"type\")\n    if kind not in END_EFFECTORS | {\"joint\"}:\n        raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n    model = None if kind in END_EFFECTORS else JointModel(prof)\n    # A gripper or suction tool takes one command value whatever it reports back.\n    width = _cartesian_width(entry, prof)\n    return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n            \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n    \"\"\"One action, split across the commanded devices in order.\"\"\"\n    if isinstance(action, dict):\n        missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n        if missing:\n            raise ValueError(f\"action names no value for {', '.join(missing)}\")\n        return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n    vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n    shares, cursor = {}, 0\n    for entry in plan:\n        shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n        cursor += entry[\"width\"]\n    return shares\n\n\ndef _cartesian_width(entry, prof):\n    space = entry.get(\"space\", \"device\")\n    if space not in {\"device\", \"pose\", \"pose_delta\"}:\n        raise ValueError(f\"unknown policy space: {space}\")\n    if space == \"device\":\n        if \"frame\" in entry or \"target_frame\" in entry:\n            raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n        return None\n    if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n        raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n    return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n    \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n    action = vector(action, 6, \"pose_delta\")\n    angle = math.hypot(*action[3:])\n    scale = math.sin(angle / 2) / angle if angle else 0.5\n    rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n    return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n    value = number(value, \"action\")\n    if not -1 <= value <= 1:\n        raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n    return value\n\n\ndef _grip(profile, value):\n    \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n    grip = profile[\"grip\"]\n    if not isinstance(grip, dict):\n        raise ValueError(\"the gripper has no declared grip joint\")\n    return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n    \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n    value = _action(value)\n    if profile[\"vacuum\"] == \"binary\":\n        return 1.0 if value > 0 else 0.0\n    if profile[\"vacuum\"] != \"proportional\":\n        raise ValueError(\"the suction device declares no vacuum control\")\n    return (value + 1) / 2\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 24765, "outputTokens": 63, "cachedInputTokens": 23680, "cacheReportedInputTokens": 24765, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 24765, "reasoningOutputTokens": 21, "reasoningReportedOutputTokens": 63}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "63212478", "role": "toolResult", "timestamp": "2026-10-28T21:37:26.679Z", "parts": [{"type": "text", "text": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n                     max_actions=None, inference_args=None):\n    \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n    model serves a Json \"infer\" operation taking obs (and task) and returning\n    actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n    entries in flat-vector split order; named action maps use device names.\n    space \"device\" takes absolute joint values in URDF order, or end-effector\n    values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n    \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n    tip_link, frozen at the observation stamp. max_actions caps the horizon and\n    action_hz paces actions on the world clock. Joint trajectories submit\n    asynchronously; end-effector targets are paced by world waits before\n    returning. Returns actions_submitted, duration_s and ik_failure\n    (device, action_index, error) or None.\n\n    Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n    \"\"\"\n    import asyncio\n    from kinex.communication import duration\n    from kinex.helpers.frames import IDENTITY, transform_pose\n    from kinex.lib.geometry import invert_rt, rt_to_pose\n    plans = [_commanded(entry) for entry in command]\n    if len({entry[\"name\"] for entry in plans}) != len(plans):\n        raise ValueError(\"command must name each device once\")\n    wait = f\"{world}.wait\"\n    if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n        raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n    options = inference_args or {}\n    if set(options) & {\"obs\", \"task\"}:\n        raise ValueError(\"inference_args cannot override obs or task\")\n    data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n    actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n    if not actions:\n        raise ValueError(\"infer must return nonempty actions\")\n    actions = actions[:max_actions or len(actions)]\n    split = [_share(action, plans) for action in actions]\n    count, failure, sequences = len(split), None, {}\n    for entry in plans:\n        name, spec = entry[\"name\"], entry[\"spec\"]\n        samples = [item[name] for item in split[:count]]\n        if spec.get(\"space\", \"device\") != \"device\":\n            stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n            try:\n                state = await stream.next(timeout_s=10)\n            finally:\n                stream.close()\n            joints = entry[\"model\"]\n            seed = measured_positions(state, joints.joints)\n            base = f\"{name}/base\"\n            ik_frame = f\"{name}/{joints.tip}\"\n            target_frame = spec.get(\"target_frame\", ik_frame)\n            reference = spec.get(\"frame\", base)\n            # target_frame is an attached tool frame. Freeze its offset at the\n            # observation stamp while resolving the policy's Cartesian chunk;\n            # URDF FK of the measured joints places the tip.\n            offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n            tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n            current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n            converted = []\n            for sample in samples:\n                if spec[\"space\"] == \"pose_delta\":\n                    value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n                    current = value[\"translation\"] + value[\"rotation\"]\n                else:\n                    current = sample\n                ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n                goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n                try:\n                    seed = joints.ik(goal, seed)\n                except ValueError as error:\n                    failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n                    break\n                converted.append(seed)\n            samples = converted\n            count = min(count, len(samples))\n        sequences[name] = samples\n    if count == 0:\n        return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n    seconds = count / number(action_hz, \"action_hz\")\n    prepared, scalars = [], []\n    for entry in plans:\n        name, prof = entry[\"name\"], entry[\"profile\"]\n        samples = sequences[name][:count]\n        if entry[\"model\"]:\n            joint_command(prof, samples, entry[\"model\"].joints)\n            fields = {\"joint_names\": entry[\"model\"].names,\n                      \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n                                 for index, value in enumerate(samples)]}\n            operation = f\"{name}.joint_target\"\n        else:\n            target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n            targets = [target(prof, value[0]) for value in samples]\n            fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n            scalars.append((operation, targets))\n        prepared.append((operation, fields))\n\n    await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n    # End-effector targets persist. Timing belongs to this workflow and the world,\n    # never a device-side duration or a background worker.\n    if scalars:\n        for index in range(count):\n            if index:\n                await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n                                       for operation, values in scalars))\n            await io.request(wait, duration_s=seconds / count)\n    return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n    \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n    name = entry[\"device\"]\n    prof = profile(name)\n    kind = prof.get(\"type\")\n    if kind not in END_EFFECTORS | {\"joint\"}:\n        raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n    model = None if kind in END_EFFECTORS else JointModel(prof)\n    # A gripper or suction tool takes one command value whatever it reports back.\n    width = _cartesian_width(entry, prof)\n    return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n            \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n    \"\"\"One action, split across the commanded devices in order.\"\"\"\n    if isinstance(action, dict):\n        missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n        if missing:\n            raise ValueError(f\"action names no value for {', '.join(missing)}\")\n        return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n    vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n    shares, cursor = {}, 0\n    for entry in plan:\n        shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n        cursor += entry[\"width\"]\n    return shares\n\n\ndef _cartesian_width(entry, prof):\n    space = entry.get(\"space\", \"device\")\n    if space not in {\"device\", \"pose\", \"pose_delta\"}:\n        raise ValueError(f\"unknown policy space: {space}\")\n    if space == \"device\":\n        if \"frame\" in entry or \"target_frame\" in entry:\n            raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n        return None\n    if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n        raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n    return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n    \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n    action = vector(action, 6, \"pose_delta\")\n    angle = math.hypot(*action[3:])\n    scale = math.sin(angle / 2) / angle if angle else 0.5\n    rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n    return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n    value = number(value, \"action\")\n    if not -1 <= value <= 1:\n        raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n    return value\n\n\ndef _grip(profile, value):\n    \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n    grip = profile[\"grip\"]\n    if not isinstance(grip, dict):\n        raise ValueError(\"the gripper has no declared grip joint\")\n    return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n    \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n    value = _action(value)\n    if profile[\"vacuum\"] == \"binary\":\n        return 1.0 if value > 0 else 0.0\n    if profile[\"vacuum\"] != \"proportional\":\n        raise ValueError(\"the suction device declares no vacuum control\")\n    return (value + 1) / 2\n"}]}, {"id": "b0912ee4", "role": "assistant", "timestamp": "2026-10-28T21:37:29.087Z", "parts": [{"type": "tool", "callID": "call_6d05b38858304b3bb75a6d40e1fb933a|fc_08ccae8539651651016ae26b18c8ac81a2990f177ed78f48db", "name": "device", "input": {"deviceID": "head_camera", "action": "image"}, "output": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-orrg2pv4.png\"\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27118, "outputTokens": 23, "cachedInputTokens": 24704, "cacheReportedInputTokens": 27118, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27118, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 23}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0d916554", "role": "toolResult", "timestamp": "2026-10-28T21:37:29.293Z", "parts": [{"type": "text", "text": "{\n  \"deviceID\": \"head_camera\",\n  \"timestamp\": 0,\n  \"frame_id\": \"head_camera/base\",\n  \"width\": 320,\n  \"height\": 240,\n  \"file\": \"/tmp/kinex-observation-orrg2pv4.png\"\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "346e94ec", "role": "assistant", "timestamp": "2026-10-28T21:37:36.926Z", "parts": [{"type": "tool", "callID": "call_97f69f82def545d69f64000042e044a8|fc_08ccae8539651651016ae26b1aea6081a2b7e8af07602854ff", "name": "write", "input": {"path": ".kinex/tools/handover_policy.py", "content": "\"\"\"One bounded policy chunk followed by fresh measured feedback. Devices must be loaded.\"\"\"\nimport asyncio\nfrom kinex import communication, helpers, args, finish\n\nTASK = 'use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open'\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def main():\n    opts = args()\n    async with communication() as io:\n        images = []\n        for camera in ['head_camera', 'left_camera', 'right_camera']:\n            with io.subscribe(camera + '.image', depth=1) as stream:\n                image = await stream.next(timeout_s=10)\n            images.append(helpers.save_image(image))\n        result = await helpers.run_policy(io, 'policy', {'images': images},\n            [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n            action_hz=25, max_actions=opts.get('max_actions', 32))\n        states = {}\n        for device in DEVICES:\n            with io.subscribe(device + '.joint_state', depth=1) as stream:\n                state = await stream.next(timeout_s=10)\n            states[device] = {'position': list(state.position), 'stamp': state.header.stamp}\n        with io.subscribe('head_camera.image', depth=1) as stream:\n            image = await stream.next(timeout_s=10)\n        path = helpers.save_image(image)\n    finish({'execution': result, 'measured': states}, file=path)\n\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/handover_policy.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27309, "outputTokens": 412, "cachedInputTokens": 27008, "cacheReportedInputTokens": 27309, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27309, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 412}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d97ec9b0", "role": "toolResult", "timestamp": "2026-10-28T21:37:36.929Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/handover_policy.py"}]}, {"id": "99102907", "role": "assistant", "timestamp": "2026-10-28T21:37:39.608Z", "parts": [{"type": "tool", "callID": "call_dbfd26603ee74ce5a556516cd3d58bf0|fc_08ccae8539651651016ae26b22d8b881a2a920f8a417752f5d", "name": "exec", "input": {"description": "grasp approach policy", "file": ".kinex/tools/handover_policy.py", "timeout_s": 180}, "output": "{\n  \"output\": \"[INFO] [1793223460.918079201] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.000841967121232301,0.5760459899902344,0.5073635578155518,0.11193818598985672,0.38478362560272217,0.011398900300264359],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"left_gripper\":{\"position\":[1.0020332282239741],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_arm\":{\"position\":[-0.003331375541165471,0.00015323919069487602,0.004788832738995552,-0.0013015313306823373,-0.0026872919406741858,-0.002288535935804248],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_gripper\":{\"position\":[0.9922204071825201],\"stamp\":{\"sec\":0,\"nanosec\":640000000}}},\n    \"file\": \"/tmp/kinex-observation-vu2mzt27.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27743, "outputTokens": 37, "cachedInputTokens": 27520, "cacheReportedInputTokens": 27743, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27743, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 37}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "b5bff240", "role": "toolResult", "timestamp": "2026-10-28T21:37:45.442Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223460.918079201] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.000841967121232301,0.5760459899902344,0.5073635578155518,0.11193818598985672,0.38478362560272217,0.011398900300264359],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"left_gripper\":{\"position\":[1.0020332282239741],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_arm\":{\"position\":[-0.003331375541165471,0.00015323919069487602,0.004788832738995552,-0.0013015313306823373,-0.0026872919406741858,-0.002288535935804248],\"stamp\":{\"sec\":0,\"nanosec\":640000000}},\"right_gripper\":{\"position\":[0.9922204071825201],\"stamp\":{\"sec\":0,\"nanosec\":640000000}}},\n    \"file\": \"/tmp/kinex-observation-vu2mzt27.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "7cb874b5", "role": "assistant", "timestamp": "2026-10-28T21:37:47.746Z", "parts": [{"type": "tool", "callID": "call_66e5d7cd0a344460acc9e062f1d21b7c|fc_08ccae8539651651016ae26b2b4f5c81a2821f711cfcbf299e", "name": "exec", "input": {"description": "approach red block", "file": ".kinex/tools/handover_policy.py", "timeout_s": 180}, "output": "{\n  \"output\": \"[INFO] [1793223469.135563916] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04086500033736229,1.74478018283844,1.4346884489059448,0.307611346244812,1.0182968378067017,0.03287385031580925],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"left_gripper\":{\"position\":[0.9985552294687792],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_arm\":{\"position\":[0.012346736155450344,0.08589094132184982,0.07706005871295929,-0.05403941124677658,-0.005238042213022709,-0.005991658661514521],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_gripper\":{\"position\":[0.9958593384786086],\"stamp\":{\"sec\":1,\"nanosec\":280000000}}},\n    \"file\": \"/tmp/kinex-observation-n_o6e3b9.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 28194, "outputTokens": 37, "cachedInputTokens": 27648, "cacheReportedInputTokens": 28194, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 28194, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 37}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "94f007e4", "role": "toolResult", "timestamp": "2026-10-28T21:37:53.297Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223469.135563916] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04086500033736229,1.74478018283844,1.4346884489059448,0.307611346244812,1.0182968378067017,0.03287385031580925],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"left_gripper\":{\"position\":[0.9985552294687792],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_arm\":{\"position\":[0.012346736155450344,0.08589094132184982,0.07706005871295929,-0.05403941124677658,-0.005238042213022709,-0.005991658661514521],\"stamp\":{\"sec\":1,\"nanosec\":280000000}},\"right_gripper\":{\"position\":[0.9958593384786086],\"stamp\":{\"sec\":1,\"nanosec\":280000000}}},\n    \"file\": \"/tmp/kinex-observation-n_o6e3b9.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "ac58b192", "role": "assistant", "timestamp": "2026-10-28T21:37:55.907Z", "parts": [{"type": "tool", "callID": "call_e58dedea80a245418706e48d32f83f5b|fc_08ccae8539651651016ae26b338de081a2a7775d59f40d5e10", "name": "exec", "input": {"description": "align left grasp", "file": ".kinex/tools/handover_policy.py", "timeout_s": 180}, "output": "{\n  \"output\": \"[INFO] [1793223477.284126808] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.058308765292167664,1.929189920425415,1.599496841430664,0.3592958450317383,1.0783706903457642,-0.016372166574001312],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"left_gripper\":{\"position\":[1.0109648487784646],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_arm\":{\"position\":[0.09248543530702591,0.4811040759086609,0.44249579310417175,-0.3714327812194824,0.0018695585895329714,-0.09739844501018524],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_gripper\":{\"position\":[0.9959367567842657],\"stamp\":{\"sec\":1,\"nanosec\":920000000}}},\n    \"file\": \"/tmp/kinex-observation-u4ddnv07.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 28640, "outputTokens": 36, "cachedInputTokens": 28032, "cacheReportedInputTokens": 28640, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 28640, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 36}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "366115a6", "role": "toolResult", "timestamp": "2026-10-28T21:38:01.032Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223477.284126808] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.058308765292167664,1.929189920425415,1.599496841430664,0.3592958450317383,1.0783706903457642,-0.016372166574001312],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"left_gripper\":{\"position\":[1.0109648487784646],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_arm\":{\"position\":[0.09248543530702591,0.4811040759086609,0.44249579310417175,-0.3714327812194824,0.0018695585895329714,-0.09739844501018524],\"stamp\":{\"sec\":1,\"nanosec\":920000000}},\"right_gripper\":{\"position\":[0.9959367567842657],\"stamp\":{\"sec\":1,\"nanosec\":920000000}}},\n    \"file\": \"/tmp/kinex-observation-u4ddnv07.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "7afdc28a", "role": "assistant", "timestamp": "2026-10-28T21:38:05.713Z", "parts": [{"type": "reasoning", "text": "**Checking camera framing**"}, {"type": "tool", "callID": "call_7bf474a854ed4138ac527b4a04154d94|fc_08ccae8539651651016ae26b3d71f881a287628609600ac816", "name": "exec", "input": {"description": "close left grasp", "file": ".kinex/tools/handover_policy.py", "timeout_s": 180}, "output": "{\n  \"output\": \"[INFO] [1793223486.950859814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04435889795422554,1.231294870376587,1.0781995058059692,0.17081087827682495,0.6730543971061707,-0.069943368434906],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"left_gripper\":{\"position\":[1.0099254927851937],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_arm\":{\"position\":[0.2739063501358032,1.267305612564087,1.2323172092437744,-0.9213956594467163,0.01157537754625082,-0.31220439076423645],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_gripper\":{\"position\":[0.9980137077244846],\"stamp\":{\"sec\":2,\"nanosec\":560000000}}},\n    \"file\": \"/tmp/kinex-observation-2otm1ktx.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 29084, "outputTokens": 54, "cachedInputTokens": 28544, "cacheReportedInputTokens": 29084, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 29084, "reasoningOutputTokens": 16, "reasoningReportedOutputTokens": 54}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3da59974", "role": "toolResult", "timestamp": "2026-10-28T21:38:10.478Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223486.950859814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.04435889795422554,1.231294870376587,1.0781995058059692,0.17081087827682495,0.6730543971061707,-0.069943368434906],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"left_gripper\":{\"position\":[1.0099254927851937],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_arm\":{\"position\":[0.2739063501358032,1.267305612564087,1.2323172092437744,-0.9213956594467163,0.01157537754625082,-0.31220439076423645],\"stamp\":{\"sec\":2,\"nanosec\":560000000}},\"right_gripper\":{\"position\":[0.9980137077244846],\"stamp\":{\"sec\":2,\"nanosec\":560000000}}},\n    \"file\": \"/tmp/kinex-observation-2otm1ktx.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "7d25a057", "role": "assistant", "timestamp": "2026-10-28T21:38:15.957Z", "parts": [{"type": "reasoning", "text": ""}, {"type": "tool", "callID": "call_03526e81f05043ccafaed3fb41137c8a|fc_08ccae8539651651016ae26b47d61c81a29f746a86d366c4aa", "name": "devices", "input": {"calls": [{"deviceID": "left_camera", "action": "image"}, {"deviceID": "right_camera", "action": "image"}]}, "output": "{\n  \"results\": [\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-z3rv78oi.png\"},\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dch2bguz.png\"}\n  ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 29546, "outputTokens": 59, "cachedInputTokens": 28928, "cacheReportedInputTokens": 29546, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 29546, "reasoningOutputTokens": 20, "reasoningReportedOutputTokens": 59}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ec2f180d", "role": "toolResult", "timestamp": "2026-10-28T21:38:16.215Z", "parts": [{"type": "text", "text": "{\n  \"results\": [\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-z3rv78oi.png\"},\n    {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":2.56,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dch2bguz.png\"}\n  ]\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "1be3155b", "role": "assistant", "timestamp": "2026-10-28T21:38:18.646Z", "parts": [{"type": "tool", "callID": "call_ab5510d8cc4d4cfa9f91130a9b4e475c|fc_08ccae8539651651016ae26b4a016881a2a586a44823029688", "name": "exec", "input": {"description": "reobserve and infer handover", "file": ".kinex/tools/handover_policy.py", "timeout_s": 180}, "output": "{\n  \"output\": \"[INFO] [1793223500.049809102] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.0294959656894207,0.6043052077293396,0.5100125670433044,0.10033784061670303,0.33127984404563904,-0.05462042614817619],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"left_gripper\":{\"position\":[1.004523279992017],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_arm\":{\"position\":[0.36748456954956055,1.8742890357971191,1.8310723304748535,-1.2713841199874878,-0.005831398069858551,-0.4040740430355072],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_gripper\":{\"position\":[1.0009333870627664],\"stamp\":{\"sec\":3,\"nanosec\":200000000}}},\n    \"file\": \"/tmp/kinex-observation-mpnweq9k.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 29928, "outputTokens": 39, "cachedInputTokens": 29568, "cacheReportedInputTokens": 29928, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 29928, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e3468321", "role": "toolResult", "timestamp": "2026-10-28T21:38:23.371Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223500.049809102] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"execution\": {\"actions_submitted\":16,\"duration_s\":0.64,\"ik_failure\":null},\n    \"measured\": {\"left_arm\":{\"position\":[0.0294959656894207,0.6043052077293396,0.5100125670433044,0.10033784061670303,0.33127984404563904,-0.05462042614817619],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"left_gripper\":{\"position\":[1.004523279992017],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_arm\":{\"position\":[0.36748456954956055,1.8742890357971191,1.8310723304748535,-1.2713841199874878,-0.005831398069858551,-0.4040740430355072],\"stamp\":{\"sec\":3,\"nanosec\":200000000}},\"right_gripper\":{\"position\":[1.0009333870627664],\"stamp\":{\"sec\":3,\"nanosec\":200000000}}},\n    \"file\": \"/tmp/kinex-observation-mpnweq9k.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "480babf2", "role": "assistant", "timestamp": "2026-10-28T21:38:30.304Z", "parts": [{"type": "reasoning", "text": "**Checking red block geometry**"}, {"type": "tool", "callID": "call_0adbc53793574001b52d8b79bcec99b3|fc_08ccae8539651651016ae26b562ef881a29716ef254f27abd6", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", "offset": null, "limit": null}, "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_4cc45f89363340c7aa0d9f58b8f06be6|fc_08ccae8539651651016ae26b56318081a294f16725444a70f3", "name": "read", "input": {"path": ".kinex/devices/left_arm/arm.urdf", "offset": null, "limit": null}, "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->\n<robot name=\"aloha_left_arm\">\n  <link name=\"base_link\"/>\n  <link name=\"link1\"/>\n  <link name=\"link2\"/>\n  <link name=\"link3\"/>\n  <link name=\"link4\"/>\n  <link name=\"link5\"/>\n  <link name=\"flange\"/>\n  <joint name=\"fl_joint1\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"link1\"/>\n    <origin xyz=\"0 0 0.058\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint2\" type=\"revolute\">\n    <parent link=\"link1\"/>\n    <child link=\"link2\"/>\n    <origin xyz=\"0.025013 0.00060169 0.042\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint3\" type=\"revolute\">\n    <parent link=\"link2\"/>\n    <child link=\"link3\"/>\n    <origin xyz=\"-0.26396 0.0044548 0\" rpy=\"3.14158530718 0 -0.015928\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint4\" type=\"revolute\">\n    <parent link=\"link3\"/>\n    <child link=\"link4\"/>\n    <origin xyz=\"0.246 -0.00025 -0.06\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint5\" type=\"revolute\">\n    <parent link=\"link4\"/>\n    <child link=\"link5\"/>\n    <origin xyz=\"0.06775 0.0015 -0.0855\" rpy=\"0 0 -0.015928\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint6\" type=\"revolute\">\n    <parent link=\"link5\"/>\n    <child link=\"flange\"/>\n    <origin xyz=\"0.03095 0 0.0855\" rpy=\"3.14158530718 0 0\"/>\n    <axis xyz=\"1 0 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n</robot>\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_64a1b5c11fcd454ab5594f92b8c9f74b|fc_08ccae8539651651016ae26b56318081a2924df69d48a139b1", "name": "read", "input": {"path": ".kinex/devices/left_gripper/gripper.urdf", "offset": null, "limit": null}, "output": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 30376, "outputTokens": 178, "cachedInputTokens": 29824, "cacheReportedInputTokens": 30376, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 30376, "reasoningOutputTokens": 51, "reasoningReportedOutputTokens": 178}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "aaec1966", "role": "toolResult", "timestamp": "2026-10-28T21:38:30.307Z", "parts": [{"type": "text", "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n    \"\"\"Load a joint device's model without device I/O.\n\n    joints lists the commanded joints in URDF document order with limits and\n    rate limits; names holds their names. fk(positions, link=None) returns the\n    link pose [x, y, z, qx, qy, qz, qw] in <device>/base. ik(pose, seed,\n    link=None, **options) returns commanded positions nearest the seed, or\n    raises ValueError when no verified solution is found. link defaults to the\n    model's tip_link. Measured links are also on TF as <device>/<link>.\n\n    Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n    \"\"\"\n    return JointModel(profile(device))\n"}]}, {"id": "49d594c7", "role": "toolResult", "timestamp": "2026-10-28T21:38:30.308Z", "parts": [{"type": "text", "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->\n<robot name=\"aloha_left_arm\">\n  <link name=\"base_link\"/>\n  <link name=\"link1\"/>\n  <link name=\"link2\"/>\n  <link name=\"link3\"/>\n  <link name=\"link4\"/>\n  <link name=\"link5\"/>\n  <link name=\"flange\"/>\n  <joint name=\"fl_joint1\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"link1\"/>\n    <origin xyz=\"0 0 0.058\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint2\" type=\"revolute\">\n    <parent link=\"link1\"/>\n    <child link=\"link2\"/>\n    <origin xyz=\"0.025013 0.00060169 0.042\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint3\" type=\"revolute\">\n    <parent link=\"link2\"/>\n    <child link=\"link3\"/>\n    <origin xyz=\"-0.26396 0.0044548 0\" rpy=\"3.14158530718 0 -0.015928\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint4\" type=\"revolute\">\n    <parent link=\"link3\"/>\n    <child link=\"link4\"/>\n    <origin xyz=\"0.246 -0.00025 -0.06\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint5\" type=\"revolute\">\n    <parent link=\"link4\"/>\n    <child link=\"link5\"/>\n    <origin xyz=\"0.06775 0.0015 -0.0855\" rpy=\"0 0 -0.015928\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint6\" type=\"revolute\">\n    <parent link=\"link5\"/>\n    <child link=\"flange\"/>\n    <origin xyz=\"0.03095 0 0.0855\" rpy=\"3.14158530718 0 0\"/>\n    <axis xyz=\"1 0 0\"/>\n    <limit lower=\"-10\" upper=\"10\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n</robot>\n"}]}, {"id": "22f2f198", "role": "toolResult", "timestamp": "2026-10-28T21:38:30.308Z", "parts": [{"type": "text", "text": "<?xml version=\"1.0\"?>\n<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->\n<robot name=\"aloha_left_gripper\">\n  <link name=\"base_link\"/>\n  <link name=\"drive\"/>\n  <link name=\"finger_a\"/>\n  <link name=\"finger_b\"/>\n  <joint name=\"left_gripper\" type=\"revolute\">\n    <parent link=\"base_link\"/>\n    <child link=\"drive\"/>\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 0 1\"/>\n    <limit lower=\"0\" upper=\"1\" effort=\"100\" velocity=\"10\"/>\n  </joint>\n  <joint name=\"fl_joint7\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_a\"/>\n    <origin xyz=\"0.08457 0.024493 -0.00010349\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n  <joint name=\"fl_joint8\" type=\"prismatic\">\n    <parent link=\"base_link\"/>\n    <child link=\"finger_b\"/>\n    <origin xyz=\"0.08457 -0.024496 -0.00010354\" rpy=\"0 0 0\"/>\n    <axis xyz=\"0 -1 0\"/>\n    <limit lower=\"0\" upper=\"0.04765\" effort=\"100\" velocity=\"10\"/>\n    <mimic joint=\"left_gripper\" multiplier=\"0.055\" offset=\"-0.01\"/>\n  </joint>\n</robot>\n"}]}, {"id": "0d6f892f", "role": "assistant", "timestamp": "2026-10-28T21:38:37.550Z", "parts": [{"type": "reasoning", "text": "**Reconstructing red-block depth**"}, {"type": "tool", "callID": "call_ad0c092304d045caae710eef68ffc64e|fc_08ccae8539651651016ae26b5d730c81a2a3f06ca5ed26ef49", "name": "exec", "input": {"description": "measure scene geometry", "python": "import asyncio, numpy as np\nfrom kinex import communication, helpers, finish\nasync def main():\n async with communication() as io:\n  f=await helpers.camera_frame(io,'head_camera')\n  finish({'frame_type':str(type(f)), 'keys':list(f) if isinstance(f,dict) else None})\nasyncio.run(main())"}, "output": "{\n  \"output\": \"[INFO] [1793223518.853304393] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"frame_type\": \"<class 'tuple'>\",\n    \"keys\": null\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 31921, "outputTokens": 171, "cachedInputTokens": 30464, "cacheReportedInputTokens": 31921, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 31921, "reasoningOutputTokens": 70, "reasoningReportedOutputTokens": 171}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9b35b133", "role": "toolResult", "timestamp": "2026-10-28T21:38:39.626Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223518.853304393] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"frame_type\": \"<class 'tuple'>\",\n    \"keys\": null\n  }\n}"}]}, {"id": "426b5575", "role": "assistant", "timestamp": "2026-10-28T21:38:41.954Z", "parts": [{"type": "tool", "callID": "call_01b5f2780dbc4b868019ad4c2dfe1f40|fc_08ccae8539651651016ae26b615b4081a29291e6a02e944ba4", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", "offset": 1, "limit": 170}, "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32167, "outputTokens": 39, "cachedInputTokens": 32000, "cacheReportedInputTokens": 32167, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32167, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e35b8613", "role": "toolResult", "timestamp": "2026-10-28T21:38:41.959Z", "parts": [{"type": "text", "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n    \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n    Both come from one acquisition (equal stamps); reading never advances world time.\n\n    Example: image, depth = await helpers.camera_frame(io, \"camera\")\n    \"\"\"\n    return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n    \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n    Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n    \"\"\"\n    return array(image)\n\n\ndef save_image(image):\n    \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n    Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n    \"\"\"\n    return save(image)\n"}]}, {"id": "b5e57b81", "role": "assistant", "timestamp": "2026-10-28T21:38:49.163Z", "parts": [{"type": "tool", "callID": "call_e3e9014848e04057871cd9d4d50054f4|fc_08ccae8539651651016ae26b63b7b881a289f3ed6fbd8a41e4", "name": "exec", "input": {"description": "locate block and pad", "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import communication, helpers,finish,profile\nasync def main():\n async with communication() as io:\n  im,dep=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); d=helpers.image_array(dep)*.001\n  p=profile('head_camera'); fr=p['frames']['base']; r=Rotation.from_quat(fr['rotation_xyzw']); out={}\n  for name,mask in [('red',(a[:,:,0]>150)&(a[:,:,1]<80)&(a[:,:,2]<80)),('blue',(a[:,:,2]>150)&(a[:,:,0]<80)&(a[:,:,1]<80))]:\n   v,u=np.where(mask & (d>0)); z=d[v,u]; xyz=np.array([(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z]).T; w=r.apply(xyz)+fr['translation_m']\n   out[name]={'bounds': [w.min(0),w.max(0)],'median':np.median(w,0),'pixels':[u.min(),v.min(),u.max(),v.max()]}\n  for arm in ['left','right']:\n   out[arm]=await helpers.relative_pose(io,{'frame':arm+'_gripper:tcp'},{'frame':'world:base'})\n finish(out)\nasyncio.run(main())"}, "output": "{\n  \"output\": \"[INFO] [1793223531.315992288] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"red\": {\"bounds\":[[-0.1951807511227014,0.1619458755140133,0.7403946746887228],[-0.13826190259151744,0.17530309445014303,0.9257439697592391]],\"median\":[-0.16744837463208706,0.16852240987786182,0.8441180625125884],\"pixels\":[77,0,115,67]},\n    \"blue\": {\"bounds\":[[0.0630836271454373,0.10104295310740785,0.7421885613696891],[0.16190775280083636,0.19940375685842826,0.746340314718351]],\"median\":[0.11238624941640478,0.14487610727741018,0.7460899470946761],\"pixels\":[199,55,244,86]},\n    \"left\": {\"pose\":[-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\"timestamp\":3.2},\n    \"right\": {\"pose\":[0.15039597241518135,-0.04177619542231542,0.9706620944772414,-0.5838925600381027,0.20359696955881887,0.7134881688183462,0.32947289004538605],\"timestamp\":3.2}\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32438, "outputTokens": 342, "cachedInputTokens": 32000, "cacheReportedInputTokens": 32438, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32438, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 342}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "337226d1", "role": "toolResult", "timestamp": "2026-10-28T21:38:51.990Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223531.315992288] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"red\": {\"bounds\":[[-0.1951807511227014,0.1619458755140133,0.7403946746887228],[-0.13826190259151744,0.17530309445014303,0.9257439697592391]],\"median\":[-0.16744837463208706,0.16852240987786182,0.8441180625125884],\"pixels\":[77,0,115,67]},\n    \"blue\": {\"bounds\":[[0.0630836271454373,0.10104295310740785,0.7421885613696891],[0.16190775280083636,0.19940375685842826,0.746340314718351]],\"median\":[0.11238624941640478,0.14487610727741018,0.7460899470946761],\"pixels\":[199,55,244,86]},\n    \"left\": {\"pose\":[-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\"timestamp\":3.2},\n    \"right\": {\"pose\":[0.15039597241518135,-0.04177619542231542,0.9706620944772414,-0.5838925600381027,0.20359696955881887,0.7134881688183462,0.32947289004538605],\"timestamp\":3.2}\n  }\n}"}]}, {"id": "faf0d4fd", "role": "assistant", "timestamp": "2026-10-28T21:39:20.244Z", "parts": [{"type": "reasoning", "text": "**Positioning for tall block**\n\n**Aligning tool for tall block**"}, {"type": "text", "text": "I’m using the camera geometry to guide the grasp and handoff, checking the measured pose after each move."}, {"type": "tool", "callID": "call_71bc1611296b4b78a0d238e63f5a7b31|fc_08ccae8539651651016ae26b7ee55c81a2afd7dc07a563eb35", "name": "write", "input": {"path": ".kinex/tools/cartesian_move.py", "content": "\"\"\"Bounded world-frame TCP motion with interpolated IK and measured endpoint reporting.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish, profile\nfrom kinex.communication import duration\n\nasync def main():\n    o=args(); arm=o.get('arm','left_arm'); side=arm.split('_')[0]; grip=side+'_gripper'\n    async with communication() as io:\n        model=helpers.joint_model(arm)\n        with io.subscribe(arm+'.joint_state',depth=1) as stream:\n            state=await stream.next(timeout_s=10)\n        q=list(state.position)\n        tcp=(await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'}))['pose']\n        base=profile(arm)['frames']['base']; rb=Rotation.from_quat(base['rotation_xyzw']); tb=np.array(base['translation_m'])\n        target=o.get('pose',tcp); seconds=o.get('duration_s',1.6)\n        n=max(1,int(seconds/.04)); rots=Slerp([0,1],Rotation.from_quat([tcp[3:],target[3:]])); points=[]\n        for i in range(1,n+1):\n            f=i/n; r=rots(f); pos=np.array(tcp[:3])*(1-f)+np.array(target[:3])*f\n            flange=pos-r.apply([.12,0,0]); local=rb.inv().apply(flange-tb); rot=rb.inv()*r\n            q=model.ik([*local,*rot.as_quat()],q)\n            points.append({'positions':q,'time_from_start':duration(i*seconds/n)})\n        if o.get('dry_run'):\n            finish({'start':tcp,'target':target,'q':q}); return\n        await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n        if 'grip' in o: await io.publish(grip+'.target',{'data':o['grip']})\n        wait=await io.request('world.wait',duration_s=seconds+o.get('settle_s',.16))\n        measured={}\n        for device in [arm,grip]:\n            with io.subscribe(device+'.joint_state',depth=1) as stream: s=await stream.next(timeout_s=10)\n            measured[device]=list(s.position)\n        actual=await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'})\n        with io.subscribe('head_camera.image',depth=1) as stream: im=await stream.next(timeout_s=10)\n        image=helpers.save_image(im)\n    finish({'wait':wait,'measured':measured,'tcp':actual},file=image)\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/cartesian_move.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 33195, "outputTokens": 954, "cachedInputTokens": 32640, "cacheReportedInputTokens": 33195, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 33195, "reasoningOutputTokens": 252, "reasoningReportedOutputTokens": 954}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "c0f01b4c", "role": "toolResult", "timestamp": "2026-10-28T21:39:20.248Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/cartesian_move.py"}]}, {"id": "27ad9df5", "role": "assistant", "timestamp": "2026-10-28T21:39:23.965Z", "parts": [{"type": "tool", "callID": "call_9952cf89eb744af2b8cbffe657bed045|fc_08ccae8539651651016ae26b8afccc81a2afcac521bdbff710", "name": "exec", "input": {"description": "plan left block approach", "file": ".kinex/tools/cartesian_move.py", "args": {"pose": [-0.167, 0.09, 0.86, 0, 0, 0.70710678, 0.70710678], "duration_s": 2, "dry_run": true}}, "output": "{\n  \"output\": \"[INFO] [1793223565.977362546] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"start\": [-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\n    \"target\": [-0.167,0.09,0.86,0,0,0.70710678,0.70710678],\n    \"q\": [-0.37572560008508377,2.272537219262441,0.9478505586577836,1.3291237834325125,-0.32947784190081975,0.012933085784625174]\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34170, "outputTokens": 74, "cachedInputTokens": 34048, "cacheReportedInputTokens": 34170, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34170, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 74}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3a18aeec", "role": "toolResult", "timestamp": "2026-10-28T21:39:26.765Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223565.977362546] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"start\": [-0.2616530267915924,-0.1484765921145812,1.070289798713373,-0.021897730331534127,-0.02189688895282258,0.6015608300353317,0.7982265238813591],\n    \"target\": [-0.167,0.09,0.86,0,0,0.70710678,0.70710678],\n    \"q\": [-0.37572560008508377,2.272537219262441,0.9478505586577836,1.3291237834325125,-0.32947784190081975,0.012933085784625174]\n  }\n}"}]}, {"id": "219b9da5", "role": "assistant", "timestamp": "2026-10-28T21:39:29.294Z", "parts": [{"type": "tool", "callID": "call_9d081ed3d01c42deb851ceb545a56207|fc_08ccae8539651651016ae26b9084f881a28c72b274a17a9916", "name": "exec", "input": {"description": "left pregrasp motion", "file": ".kinex/tools/cartesian_move.py", "args": {"pose": [-0.167, 0.09, 0.86, 0, 0, 0.70710678, 0.70710678], "duration_s": 2, "grip": 1}}, "output": "{\n  \"output\": \"[INFO] [1793223571.307316769] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":5.36,\"waited_s\":2.16},\n    \"measured\": {\"left_arm\":[-0.36332982778549194,2.2126855850219727,0.8944761157035828,1.319221019744873,-0.30946946144104004,0.010132407769560814],\"left_gripper\":[1.0015521916476162]},\n    \"tcp\": {\"pose\":[-0.1736376085877378,0.08114210634963587,0.8698353295464993,-0.0019216401989559503,0.00033106205038283427,0.7041296202769071,0.7100687822637771],\"timestamp\":5.36},\n    \"file\": \"/tmp/kinex-observation-isoz5fmq.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34460, "outputTokens": 75, "cachedInputTokens": 34048, "cacheReportedInputTokens": 34460, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34460, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 75}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "aec57b8e", "role": "toolResult", "timestamp": "2026-10-28T21:39:39.161Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223571.307316769] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":5.36,\"waited_s\":2.16},\n    \"measured\": {\"left_arm\":[-0.36332982778549194,2.2126855850219727,0.8944761157035828,1.319221019744873,-0.30946946144104004,0.010132407769560814],\"left_gripper\":[1.0015521916476162]},\n    \"tcp\": {\"pose\":[-0.1736376085877378,0.08114210634963587,0.8698353295464993,-0.0019216401989559503,0.00033106205038283427,0.7041296202769071,0.7100687822637771],\"timestamp\":5.36},\n    \"file\": \"/tmp/kinex-observation-isoz5fmq.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "1867263c", "role": "assistant", "timestamp": "2026-10-28T21:39:44.115Z", "parts": [{"type": "reasoning", "text": "**Approaching surface**"}, {"type": "tool", "callID": "call_bcb95f3488544f8492645067724488a5|fc_08ccae8539651651016ae26b9ea01c81a29c4cde48f7e394a7", "name": "exec", "input": {"description": "left grasp insertion", "file": ".kinex/tools/cartesian_move.py", "args": {"pose": [-0.166, 0.19, 0.86, 0, 0, 0.70710678, 0.70710678], "duration_s": 1.2, "grip": 1, "settle_s": 0.24}}, "output": "{\n  \"output\": \"[INFO] [1793223586.167183748] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.8,\"waited_s\":1.4399999999999995},\n    \"measured\": {\"left_arm\":[-0.3176051676273346,2.466261625289917,1.4066729545593262,1.0674532651901245,-0.2630546987056732,0.010167332366108894],\"left_gripper\":[1.0017503770914944]},\n    \"tcp\": {\"pose\":[-0.16256753019454037,0.1836445965947115,0.8630782542882497,0.0011261512119247997,-0.002236338043285821,0.7049367356530278,0.7092657677503246],\"timestamp\":6.8},\n    \"file\": \"/tmp/kinex-observation-1254cd4e.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34892, "outputTokens": 113, "cachedInputTokens": 34432, "cacheReportedInputTokens": 34892, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34892, "reasoningOutputTokens": 28, "reasoningReportedOutputTokens": 113}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "c76830d5", "role": "toolResult", "timestamp": "2026-10-28T21:39:52.093Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223586.167183748] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.8,\"waited_s\":1.4399999999999995},\n    \"measured\": {\"left_arm\":[-0.3176051676273346,2.466261625289917,1.4066729545593262,1.0674532651901245,-0.2630546987056732,0.010167332366108894],\"left_gripper\":[1.0017503770914944]},\n    \"tcp\": {\"pose\":[-0.16256753019454037,0.1836445965947115,0.8630782542882497,0.0011261512119247997,-0.002236338043285821,0.7049367356530278,0.7092657677503246],\"timestamp\":6.8},\n    \"file\": \"/tmp/kinex-observation-1254cd4e.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "5b087920", "role": "assistant", "timestamp": "2026-10-28T21:39:54.483Z", "parts": [{"type": "tool", "callID": "call_d2f4b8faec684d8b95827a648459195d|fc_08ccae8539651651016ae26ba9b98c81a2873c9b4344894296", "name": "exec", "input": {"description": "secure left grasp", "file": ".kinex/tools/cartesian_move.py", "args": {"duration_s": 0.4, "grip": 0, "settle_s": 0.24}}, "output": "{\n  \"output\": \"[INFO] [1793223596.492892278] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":7.44,\"waited_s\":0.6400000000000006},\n    \"measured\": {\"left_arm\":[-0.3172675371170044,2.46722412109375,1.408494234085083,1.067131757736206,-0.2634282112121582,0.011745059862732887],\"left_gripper\":[0.7420821664008228]},\n    \"tcp\": {\"pose\":[-0.16274491039040148,0.18399577706677667,0.8631423899018195,0.0018189100348115054,-0.0019111422555692988,0.7051953688926306,0.70900813168394],\"timestamp\":7.44},\n    \"file\": \"/tmp/kinex-observation-oh4rbjpz.png\"\n  }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 35366, "outputTokens": 52, "cachedInputTokens": 34816, "cacheReportedInputTokens": 35366, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 35366, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 52}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "bbad1c11", "role": "toolResult", "timestamp": "2026-10-28T21:39:59.308Z", "parts": [{"type": "text", "text": "{\n  \"output\": \"[INFO] [1793223596.492892278] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n  \"result\": {\n    \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":7.44,\"waited_s\":0.6400000000000006},\n    \"measured\": {\"left_arm\":[-0.3172675371170044,2.46722412109375,1.408494234085083,1.067131757736206,-0.2634282112121582,0.011745059862732887],\"left_gripper\":[0.7420821664008228]},\n    \"tcp\": {\"pose\":[-0.16274491039040148,0.18399577706677667,0.8631423899018195,0.0018189100348115054,-0.0019111422555692988,0.7051953688926306,0.70900813168394],\"timestamp\":7.44},\n    \"file\": \"/tmp/kinex-observation-oh4rbjpz.png\"\n  }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "595bd54a", "role": "assistant", "timestamp": "2026-10-28T21:40:04.453Z", "parts": [{"type": "reasoning", "text": "**Adjusting grip on block**"}, {"type": "tool", "callID": "call_923ed3d471104e4091e9fc844c6ec712|fc_08ccae8539651651016ae26bb4490081a2970d81c5b693ebc2", "name": "exec", "input": {"description": 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{\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-w35ecyf0.png\"},\n    {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":12.36,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-ug63nito.png\"}\n  ]\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "d9e6a2b6", "role": "assistant", "timestamp": "2026-10-28T21:41:16.372Z", "parts": [{"type": "reasoning", "text": "**Estimating red shape geometry**"}, {"type": "tool", "callID": "call_3173ea39534e4bcea668577f8fbf32b1|fc_08ccae8539651651016ae26bf84b8081a2a6ac741a3914dead", "name": "write", "input": {"path": ".kinex/tools/color_geometry.py", "content": "\"\"\"World-coordinate visible red/blue surface bounds from calibrated RGB-D.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import communication, helpers, args, finish, profile\nasync def main():\n    out={}\n    async with communication() as io:\n        for camera in args().get('cameras',['head_camera','right_camera']):\n            im,depth=await helpers.camera_frame(io,camera)\n            a=helpers.image_array(im); d=helpers.image_array(depth)*.001\n            t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})\n            pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']\n            objects={}\n            for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:\n                v,u=np.where(mask&(d>0))\n                if len(u)<5: continue\n                z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T\n                w=r.apply(xyz)+pose[:3]\n                objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}\n            out[camera]=objects\n    finish(out)\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/color_geometry.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 37866, "outputTokens": 428, "cachedInputTokens": 37376, "cacheReportedInputTokens": 37866, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 37866, "reasoningOutputTokens": 25, "reasoningReportedOutputTokens": 428}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "cf187b01", "role": "toolResult", "timestamp": "2026-10-28T21:41:16.374Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/color_geometry.py"}]}, {"id": "df7b82d0", "role": "assistant", "timestamp": "2026-10-28T21:41:18.616Z", "parts": [{"type": "tool", "callID": 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"call_5e5797bcb87d4136b16e5612d808b0f6|fc_08ccae8539651651016ae26c941bd081a2a40082c8164e7ad1", "name": "device_stop", "input": {"reason": "World wait reported terminal episode; hold devices and inspect final measurements."}, "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", "isError": false, "metadata": {}}], "usage": {"inputTokens": 43328, "outputTokens": 30, "cachedInputTokens": 43008, "cacheReportedInputTokens": 43328, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 43328, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 30}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7c9ca58f", "role": "toolResult", "timestamp": "2026-10-28T21:43:49.181Z", "parts": [{"type": "text", "text": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped"}]}, {"id": "26e6fcf1", "role": "assistant", "timestamp": "2026-10-28T21:43:52.322Z", "parts": [{"type": "tool", "callID": "call_2119df98c1784b2bbd875f3221d54448|fc_08ccae8539651651016ae26c97b1d481a2b9cd6c5352432d99", "name": "devices", "input": {"calls": [{"deviceID": "head_camera", "action": "image"}, {"deviceID": "right_gripper", "action": "joint_state"}, {"deviceID": "left_gripper", "action": "joint_state"}]}, "output": "{\n  \"results\": [\n    {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":20.92,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-327div3_.png\"},\n    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"name": "device", "input": {"deviceID": "right_gripper", "action": "target", "args": {"data": 1}}, "output": "{\n  \"published\": true\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 43756, "outputTokens": 127, "cachedInputTokens": 43392, "cacheReportedInputTokens": 43756, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 43756, "reasoningOutputTokens": 95, "reasoningReportedOutputTokens": 127}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7e4777ba", "role": "toolResult", "timestamp": "2026-10-28T21:43:59.565Z", "parts": [{"type": "text", "text": "{\n  \"published\": true\n}"}]}, {"id": "2169b215", "role": "assistant", "timestamp": "2026-10-28T21:44:08.423Z", "parts": [{"type": "reasoning", "text": "**Building largest-component ROI**"}, {"type": "tool", "callID": "call_7601c444d1924c5d8b54cae42375943f|fc_08ccae8539651651016ae26ca49e8c81a2935fd086d8f4b25c", "name": "edit", "input": {"path": ".kinex/tools/color_geometry.py", "edits": [{"oldText": "from scipy.spatial.transform import Rotation\n", "newText": "from scipy.spatial.transform import Rotation\nfrom scipy.ndimage import label\n"}, {"oldText": "                v,u=np.where(mask&(d>0))\n                if len(u)<5: continue\n", "newText": "                components,count=label(mask)\n                if not count: continue\n                sizes=np.bincount(components.ravel()); sizes[0]=0\n                mask=components==int(sizes.argmax())\n                # Near-gripper RGB-D can be inside the declared minimum range.\n                v,u=np.where(mask&(d>=.1)&(d<2))\n                if len(u)<5: continue\n"}]}, "output": "Successfully replaced 2 block(s) in .kinex/tools/color_geometry.py.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 43901, "outputTokens": 229, "cachedInputTokens": 43776, "cacheReportedInputTokens": 43901, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 43901, "reasoningOutputTokens": 50, "reasoningReportedOutputTokens": 229}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7bb995dc", "role": "toolResult", "timestamp": "2026-10-28T21:44:08.431Z", "parts": [{"type": "text", "text": "Successfully replaced 2 block(s) in .kinex/tools/color_geometry.py."}]}, {"id": "fb15e3f6", "role": "assistant", "timestamp": "2026-10-28T21:44:31.750Z", "parts": [{"type": "tool", "callID": "call_4312b178a5234e2b8111a2a90077805c|fc_08ccae8539651651016ae26caab28481a28e9d7cf3496c0212", "name": "write", "input": {"path": ".kinex/memos/handover-block.md", "content": "# ALOHA AgileX handover\n\nRead refreshed profiles and harness each episode. Native dt=.04 s; all waits sequential. No reset. Available tools: `handover_policy.py` bounded StarVLA chunk + measured feedback; `cartesian_move.py` world TCP interpolation/IK + feedback; `color_geometry.py` visible surface bounds, not object centers. Load devices through host. Inspect operation schemas.\n\nPolicy returned 16-action chunks, but first 80 frames did not grasp; geometric control worked more directly. Do not blindly reuse numerical scene positions: measure RGB-D each episode. Red object is a tall upright rectangular block, not a small cube. This episode front surface was x≈-.167,y≈.168, base z≈.740; blue pad center≈[.113,.150], top z≈.746. Block width/thickness≈.06 m. Wrist depth at near grasp is unreliable (<.1m), head views may occlude block; color geometry now filters largest component and valid range.\n\nGripper TCP +x approaches; fingers close along local y. World left orientation quat xyzw=[0,0,.707107,.707107] approaches +y, closes x. Left pregrasp [-.167,.09,.86], insertion [-.166,.19,.86], close 0; measured opening≈.74 with object. Lift to [-.163,.135,.98], stage [-.03,.09,1.0]. Block bottom then≈.879. Right orientation [0,0,1,0] approaches -x, closes y. Right pregrasp [.085,.107,.923], insertion [-.035,.108,.923], close; measured≈.76. Right grasps ~.077m below left; open left then withdraw along -y to -.07. Carry right [.125,.154,.94], lower [.137,.154,.803], seat [.140,.154,.787]. Actual TCP tracking lag 5–15 mm; block bottom≈TCP z-.046, center x≈TCP x-.02. Verify measurements instead of relying on targets. Right-held block visibly moved while left open and withdrawn.\n\nLatest episode: release target 1 at t20.8; world terminated during wait at20.92 (523 frames), not a full successful wait. Held devices, read final image/block over pad and right opening .763, left≈1.0. Restored right open target1 after hold, no further waits. Full-open arrival and native verdict were NOT available; owner finalizes. A terminal wait alone is never success evidence.\n"}, "output": "Successfully wrote to .kinex/memos/handover-block.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 44156, "outputTokens": 567, "cachedInputTokens": 44032, "cacheReportedInputTokens": 44156, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 44156, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 567}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "5e715242", "role": "toolResult", "timestamp": "2026-10-28T21:44:31.753Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/memos/handover-block.md"}]}, {"id": "7ec0985d", "role": "assistant", "timestamp": "2026-10-28T21:44:34.975Z", "parts": [{"type": "text", "text": "Handed the block to the right arm and placed it on the blue pad. Commanded release, but the episode ended before full gripper opening could be verified."}], "usage": {"inputTokens": 44746, "outputTokens": 37, "cachedInputTokens": 44544, "cacheReportedInputTokens": 44746, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 44746, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 37}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "stop"}]}
diff --git a/experiments/robotwin-native/kinex_policy-1/usage-audit.json b/experiments/robotwin-native/kinex_policy-1/usage-audit.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d328f91c451e2f2a6396b2cd8bb9e994950bee0
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/usage-audit.json
@@ -0,0 +1,386 @@
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diff --git a/experiments/robotwin-native/kinex_policy-1/workspace.json b/experiments/robotwin-native/kinex_policy-1/workspace.json
new file mode 100644
index 0000000000000000000000000000000000000000..2def95b4420ad1c1105fafc57d95c7a928762e19
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace.json
@@ -0,0 +1,163 @@
+{
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+    ".kinex/devices/left_arm/arm.urdf",
+    ".kinex/devices/left_arm/links.py",
+    ".kinex/devices/left_arm/profile.yaml",
+    ".kinex/devices/left_camera/profile.yaml",
+    ".kinex/devices/left_gripper/gripper.urdf",
+    ".kinex/devices/left_gripper/links.py",
+    ".kinex/devices/left_gripper/profile.yaml",
+    ".kinex/devices/policy/configuration.json",
+    ".kinex/devices/policy/env.sh",
+    ".kinex/devices/policy/node.py",
+    ".kinex/devices/policy/profile.yaml",
+    ".kinex/devices/right_arm/arm.urdf",
+    ".kinex/devices/right_arm/links.py",
+    ".kinex/devices/right_arm/profile.yaml",
+    ".kinex/devices/right_camera/profile.yaml",
+    ".kinex/devices/right_gripper/gripper.urdf",
+    ".kinex/devices/right_gripper/links.py",
+    ".kinex/devices/right_gripper/profile.yaml",
+    ".kinex/devices/world/profile.yaml",
+    ".kinex/kinex.json",
+    ".kinex/memos/handover-block.md",
+    ".kinex/tools/cartesian_move.py",
+    ".kinex/tools/color_geometry.py",
+    ".kinex/tools/handover_policy.py",
+    ".kinex/workspace.yaml",
+    "harness.md",
+    "instruction.md"
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+  "schema": "rlebench/episode-workspace/1",
+  "scope": [
+    "instruction.md",
+    "harness.md",
+    ".kinex/workspace.yaml",
+    ".kinex/devices",
+    ".kinex/kinex.json",
+    "tools",
+    "skills",
+    "memos",
+    ".kinex/tools",
+    ".kinex/skills",
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diff --git a/experiments/robotwin-native/kinex_policy-1/workspace.tar.gz b/experiments/robotwin-native/kinex_policy-1/workspace.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..482c92d84e2bd130a5e78787e0d29174dda83cef
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace.tar.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e66432f719e5216ab014602eb8a8951f79707200e1454171e756a65526e0e97
+size 12899
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/head_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ff6fc5326686558734584fe8811c12d156f3a1da
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/head_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: head_camera
+description: "Fixed RGB-D head camera (D435 model) above the table. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.364Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.03200000152
+      - -0.449999988079
+      - 1.350000023842
+    rotation_xyzw:
+      - -0.948683301192
+      - 0
+      - 0
+      - 0.316227769682
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->
+<robot name="aloha_left_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fl_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b07533f637bb8d5afa93a20758db030614e75c9a
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: left_arm
+description: "ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/left_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/left_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.372Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.296999871731
+      - -0.419500023127
+      - 0.782000243664
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 0.714142563298
+      - 0.700000389985
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fl_joint1: 0
+  fl_joint2: 0
+  fl_joint3: 0
+  fl_joint4: 0
+  fl_joint5: 0
+  fl_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - left_arm
+type: joint
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5fd898b5613875a8ab534531c0344033b2f25752
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: left_camera
+description: "RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the left_arm flange are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.363Z"
+frames:
+  base:
+    parent: "left_arm:flange"
+    translation_m:
+      - 0.07000001149
+      - 0.031999994336
+      - 0.065000047158
+    rotation_xyzw:
+      - -0.589367966393
+      - 0.589367919412
+      - -0.390698641271
+      - 0.39069867358
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_left_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="left_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fl_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..48a11781401d94760292ab236c3a1b8c0eba8b86
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/left_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: left_gripper
+description: "ALOHA AgileX left parallel two-finger gripper on the left_arm flange. The grip joint left_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/left_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/left_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.375Z"
+frames:
+  base:
+    parent: "left_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "left_gripper:base"
+    translation_m:
+      - 0.1199999989
+      - 7.305e-9
+      - -7.152e-9
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 3.2247e-8
+      - 0.999999998609
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: left_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - left_gripper
+type: gripper
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/configuration.json b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/configuration.json
new file mode 100644
index 0000000000000000000000000000000000000000..df0544ba289dd0c274ffb539eb43b14f90c2b877
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/configuration.json
@@ -0,0 +1,15 @@
+{
+  "policy": {
+    "attention": "sdpa",
+    "base_vlm": "/models/Qwen3-VL-4B-Instruct",
+    "cameras": 3,
+    "checkpoint": "/models/oft/checkpoints/steps_40000_pytorch_model.pt",
+    "embodiment": "robotwin",
+    "image_size": [
+      224,
+      224
+    ],
+    "unnorm_key": "new_embodiment"
+  },
+  "trace_directory": "/workspace/artifacts/policy"
+}
\ No newline at end of file
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/env.sh b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/env.sh
new file mode 100644
index 0000000000000000000000000000000000000000..edf16febfd76b8a4854d90c45b51f7d3ac192a43
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/env.sh
@@ -0,0 +1,4 @@
+export PYTHONPATH="/opt/policy/lib/python3.12/site-packages:/opt/starvla${PYTHONPATH:+:$PYTHONPATH}"
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export NO_ALBUMENTATIONS_UPDATE=1
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/node.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/node.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c51a97b60baa14f2c3e5a50e87e3d3bc461ea5e
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/node.py
@@ -0,0 +1,5 @@
+from pathlib import Path
+from programvla.adapters.model_node import create as factory
+
+def create(profile, context):
+    return factory(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ee57292b3421b52e56dc0024ebf340e5546992da
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/policy/profile.yaml
@@ -0,0 +1,38 @@
+name: policy
+description: "Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images in head, left wrist, right wrist order and a task instruction. Returns a chunk of absolute ALOHA joint actions. Use the native run_policy tool with command order left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes one inference per request; it does not control the robot or decide when to re-observe."
+source: default
+calibrated_at: null
+method: unknown
+runtime:
+  overlays:
+    - env.sh
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: node.py
+      factory: create
+  ready_timeout_s: 600
+operations:
+  infer:
+    description: "Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace."
+    transport: service
+    direction: input
+    endpoint: "/kinex/policy/infer"
+    type: kinex_interfaces/srv/Json
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 120
+loaded: true
+meta:
+  created_at: "2026-10-28T21:34:56.980232+00:00"
+  updated_at: "2026-10-28T21:36:29.367Z"
+family: starvla
+variant: null
+implementation: null
+artifacts: []
+compute:
+  kind: cuda
+  index: 0
+type: model
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right ARX5 arm from arx5_description_isaac.urdf: fr_base_link..fr_link6; flange is the fr_link6 frame. -->
+<robot name="aloha_right_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fr_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..efa04aaec9b18b4738c1f4c1aeb7a181ed9a4f93
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: right_arm
+description: "ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/right_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/right_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.373Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - 0.306300014257
+      - -0.418499916792
+      - 0.78100001812
+    rotation_xyzw:
+      - -2.9802e-8
+      - 5.9605e-8
+      - 0.71063355368
+      - 0.703562409721
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fr_joint1: 0
+  fr_joint2: 0
+  fr_joint3: 0
+  fr_joint4: 0
+  fr_joint5: 0
+  fr_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - right_arm
+type: joint
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5cb732f8267612b7758e9969e9962b52682ace8c
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: right_camera
+description: "RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the right_arm flange are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.366Z"
+frames:
+  base:
+    parent: "right_arm:flange"
+    translation_m:
+      - 0.069999999887
+      - 0.031999976227
+      - 0.064999980711
+    rotation_xyzw:
+      - -0.58936798274
+      - 0.589367967612
+      - -0.390698619841
+      - 0.390698640561
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right parallel gripper on the fr_link6 flange. right_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_right_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="right_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fr_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1cf53b3243b2b26ac363db434c571ef91009e787
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/right_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: right_gripper
+description: "ALOHA AgileX right parallel two-finger gripper on the right_arm flange. The grip joint right_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured right_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/right_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/right_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.370Z"
+frames:
+  base:
+    parent: "right_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "right_gripper:base"
+    translation_m:
+      - 0.119999992883
+      - -9.757e-9
+      - -7.153e-9
+    rotation_xyzw:
+      - 1e-12
+      - 2.9802e-8
+      - -6.4393e-8
+      - 0.999999973161
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: right_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - right_gripper
+type: gripper
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/world/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a912a44e8e8d111e882a42fdae1cd7f5bac27383
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/devices/world/profile.yaml
@@ -0,0 +1,58 @@
+name: world
+description: Scene reference and authoritative world clock of the RoboTwin 2.0 table. Kinex supplies frame transforms independently. This device exposes no task instructions or evaluator results.
+source: default
+calibrated_at: null
+method: unknown
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: time
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  time:
+    description: Authoritative world clock
+    transport: topic
+    direction: output
+    endpoint: "/clock"
+    type: rosgraph_msgs/msg/Clock
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  wait:
+    description: "Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results."
+    transport: service
+    direction: input
+    endpoint: "/world/wait"
+    type: kinex_interfaces/srv/Wait
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 120
+  stop:
+    description: Cancel the pending wait.
+    transport: service
+    direction: input
+    endpoint: "/world/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:36:29.361Z"
+frames:
+  base:
+    parent: null
+    translation_m: null
+    rotation_xyzw: null
+bounds: null
+surfaces: []
+type: world
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/kinex.json b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/kinex.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/kinex.json
@@ -0,0 +1,5 @@
+{
+  "python": {
+    "provider": "ros-jazzy"
+  }
+}
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/memos/handover-block.md b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/memos/handover-block.md
new file mode 100644
index 0000000000000000000000000000000000000000..4f1af73f10e1530e1bd0be8aec23019f88913d21
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/memos/handover-block.md
@@ -0,0 +1,9 @@
+# ALOHA AgileX handover
+
+Read refreshed profiles and harness each episode. Native dt=.04 s; all waits sequential. No reset. Available tools: `handover_policy.py` bounded StarVLA chunk + measured feedback; `cartesian_move.py` world TCP interpolation/IK + feedback; `color_geometry.py` visible surface bounds, not object centers. Load devices through host. Inspect operation schemas.
+
+Policy returned 16-action chunks, but first 80 frames did not grasp; geometric control worked more directly. Do not blindly reuse numerical scene positions: measure RGB-D each episode. Red object is a tall upright rectangular block, not a small cube. This episode front surface was x≈-.167,y≈.168, base z≈.740; blue pad center≈[.113,.150], top z≈.746. Block width/thickness≈.06 m. Wrist depth at near grasp is unreliable (<.1m), head views may occlude block; color geometry now filters largest component and valid range.
+
+Gripper TCP +x approaches; fingers close along local y. World left orientation quat xyzw=[0,0,.707107,.707107] approaches +y, closes x. Left pregrasp [-.167,.09,.86], insertion [-.166,.19,.86], close 0; measured opening≈.74 with object. Lift to [-.163,.135,.98], stage [-.03,.09,1.0]. Block bottom then≈.879. Right orientation [0,0,1,0] approaches -x, closes y. Right pregrasp [.085,.107,.923], insertion [-.035,.108,.923], close; measured≈.76. Right grasps ~.077m below left; open left then withdraw along -y to -.07. Carry right [.125,.154,.94], lower [.137,.154,.803], seat [.140,.154,.787]. Actual TCP tracking lag 5–15 mm; block bottom≈TCP z-.046, center x≈TCP x-.02. Verify measurements instead of relying on targets. Right-held block visibly moved while left open and withdrawn.
+
+Latest episode: release target 1 at t20.8; world terminated during wait at20.92 (523 frames), not a full successful wait. Held devices, read final image/block over pad and right opening .763, left≈1.0. Restored right open target1 after hold, no further waits. Full-open arrival and native verdict were NOT available; owner finalizes. A terminal wait alone is never success evidence.
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/cartesian_move.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/cartesian_move.py
new file mode 100644
index 0000000000000000000000000000000000000000..c36318af1b8d7c03c9ba8db73b1e7c42ad1c6f82
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/cartesian_move.py
@@ -0,0 +1,37 @@
+"""Bounded world-frame TCP motion with interpolated IK and measured endpoint reporting."""
+import asyncio
+import numpy as np
+from scipy.spatial.transform import Rotation, Slerp
+from kinex import communication, helpers, args, finish, profile
+from kinex.communication import duration
+
+async def main():
+    o=args(); arm=o.get('arm','left_arm'); side=arm.split('_')[0]; grip=side+'_gripper'
+    async with communication() as io:
+        model=helpers.joint_model(arm)
+        with io.subscribe(arm+'.joint_state',depth=1) as stream:
+            state=await stream.next(timeout_s=10)
+        q=list(state.position)
+        tcp=(await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'}))['pose']
+        base=profile(arm)['frames']['base']; rb=Rotation.from_quat(base['rotation_xyzw']); tb=np.array(base['translation_m'])
+        target=o.get('pose',tcp); seconds=o.get('duration_s',1.6)
+        n=max(1,int(seconds/.04)); rots=Slerp([0,1],Rotation.from_quat([tcp[3:],target[3:]])); points=[]
+        for i in range(1,n+1):
+            f=i/n; r=rots(f); pos=np.array(tcp[:3])*(1-f)+np.array(target[:3])*f
+            flange=pos-r.apply([.12,0,0]); local=rb.inv().apply(flange-tb); rot=rb.inv()*r
+            q=model.ik([*local,*rot.as_quat()],q)
+            points.append({'positions':q,'time_from_start':duration(i*seconds/n)})
+        if o.get('dry_run'):
+            finish({'start':tcp,'target':target,'q':q}); return
+        await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})
+        if 'grip' in o: await io.publish(grip+'.target',{'data':o['grip']})
+        wait=await io.request('world.wait',duration_s=seconds+o.get('settle_s',.16))
+        measured={}
+        for device in [arm,grip]:
+            with io.subscribe(device+'.joint_state',depth=1) as stream: s=await stream.next(timeout_s=10)
+            measured[device]=list(s.position)
+        actual=await helpers.relative_pose(io,{'frame':grip+':tcp'},{'frame':'world:base'})
+        with io.subscribe('head_camera.image',depth=1) as stream: im=await stream.next(timeout_s=10)
+        image=helpers.save_image(im)
+    finish({'wait':wait,'measured':measured,'tcp':actual},file=image)
+asyncio.run(main())
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/color_geometry.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/color_geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5553e8097bb04e9877ac41e823e37dbd49b4045
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/color_geometry.py
@@ -0,0 +1,29 @@
+"""World-coordinate visible red/blue surface bounds from calibrated RGB-D."""
+import asyncio
+import numpy as np
+from scipy.spatial.transform import Rotation
+from scipy.ndimage import label
+from kinex import communication, helpers, args, finish, profile
+async def main():
+    out={}
+    async with communication() as io:
+        for camera in args().get('cameras',['head_camera','right_camera']):
+            im,depth=await helpers.camera_frame(io,camera)
+            a=helpers.image_array(im); d=helpers.image_array(depth)*.001
+            t=await helpers.relative_pose(io,{'frame':camera+':base'},{'frame':'world:base'})
+            pose=t['pose']; r=Rotation.from_quat(pose[3:]); p=profile(camera)['color']['projection']
+            objects={}
+            for name,mask in [('red',(a[:,:,0]>130)&(a[:,:,1]<90)&(a[:,:,2]<90)),('blue',(a[:,:,2]>130)&(a[:,:,0]<90)&(a[:,:,1]<90))]:
+                components,count=label(mask)
+                if not count: continue
+                sizes=np.bincount(components.ravel()); sizes[0]=0
+                mask=components==int(sizes.argmax())
+                # Near-gripper RGB-D can be inside the declared minimum range.
+                v,u=np.where(mask&(d>=.1)&(d<2))
+                if len(u)<5: continue
+                z=d[v,u]; xyz=np.array([(u-p['cx'])*z/p['fx'],(v-p['cy'])*z/p['fy'],z]).T
+                w=r.apply(xyz)+pose[:3]
+                objects[name]={'bounds':[np.quantile(w,.01,axis=0),np.quantile(w,.99,axis=0)],'median':np.median(w,axis=0),'n':len(u)}
+            out[camera]=objects
+    finish(out)
+asyncio.run(main())
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/handover_policy.py b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/handover_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..466308cd90209ed2659c160eaaf06f417eae7b86
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/tools/handover_policy.py
@@ -0,0 +1,29 @@
+"""One bounded policy chunk followed by fresh measured feedback. Devices must be loaded."""
+import asyncio
+from kinex import communication, helpers, args, finish
+
+TASK = 'use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open'
+DEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']
+
+async def main():
+    opts = args()
+    async with communication() as io:
+        images = []
+        for camera in ['head_camera', 'left_camera', 'right_camera']:
+            with io.subscribe(camera + '.image', depth=1) as stream:
+                image = await stream.next(timeout_s=10)
+            images.append(helpers.save_image(image))
+        result = await helpers.run_policy(io, 'policy', {'images': images},
+            [{'device': d} for d in DEVICES], task=opts.get('task', TASK),
+            action_hz=25, max_actions=opts.get('max_actions', 32))
+        states = {}
+        for device in DEVICES:
+            with io.subscribe(device + '.joint_state', depth=1) as stream:
+                state = await stream.next(timeout_s=10)
+            states[device] = {'position': list(state.position), 'stamp': state.header.stamp}
+        with io.subscribe('head_camera.image', depth=1) as stream:
+            image = await stream.next(timeout_s=10)
+        path = helpers.save_image(image)
+    finish({'execution': result, 'measured': states}, file=path)
+
+asyncio.run(main())
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/workspace.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/.kinex/workspace.yaml
@@ -0,0 +1,16 @@
+identity:
+  id: robotwin-aloha-agilex
+  name: RoboTwin ALOHA AgileX
+  kind: arm
+  model: ALOHA AgileX (dual ARX5)
+  source: user
+devices:
+  source: user
+safety:
+  source: user
+  max_joint_velocity: null
+  max_cartesian_velocity: null
+  keep_out_zones: []
+meta:
+  created_at: '2026-10-24T00:00:00Z'
+  updated_at: '2026-10-24T00:00:00Z'
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/harness.md b/experiments/robotwin-native/kinex_policy-1/workspace/harness.md
new file mode 100644
index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/harness.md
@@ -0,0 +1,6 @@
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
diff --git a/experiments/robotwin-native/kinex_policy-1/workspace/instruction.md b/experiments/robotwin-native/kinex_policy-1/workspace/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..655004f8664de4cbee54ebc3865f6f44e18aa581
--- /dev/null
+++ b/experiments/robotwin-native/kinex_policy-1/workspace/instruction.md
@@ -0,0 +1,45 @@
+# Task: robotwin/handover-block-aloha-agilex
+
+use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open
+
+## Work to do
+
+Solve the task efficiently using the running rosbridge environment. Follow the
+task instruction above, including its completion requirements. Read `harness.md`
+and the device profiles for public interfaces, units and calibrated frames.
+Reuse your saved tools, skills, and memos.
+
+## Limits
+
+You have 800 native action frames. The execution mode is `stepped`: physics is
+frozen while idle. Use `world.wait` with `duration_s` to advance the native clock.
+Joint targets are ROS JointTrajectory messages; gripper targets use the units
+declared in their profiles. Reads and command publication do not advance physics.
+Waits are sequential; concurrent waits are rejected. A command or elapsed time
+does not prove arrival; inspect measured feedback. Device stop holds the device
+without ending the episode. The safety timeout is 28800 seconds.
+Do not launch or reset a simulation.
+
+## Completion
+
+Return when finished. The owner finalizes the episode and reads its native
+verdict. An interrupted or rejected wait does not establish task success. The
+native step limit stops physics even if your requested wait is longer.
+
+## Saved resources
+
+Maintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes
+in `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated
+content. Create a new file only when no existing resource fits; do not create
+per-attempt reflections or numbered copies. These files persist across fresh
+conversations. Update them before finishing your response.
+
+## Robot and device access
+
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
+
diff --git a/experiments/robotwin-native/standalone-1/detail.json b/experiments/robotwin-native/standalone-1/detail.json
index 4a2092014e921fc128928c46f5d739d70775cd22..5e676bb1192eb2b2b3182fcc7c8c47fe7339c735 100644
--- a/experiments/robotwin-native/standalone-1/detail.json
+++ b/experiments/robotwin-native/standalone-1/detail.json
@@ -1,6 +1,6 @@
 {
   "instruction": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open",
-  "observation": "Analysis pending.",
+  "observation": "The policy consumed all 800 native control frames in 50 inferences. The red block remained away from the blue pad in the terminal recording. No planning agent or manual recovery intervened.",
   "usage": {
     "n_input_tokens": 0,
     "n_output_tokens": 0,
@@ -77,8 +77,8 @@
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       "name": "media.json",
       "path": "experiments/robotwin-native/standalone-1/media.json",
-      "bytes": 1796,
-      "sha256": "b16c4861745c896230100c586d9ff705bcdf4e0457a591281e65d63ee02c42ef"
+      "bytes": 2320,
+      "sha256": "8d084c2277d6028e121cd92c762c660e0d8a14f52b5a6db802a7efe1d9ec3814"
     },
     {
       "name": "policy/069145b74d224c06b293906377fd3d48-0.png",
diff --git a/experiments/robotwin-native/standalone-2/artifacts.json b/experiments/robotwin-native/standalone-2/artifacts.json
new file mode 100644
index 0000000000000000000000000000000000000000..41f854398bfc0f5ba8d5c2dd1d5d291a324f04a5
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/artifacts.json
@@ -0,0 +1,109 @@
+{
+  "evidence": {
+    "complete": true,
+    "path": "evidence.tar.gz"
+  },
+  "recording": {
+    "complete": true,
+    "enabled": true,
+    "encoder_queue_drops": 0,
+    "lossless": false,
+    "manifest": {
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+      "error": null,
+      "fps": 10.0,
+      "run_id": "395cc93f0d43",
+      "schema": "rosbridge/recording/1",
+      "spectator_views": [
+        "third_person",
+        "left_wrist",
+        "right_wrist"
+      ],
+      "state": "complete",
+      "task": "robotwin/place-dual-shoes-aloha-agilex",
+      "topics": [],
+      "type": "mp4",
+      "views": {
+        "left_wrist": {
+          "dropped": 0,
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+          "segments": [
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+              "fps": 10.0,
+              "frames": 241,
+              "height": 720,
+              "last_stamp_ns": 24000000000,
+              "width": 960
+            }
+          ]
+        },
+        "right_wrist": {
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+          "segments": [
+            {
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+              "fps": 10.0,
+              "frames": 241,
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+            }
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+          "segments": [
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+    },
+    "video_available": true,
+    "videos": [
+      {
+        "bytes": 2597218,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 241,
+        "path": "recording/third_person.mp4"
+      },
+      {
+        "bytes": 4274932,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 241,
+        "path": "recording/left_wrist.mp4"
+      },
+      {
+        "bytes": 2082284,
+        "channel": "spectator",
+        "fps": 10.0,
+        "frames": 241,
+        "path": "recording/right_wrist.mp4"
+      }
+    ]
+  },
+  "schema": "rlebench/episode-artifacts/1",
+  "terminal_evidence": {
+    "available": false,
+    "complete": false,
+    "error": "FileNotFoundError: [Errno 2] No such file or directory: '/run/rlebench/private/episode_01/evidence/terminal.json'"
+  },
+  "workspace_complete": true
+}
diff --git a/experiments/robotwin-native/standalone-2/baseline.stderr b/experiments/robotwin-native/standalone-2/baseline.stderr
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiments/robotwin-native/standalone-2/baseline.stdout b/experiments/robotwin-native/standalone-2/baseline.stdout
new file mode 100644
index 0000000000000000000000000000000000000000..1aac8076a94871d207359009cf8052c932a83bb7
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/baseline.stdout
@@ -0,0 +1 @@
+{"title":"exec (Python)","output":"{\n  \"output\": \"[INFO] [1793223609.213726825] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 16, \\\"duration_s\\\": 0.64, \\\"ik_failure\\\": null}\\n{\\\"actions_submitted\\\": 8, \\\"duration_s\\\": 0.32, \\\"ik_failure\\\": null}\\n\",\n  \"result\": {\n    \"reason\": \"budget\",\n    \"actions_submitted\": 600\n  }\n}"}
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new file mode 100644
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--- /dev/null
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diff --git a/experiments/robotwin-native/standalone-2/detail.json b/experiments/robotwin-native/standalone-2/detail.json
new file mode 100644
index 0000000000000000000000000000000000000000..eeda39d6298918423fa5f203748f972a1d2c300f
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/detail.json
@@ -0,0 +1,1032 @@
+{
+  "instruction": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open",
+  "observation": "The policy consumed all 600 native control frames in 38 inferences. The native evaluator did not accept the final shoe placement. The last chunk was capped by the remaining native budget.",
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+  "model_call_count": 38
+}
diff --git a/experiments/robotwin-native/standalone-2/episode-report.json b/experiments/robotwin-native/standalone-2/episode-report.json
new file mode 100644
index 0000000000000000000000000000000000000000..7db776cab9327b1c5ddf43414133853fa450f651
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/episode-report.json
@@ -0,0 +1,59 @@
+{
+  "episode": 1,
+  "initialization": {
+    "expert_plan_success": false,
+    "requested_seed": 0
+  },
+  "native_reward": 0.0,
+  "planned_episodes": 1,
+  "reason": "step_limit",
+  "simulation_time_s": 24.0,
+  "step_limit": 600,
+  "steps": 600,
+  "success": false,
+  "valid": true,
+  "step": "episode_01",
+  "evidence_valid": true,
+  "wall_time_sec": 242.908068,
+  "usage": {
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+    "n_output_tokens": 0,
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+    "n_cache_write_tokens": 0
+  },
+  "usage_details": {
+    "schema": "rlebench/token-usage/1",
+    "source": "model-free-controller",
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+    "response_ids_complete": true,
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+  "native": {
+    "evidence_valid": true,
+    "success": false,
+    "termination": "step_limit",
+    "steps": 600
+  },
+  "failure_attribution": {
+    "classification": "native_unsuccessful",
+    "physical_cause": null,
+    "physical_cause_evidence": null
+  },
+  "exception": null
+}
diff --git a/experiments/robotwin-native/standalone-2/episode.json b/experiments/robotwin-native/standalone-2/episode.json
new file mode 100644
index 0000000000000000000000000000000000000000..8c914ec54e58bcc6d159e9fedce501bda092c998
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/episode.json
@@ -0,0 +1,15 @@
+{
+  "episode": 1,
+  "initialization": {
+    "expert_plan_success": false,
+    "requested_seed": 0
+  },
+  "native_reward": 0.0,
+  "planned_episodes": 1,
+  "reason": "step_limit",
+  "simulation_time_s": 24.0,
+  "step_limit": 600,
+  "steps": 600,
+  "success": false,
+  "valid": true
+}
diff --git a/experiments/robotwin-native/standalone-2/evidence.tar.gz b/experiments/robotwin-native/standalone-2/evidence.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..e5854028840eca31e36b175696b2c058deaddd64
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/evidence.tar.gz
@@ -0,0 +1,3 @@
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+oid sha256:fd2a2a6dab47d878ae5b951a82175d6a59782c01f34a8a52a3c636070afaf3d7
+size 8967193
diff --git a/experiments/robotwin-native/standalone-2/instruction.json b/experiments/robotwin-native/standalone-2/instruction.json
new file mode 100644
index 0000000000000000000000000000000000000000..117bff786713490f127c6af684bb93646cd98d4d
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/instruction.json
@@ -0,0 +1,14 @@
+{
+  "binding_paths": {},
+  "bindings": {},
+  "context": {},
+  "instruction": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open",
+  "native_instruction": "",
+  "policy": "modified",
+  "prompt": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "prompt_template": "<!-- rlebench:resolved-episode-instruction -->\n# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 600 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n",
+  "schema": "rlebench/episode-instruction/3",
+  "source": "catalog",
+  "task_id": "robotwin/place-dual-shoes-aloha-agilex",
+  "template": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open"
+}
diff --git a/experiments/robotwin-native/standalone-2/media.json b/experiments/robotwin-native/standalone-2/media.json
new file mode 100644
index 0000000000000000000000000000000000000000..a84a10ca9d80092962b73f20421c0a8489673e11
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/media.json
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+{
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+  "published_frames": 241,
+  "pts_verified": true,
+  "source_start_pts": 0.2,
+  "published_start_pts": 0.0,
+  "pts_offset": -0.2,
+  "terminal_frame_retained": true,
+  "views": [
+    "third_person",
+    "left_wrist",
+    "right_wrist"
+  ],
+  "fps": "10/1",
+  "native_recording": {
+    "schema": "rosbridge/recording/1",
+    "run_id": "395cc93f0d43",
+    "task": "robotwin/place-dual-shoes-aloha-agilex",
+    "state": "complete",
+    "views": {
+      "third_person": {
+        "received": 242,
+        "dropped": 0,
+        "segments": [
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+            "file": "third_person.mp4",
+            "frames": 241,
+            "width": 960,
+            "height": 720,
+            "fps": 10.0,
+            "first_stamp_ns": 0,
+            "last_stamp_ns": 24000000000
+          }
+        ],
+        "error": null
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+      "left_wrist": {
+        "received": 242,
+        "dropped": 0,
+        "segments": [
+          {
+            "file": "left_wrist.mp4",
+            "frames": 241,
+            "width": 960,
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+            "first_stamp_ns": 0,
+            "last_stamp_ns": 24000000000
+          }
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+        "dropped": 0,
+        "segments": [
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+            "frames": 241,
+            "width": 960,
+            "height": 720,
+            "fps": 10.0,
+            "first_stamp_ns": 0,
+            "last_stamp_ns": 24000000000
+          }
+        ],
+        "error": null
+      }
+    },
+    "type": "mp4",
+    "spectator_views": [
+      "third_person",
+      "left_wrist",
+      "right_wrist"
+    ],
+    "fps": 10.0,
+    "topics": [],
+    "error": null,
+    "bag_messages": 0
+  },
+  "all_frame_visual_checks": {
+    "third_person": {
+      "frames_compared": 241,
+      "mean_ssim": 0.9970014024896265,
+      "minimum_ssim": 0.995156,
+      "label_area_excluded": true
+    },
+    "left_wrist": {
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+      "mean_ssim": 0.9930874937759336,
+      "minimum_ssim": 0.988813,
+      "label_area_excluded": true
+    },
+    "right_wrist": {
+      "frames_compared": 241,
+      "mean_ssim": 0.9964520290456431,
+      "minimum_ssim": 0.993709,
+      "label_area_excluded": true
+    }
+  }
+}
diff --git a/experiments/robotwin-native/standalone-2/policy-calls.json b/experiments/robotwin-native/standalone-2/policy-calls.json
new file mode 100644
index 0000000000000000000000000000000000000000..040d1b0e3bebd13538eb91942c1317422468730f
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/policy-calls.json
@@ -0,0 +1,20712 @@
+[
+  {
+    "call_id": "61295cca14564f61881035d655260f98",
+    "task": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open",
+    "images": [
+      "experiments/robotwin-native/standalone-2/policy/61295cca14564f61881035d655260f98-0.png",
+      "experiments/robotwin-native/standalone-2/policy/61295cca14564f61881035d655260f98-1.png",
+      "experiments/robotwin-native/standalone-2/policy/61295cca14564f61881035d655260f98-2.png"
+    ],
+    "input": {
+      "wall_time_ns": 1793223610324288037,
+      "event": "input",
+      "call_id": "61295cca14564f61881035d655260f98",
+      "task": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open",
+      "obs": {
+        "images": [
+          "/logs/agent/policy/61295cca14564f61881035d655260f98-0.png",
+          "/logs/agent/policy/61295cca14564f61881035d655260f98-1.png",
+          "/logs/agent/policy/61295cca14564f61881035d655260f98-2.png"
+        ]
+      }
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+    ".kinex/memos"
+  ],
+  "skipped_symlinks": []
+}
diff --git a/experiments/robotwin-native/standalone-2/workspace.tar.gz b/experiments/robotwin-native/standalone-2/workspace.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..d1ea10b4875a7111b5fa01c1570e7ca883a4fe15
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace.tar.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:810f3b8943e3811649164e9cd7af191d8091fcebfbbaf4f65926e4a39fd7ff3d
+size 9850
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/head_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8a047d1d3a6a9d338e9a62baf8b0e44277e414f8
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/head_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: head_camera
+description: "Fixed RGB-D head camera (D435 model) above the table. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/head_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:49.344Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.03200000152
+      - -0.449999988079
+      - 1.350000023842
+    rotation_xyzw:
+      - -0.948683301192
+      - 0
+      - 0
+      - 0.316227769682
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left ARX5 arm from arx5_description_isaac.urdf: fl_base_link..fl_link6; flange is the fl_link6 frame. -->
+<robot name="aloha_left_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fl_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ce42c07f4a90bc54a20ed050a165e8ed9ea71c16
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: left_arm
+description: "ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF as left_arm/<link>, and helpers.joint_model('left_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/left_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/left_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:51.814Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - -0.296999871731
+      - -0.419500023127
+      - 0.782000243664
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 0.714142563298
+      - 0.700000389985
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fl_joint1: 0
+  fl_joint2: 0
+  fl_joint3: 0
+  fl_joint4: 0
+  fl_joint5: 0
+  fl_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - left_arm
+type: joint
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..462c1b505834828094b02f6c75ffd267663e2b3e
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: left_camera
+description: "RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the left_arm flange are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/left_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:53.608Z"
+frames:
+  base:
+    parent: "left_arm:flange"
+    translation_m:
+      - 0.07000001149
+      - 0.031999994336
+      - 0.065000047158
+    rotation_xyzw:
+      - -0.589367966393
+      - 0.589367919412
+      - -0.390698641271
+      - 0.39069867358
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX left parallel gripper on the fl_link6 flange. left_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_left_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="left_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fl_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fl_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="left_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ee9487ec030c834aee9ce6020565a74ebcd49e05
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/left_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: left_gripper
+description: "ALOHA AgileX left parallel two-finger gripper on the left_arm flange. The grip joint left_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/left_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/left_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/left_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:38:55.266Z"
+frames:
+  base:
+    parent: "left_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "left_gripper:base"
+    translation_m:
+      - 0.1199999989
+      - 7.305e-9
+      - -7.152e-9
+    rotation_xyzw:
+      - 0
+      - 2.9802e-8
+      - 3.2247e-8
+      - 0.999999998609
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: left_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - left_gripper
+type: gripper
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/configuration.json b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/configuration.json
new file mode 100644
index 0000000000000000000000000000000000000000..ba81208dc1aae38fc91a422279774a7d96adf60b
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/configuration.json
@@ -0,0 +1,15 @@
+{
+  "policy": {
+    "attention": "sdpa",
+    "base_vlm": "/models/Qwen3-VL-4B-Instruct",
+    "cameras": 3,
+    "checkpoint": "/models/oft/checkpoints/steps_40000_pytorch_model.pt",
+    "embodiment": "robotwin",
+    "image_size": [
+      224,
+      224
+    ],
+    "unnorm_key": "new_embodiment"
+  },
+  "trace_directory": "/logs/agent/policy"
+}
\ No newline at end of file
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/env.sh b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/env.sh
new file mode 100644
index 0000000000000000000000000000000000000000..edf16febfd76b8a4854d90c45b51f7d3ac192a43
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/env.sh
@@ -0,0 +1,4 @@
+export PYTHONPATH="/opt/policy/lib/python3.12/site-packages:/opt/starvla${PYTHONPATH:+:$PYTHONPATH}"
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export NO_ALBUMENTATIONS_UPDATE=1
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/node.py b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/node.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c51a97b60baa14f2c3e5a50e87e3d3bc461ea5e
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/node.py
@@ -0,0 +1,5 @@
+from pathlib import Path
+from programvla.adapters.model_node import create as factory
+
+def create(profile, context):
+    return factory(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ec17608b85f8a0e4c775e0e1f8f3ff8c24795eb1
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/policy/profile.yaml
@@ -0,0 +1,38 @@
+name: policy
+description: "Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images in head, left wrist, right wrist order and a task instruction. Returns a chunk of absolute ALOHA joint actions. Use the native run_policy tool with command order left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes one inference per request; it does not control the robot or decide when to re-observe."
+source: default
+calibrated_at: null
+method: unknown
+runtime:
+  overlays:
+    - env.sh
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: node.py
+      factory: create
+  ready_timeout_s: 600
+operations:
+  infer:
+    description: "Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace."
+    transport: service
+    direction: input
+    endpoint: "/kinex/policy/infer"
+    type: kinex_interfaces/srv/Json
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 120
+loaded: true
+meta:
+  created_at: "2026-10-28T21:37:32.655629+00:00"
+  updated_at: "2026-10-28T21:38:57.031Z"
+family: starvla
+variant: null
+implementation: null
+artifacts: []
+compute:
+  kind: cuda
+  index: 0
+type: model
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/arm.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/arm.urdf
@@ -0,0 +1,53 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right ARX5 arm from arx5_description_isaac.urdf: fr_base_link..fr_link6; flange is the fr_link6 frame. -->
+<robot name="aloha_right_arm">
+  <link name="base_link"/>
+  <link name="link1"/>
+  <link name="link2"/>
+  <link name="link3"/>
+  <link name="link4"/>
+  <link name="link5"/>
+  <link name="flange"/>
+  <joint name="fr_joint1" type="revolute">
+    <parent link="base_link"/>
+    <child link="link1"/>
+    <origin xyz="0 0 0.058" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint2" type="revolute">
+    <parent link="link1"/>
+    <child link="link2"/>
+    <origin xyz="0.025013 0.00060169 0.042" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint3" type="revolute">
+    <parent link="link2"/>
+    <child link="link3"/>
+    <origin xyz="-0.26396 0.0044548 0" rpy="3.14158530718 0 -0.015928"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint4" type="revolute">
+    <parent link="link3"/>
+    <child link="link4"/>
+    <origin xyz="0.246 -0.00025 -0.06" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint5" type="revolute">
+    <parent link="link4"/>
+    <child link="link5"/>
+    <origin xyz="0.06775 0.0015 -0.0855" rpy="0 0 -0.015928"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint6" type="revolute">
+    <parent link="link5"/>
+    <child link="flange"/>
+    <origin xyz="0.03095 0 0.0855" rpy="3.14158530718 0 0"/>
+    <axis xyz="1 0 0"/>
+    <limit lower="-10" upper="10" effort="100" velocity="10"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..eefd50c8097a4a270ef2860c54abffa69911cc88
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_arm/profile.yaml
@@ -0,0 +1,81 @@
+name: right_arm
+description: "ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF as right_arm/<link>, and helpers.joint_model('right_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command."
+    transport: topic
+    direction: output
+    endpoint: "/right_arm/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  joint_target:
+    description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_arm/joint_target"
+    type: trajectory_msgs/msg/JointTrajectory
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: Stop arm motion and hold the measured posture.
+    transport: service
+    direction: input
+    endpoint: "/right_arm/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:40:01.047Z"
+frames:
+  base:
+    parent: "world:base"
+    translation_m:
+      - 0.306300014257
+      - -0.418499916792
+      - 0.78100001812
+    rotation_xyzw:
+      - -2.9802e-8
+      - 5.9605e-8
+      - 0.71063355368
+      - 0.703562409721
+model:
+  format: urdf
+  path: arm.urdf
+  base_link: base_link
+  tip_link: flange
+home:
+  fr_joint1: 0
+  fr_joint2: 0
+  fr_joint3: 0
+  fr_joint4: 0
+  fr_joint5: 0
+  fr_joint6: 0
+limits: native
+stop:
+  behavior: hold
+  affected_devices:
+    - right_arm
+type: joint
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_camera/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..28c3cf3541734eecb7c869135ff3eef6b4d6b9c0
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_camera/profile.yaml
@@ -0,0 +1,92 @@
+name: right_camera
+description: "RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the right_arm flange are in the profile."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  image:
+    description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/image"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  depth:
+    description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/depth"
+    type: sensor_msgs/msg/Image
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  camera_info:
+    description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms."
+    transport: topic
+    direction: output
+    endpoint: "/right_camera/camera_info"
+    type: sensor_msgs/msg/CameraInfo
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:40:02.779Z"
+frames:
+  base:
+    parent: "right_arm:flange"
+    translation_m:
+      - 0.069999999887
+      - 0.031999976227
+      - 0.064999980711
+    rotation_xyzw:
+      - -0.58936798274
+      - 0.589367967612
+      - -0.390698619841
+      - 0.390698640561
+color:
+  width: 320
+  height: 240
+  fps: null
+  projection:
+    model: pinhole
+    fx: 358.642181396484
+    fy: 358.642181396484
+    cx: 159.5
+    cy: 119.5
+    skew: 0
+  distortion:
+    model: none
+  fov:
+    horizontal_deg: 48.085891406655776
+    vertical_deg: 37.000001358777325
+type: rgbd
+depth:
+  width: 320
+  height: 240
+  fps: null
+  quantity: z
+  scale_m_per_unit: 0.001
+  invalid_value: 0
+  valid_range_m:
+    min: 0.1
+    max: 65.535
+  reliable_range_m:
+    min: 0.1
+    max: 65.535
+  registration: color
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/gripper.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/gripper.urdf
@@ -0,0 +1,31 @@
+<?xml version="1.0"?>
+<!-- RoboTwin ALOHA AgileX right parallel gripper on the fr_link6 flange. right_gripper is a geometry-less virtual drive joint carrying the native normalized opening (0 closed, 1 open); both fingers mimic it as -0.01 + 0.055 m per unit, upstream's gripper_scale. Closed targets lie below the finger stop to squeeze an object. -->
+<robot name="aloha_right_gripper">
+  <link name="base_link"/>
+  <link name="drive"/>
+  <link name="finger_a"/>
+  <link name="finger_b"/>
+  <joint name="right_gripper" type="revolute">
+    <parent link="base_link"/>
+    <child link="drive"/>
+    <origin xyz="0 0 0" rpy="0 0 0"/>
+    <axis xyz="0 0 1"/>
+    <limit lower="0" upper="1" effort="100" velocity="10"/>
+  </joint>
+  <joint name="fr_joint7" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_a"/>
+    <origin xyz="0.08457 0.024493 -0.00010349" rpy="0 0 0"/>
+    <axis xyz="0 1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+  <joint name="fr_joint8" type="prismatic">
+    <parent link="base_link"/>
+    <child link="finger_b"/>
+    <origin xyz="0.08457 -0.024496 -0.00010354" rpy="0 0 0"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="0" upper="0.04765" effort="100" velocity="10"/>
+    <mimic joint="right_gripper" multiplier="0.055" offset="-0.01"/>
+  </joint>
+</robot>
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/links.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/links.py
@@ -0,0 +1,8 @@
+"""Publish the device's URDF links on TF from measured joint states."""
+from pathlib import Path
+
+from kinex.node import LinkPublisher
+
+
+def create(profile, context):
+    return LinkPublisher(profile, context, Path(__file__).parent)
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..32828d5f63de1ab1a996837a3e3f05aab9f2afb1
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/right_gripper/profile.yaml
@@ -0,0 +1,88 @@
+name: right_gripper
+description: "ALOHA AgileX right parallel two-finger gripper on the right_arm flange. The grip joint right_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp."
+source: introspected
+calibrated_at: null
+method: introspection
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: joint_state
+  nodes:
+    - entry: links.py
+      factory: create
+  ready_timeout_s: 30
+operations:
+  joint_state:
+    description: "Measured right_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it."
+    transport: topic
+    direction: output
+    endpoint: "/right_gripper/joint_state"
+    type: sensor_msgs/msg/JointState
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  target:
+    description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state."
+    transport: topic
+    direction: input
+    endpoint: "/right_gripper/target"
+    type: std_msgs/msg/Float64
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+  stop:
+    description: "Hold the measured opening; fingers may settle briefly after the stop."
+    transport: service
+    direction: input
+    endpoint: "/right_gripper/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:40:04.408Z"
+frames:
+  base:
+    parent: "right_arm:flange"
+    translation_m:
+      - 0
+      - 0
+      - 0
+    rotation_xyzw:
+      - 0
+      - 0
+      - 0
+      - 1
+  tcp:
+    parent: "right_gripper:base"
+    translation_m:
+      - 0.119999992883
+      - -9.757e-9
+      - -7.153e-9
+    rotation_xyzw:
+      - 1e-12
+      - 2.9802e-8
+      - -6.4393e-8
+      - 0.999999973161
+model:
+  format: urdf
+  path: gripper.urdf
+  base_link: base_link
+grip:
+  joint: right_gripper
+  open: 1
+  closed: 0
+stop:
+  behavior: hold
+  affected_devices:
+    - right_gripper
+type: gripper
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/world/profile.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..78182a22825c315b5698e7e2a5ba7a89b4eaca93
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/devices/world/profile.yaml
@@ -0,0 +1,58 @@
+name: world
+description: Scene reference and authoritative world clock of the RoboTwin 2.0 table. Kinex supplies frame transforms independently. This device exposes no task instructions or evaluator results.
+source: default
+calibrated_at: null
+method: unknown
+runtime:
+  overlays: []
+  domain_id: 83
+  namespace: ''
+  observation: time
+  nodes: []
+  ready_timeout_s: 30
+operations:
+  time:
+    description: Authoritative world clock
+    transport: topic
+    direction: output
+    endpoint: "/clock"
+    type: rosgraph_msgs/msg/Clock
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: transient_local
+    timeout_s: 10
+  wait:
+    description: "Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results."
+    transport: service
+    direction: input
+    endpoint: "/world/wait"
+    type: kinex_interfaces/srv/Wait
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 120
+  stop:
+    description: Cancel the pending wait.
+    transport: service
+    direction: input
+    endpoint: "/world/stop"
+    type: std_srvs/srv/Trigger
+    qos:
+      depth: 1
+      reliability: reliable
+      durability: volatile
+    timeout_s: 10
+loaded: true
+meta:
+  created_at: "2026-10-24T00:00:00Z"
+  updated_at: "2026-10-28T21:40:06.125Z"
+frames:
+  base:
+    parent: null
+    translation_m: null
+    rotation_xyzw: null
+bounds: null
+surfaces: []
+type: world
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/kinex.json b/experiments/robotwin-native/standalone-2/workspace/.kinex/kinex.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/kinex.json
@@ -0,0 +1,5 @@
+{
+  "python": {
+    "provider": "ros-jazzy"
+  }
+}
diff --git a/experiments/robotwin-native/standalone-2/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/standalone-2/workspace/.kinex/workspace.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/.kinex/workspace.yaml
@@ -0,0 +1,16 @@
+identity:
+  id: robotwin-aloha-agilex
+  name: RoboTwin ALOHA AgileX
+  kind: arm
+  model: ALOHA AgileX (dual ARX5)
+  source: user
+devices:
+  source: user
+safety:
+  source: user
+  max_joint_velocity: null
+  max_cartesian_velocity: null
+  keep_out_zones: []
+meta:
+  created_at: '2026-10-24T00:00:00Z'
+  updated_at: '2026-10-24T00:00:00Z'
diff --git a/experiments/robotwin-native/standalone-2/workspace/harness.md b/experiments/robotwin-native/standalone-2/workspace/harness.md
new file mode 100644
index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/harness.md
@@ -0,0 +1,6 @@
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
diff --git a/experiments/robotwin-native/standalone-2/workspace/instruction.md b/experiments/robotwin-native/standalone-2/workspace/instruction.md
new file mode 100644
index 0000000000000000000000000000000000000000..c11369ff0b46fa7f1916cac5f8d4d112bd43ccf6
--- /dev/null
+++ b/experiments/robotwin-native/standalone-2/workspace/instruction.md
@@ -0,0 +1,45 @@
+# Task: robotwin/place-dual-shoes-aloha-agilex
+
+use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open
+
+## Work to do
+
+Solve the task efficiently using the running rosbridge environment. Follow the
+task instruction above, including its completion requirements. Read `harness.md`
+and the device profiles for public interfaces, units and calibrated frames.
+Reuse your saved tools, skills, and memos.
+
+## Limits
+
+You have 600 native action frames. The execution mode is `stepped`: physics is
+frozen while idle. Use `world.wait` with `duration_s` to advance the native clock.
+Joint targets are ROS JointTrajectory messages; gripper targets use the units
+declared in their profiles. Reads and command publication do not advance physics.
+Waits are sequential; concurrent waits are rejected. A command or elapsed time
+does not prove arrival; inspect measured feedback. Device stop holds the device
+without ending the episode. The safety timeout is 28800 seconds.
+Do not launch or reset a simulation.
+
+## Completion
+
+Return when finished. The owner finalizes the episode and reads its native
+verdict. An interrupted or rejected wait does not establish task success. The
+native step limit stops physics even if your requested wait is longer.
+
+## Saved resources
+
+Maintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes
+in `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated
+content. Create a new file only when no existing resource fits; do not create
+per-attempt reflections or numbered copies. These files persist across fresh
+conversations. Update them before finishing your response.
+
+## Robot and device access
+
+Use Kinex's ROS device profiles in `.kinex/devices/` and the robot
+specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.
+Use `kinex.communication` for typed topic publication, subscriptions and service
+requests. Inspect operation schemas before acting. Load the provided devices;
+they borrow the running simulator. Stop holds motion; it does not reset the world.
+Improve existing tools, skills, and memos in place.
+
diff --git a/manifest.json b/manifest.json
index 01d2fa2cfb0100e42768cc1333919f536b900497..e89cee8584310c19c794d352a9795810b363f5ae 100644
--- a/manifest.json
+++ b/manifest.json
@@ -9,21 +9,173 @@
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diff --git a/sources.json b/sources.json
index 4f239798a02dd0e781cc2044f939071394bbb5f9..e7a65edd24a28a5df9d70247a90bc92a62a2b716 100644
--- a/sources.json
+++ b/sources.json
@@ -29,7 +29,8 @@
     "task-owner": "sha256:6328c4b92b6d6d18c936b30c317ea0e45e00a9284eff2de7a0b324ce29a49b5e",
     "agent-base": "sha256:a00a70c778ef42dce0b33ff22b1dec8da11309ddac675880b9da20b47aaff4ab",
     "policy-images": "sha256:b68c21e960150c9dabaefa8736b89638e0884d5ca008f36feadf90433e09426f",
-    "policy-contract": "sha256:50fef70af05ca772d59f6ddf3d47cfd39b38798a55dc7c2e6b7dfd872a9b238f"
+    "policy-contract": "sha256:50fef70af05ca772d59f6ddf3d47cfd39b38798a55dc7c2e6b7dfd872a9b238f",
+    "policy-task-owner": "sha256:85a3d0aaa230aef25858aa47ea4721c95732d9da8dd1dfe68baa4613626300f5"
   },
   "sources": {
     "kinex": {
@@ -39,11 +40,15 @@
     "rosbridge": {
       "revision": "fb5347cdf04404fcecea5ecdff505bb038551082",
       "content_sha256": "b8e1522c9538d42493a22ec1b794a825d65006f8a97454b20d22fd7a0e688c40",
-      "note": "Frozen standard task03 provider; Plus development is a separate suite."
+      "note": "Frozen standard task03 provider; Plus development is a separate suite.",
+      "sdk_wheel_sha256": "0b890c8c7bcf2911bd041721d7ee1790e6cfd0d3d939c05c6769d796432a5f78",
+      "native_revision": "ea8b21121ebb3cd201ff5b3fe361944ac94eda3f"
     },
     "programvla": {
       "revision": "d119fb57696dace85abe3aedc9edd1db12068516",
-      "wheel_sha256": "4e68938c169ab24ca73d8ed2d7122b3fcc8c0f0a94103052f52bb6bc28046d73"
+      "wheel_sha256": "4e68938c169ab24ca73d8ed2d7122b3fcc8c0f0a94103052f52bb6bc28046d73",
+      "owner_extension_wheel_sha256": "a03e01fa7f02fca5d85d9a600882354c715dd9df500dc0b9e49b5661a14c5408",
+      "note": "The owner extension wheel configures the public workspace. The policy agent wheel adds the checkpoint normalization compatibility fix; it supplies model inference."
     },
     "rlebench": {
       "revision": "143a3522000d840a493202d247c0105b88a8b717",
@@ -52,5 +57,21 @@
     "starvla": {
       "revision": "985c2238627481f25476fbee478083812dd82a21"
     }
-  }
+  },
+  "condition_images": {
+    "standalone": {
+      "agent": "sha256:50fef70af05ca772d59f6ddf3d47cfd39b38798a55dc7c2e6b7dfd872a9b238f",
+      "owner": "sha256:85a3d0aaa230aef25858aa47ea4721c95732d9da8dd1dfe68baa4613626300f5"
+    },
+    "kinex": {
+      "agent": "sha256:a00a70c778ef42dce0b33ff22b1dec8da11309ddac675880b9da20b47aaff4ab",
+      "owner": "sha256:6328c4b92b6d6d18c936b30c317ea0e45e00a9284eff2de7a0b324ce29a49b5e"
+    },
+    "kinex_policy": {
+      "agent": "sha256:50fef70af05ca772d59f6ddf3d47cfd39b38798a55dc7c2e6b7dfd872a9b238f",
+      "owner": "sha256:85a3d0aaa230aef25858aa47ea4721c95732d9da8dd1dfe68baa4613626300f5"
+    }
+  },
+  "checkpoint_sha256": "8ccee8ddb33fa5df83a9a5e2b7d4f737db93f19a051b23f738af8a484fe2d7e5",
+  "recording_note": "Frozen image and wheel contents identify executed code. Later working-tree changes add Plus support and are not part of these standard task03 episodes."
 }