diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..db6e4acb20f5a797c784ae4682d714f693a2c25a --- /dev/null +++ b/LICENSE @@ -0,0 +1,35 @@ +S-Lab License 1.0 + +Copyright 2026 S-Lab + +Redistribution and use for non-commercial purpose in source and +binary forms, with or without modification, are permitted provided +that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0e6c356587cb9cc6c451c175115aaeea42de4e09 --- /dev/null +++ b/README.md @@ -0,0 +1,837 @@ +--- +language: +- en +- zh +license: other +pretty_name: DynamicVLA Simulation Task Examples +configs: +- config_name: all + data_files: + - split: examples + path: data/all.parquet + default: true +- config_name: task_000000 + data_files: + - split: examples + path: data/task_000000.parquet +- config_name: task_000135 + data_files: + - split: examples + path: data/task_000135.parquet +- config_name: task_000196 + data_files: + - split: examples + path: data/task_000196.parquet +- config_name: task_000246 + data_files: + - split: examples + path: data/task_000246.parquet +- config_name: task_000080 + data_files: + - split: examples + path: data/task_000080.parquet +- config_name: task_000070 + data_files: + - split: examples + path: data/task_000070.parquet +- config_name: task_010235 + data_files: + - split: examples + path: data/task_010235.parquet +- config_name: task_010588 + data_files: + - split: examples + path: data/task_010588.parquet +- config_name: task_042175 + data_files: + - split: examples + path: data/task_042175.parquet +- config_name: task_042560 + data_files: + - split: examples + path: data/task_042560.parquet +- config_name: task_060569 + data_files: + - split: examples + path: data/task_060569.parquet +- config_name: task_060993 + data_files: + - split: examples + path: data/task_060993.parquet +- config_name: task_077322 + data_files: + - split: examples + path: data/task_077322.parquet +- config_name: task_077667 + data_files: + - split: examples + path: data/task_077667.parquet +- config_name: task_108733 + data_files: + - split: examples + path: data/task_108733.parquet +- config_name: task_111207 + data_files: + - split: examples + path: data/task_111207.parquet +- config_name: task_140594 + data_files: + - split: examples + path: data/task_140594.parquet +- config_name: task_140471 + data_files: + - split: examples + path: data/task_140471.parquet +- config_name: long_horizon + data_files: + - split: examples + path: data/long_horizon.parquet +- config_name: pick + data_files: + - split: examples + path: data/pick.parquet +- config_name: place + data_files: + - split: examples + path: data/place.parquet +tags: +- robotics +- video +- simulation +license_name: slab-license +license_link: LICENSE +--- +# DynamicVLA Simulation Task Examples + +[打开 MP4 任务浏览页](https://travor278-dynamicvla-task-examples.static.hf.space/?task=0) + +DOM has 3 task families, not 3 task_index values: 140,932 structured instruction IDs and 207,306 episodes. This subset selects 6 demonstrations per family (18 total), preserving actual task_index/episode_index and structured instructions. All 3 published cameras are single-arm cameras: opposite, wrist, side, not top/left/right arms. + +所有示例是完整 H.264 MP4。单臂相机保留官方名称：opst_cam / wrist_cam / side_cam。每条有同步合并视频、各路原视频与官方动作/状态 Parquet。 + +评估标准根据官方代码和文档整理；发布数据未保存逐条示教 success 标签，故 actual_episode_success 为 null。本文不编造 0–100 得分。 + +| 官方 task_index | Task / instruction scenario | 分类 | 直接浏览 | +|---|---|---|---| +| 0 | Long Horizon · entire set of objects | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=0) | +| 70 | Long Horizon · entire set of objects | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=70) | +| 80 | Long Horizon · entire set of objects | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=80) | +| 135 | Long Horizon · entire set of objects | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=135) | +| 196 | Long Horizon · entire set of objects | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=196) | +| 246 | Long Horizon · fusiform avocado | Long-Horizon | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=246) | +| 10235 | Pick · the moving object with the lower initial velocity | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=10235) | +| 10588 | Pick · the object in the middle from left to right at the start | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=10588) | +| 42175 | Pick · the object moving in the robot's forward-left direction | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=42175) | +| 42560 | Pick · lime | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=42560) | +| 60569 | Pick · the object that is closest to the robot's left at the start | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=60569) | +| 60993 | Pick · the object that is closest to the robot's left at the start | Pick | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=60993) | +| 77322 | Place · moving-forward object | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=77322) | +| 77667 | Place · beer bottle | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=77667) | +| 108733 | Place · lemon | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=108733) | +| 111207 | Place · lemon | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=111207) | +| 140471 | Place · black cylinder water bottle | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=140471) | +| 140594 | Place · water bottle | Place | [打开](https://travor278-dynamicvla-task-examples.static.hf.space/?task=140594) | + +## task_index=0 · Long Horizon · entire set of objects + +**Instruction:** Pick up the entire set of objects and place it in/on the white box. + +**Description:** Official DOM simulation scenario long-horizon_franka_apple00d_O03_00772319_1dc5-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "white box", + "box with Tesla Logo", + "white box with Tesla Logo", + "plastic box", + "box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=0** · 194 frames / 7.76 s + +![Camera preview](media/task_000000/episode_000000/preview.jpg) + +[同步 MP4](media/task_000000/episode_000000/synchronized.mp4) · [opst_cam MP4](media/task_000000/episode_000000/opst_cam.mp4) · [wrist_cam MP4](media/task_000000/episode_000000/wrist_cam.mp4) · [side_cam MP4](media/task_000000/episode_000000/side_cam.mp4) · [官方轨迹](trajectories/task_000000/episode_000000.parquet) + +## task_index=135 · Long Horizon · entire set of objects + +**Instruction:** Pick up the entire set of objects and place it in/on the plastic box. + +**Description:** Official DOM simulation scenario long-horizon_franka_apple08d_O03_00819030_04bc-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "plastic box", + "pink box", + "pink plastic box", + "box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=200** · 227 frames / 9.08 s + +![Camera preview](media/task_000135/episode_000200/preview.jpg) + +[同步 MP4](media/task_000135/episode_000200/synchronized.mp4) · [opst_cam MP4](media/task_000135/episode_000200/opst_cam.mp4) · [wrist_cam MP4](media/task_000135/episode_000200/wrist_cam.mp4) · [side_cam MP4](media/task_000135/episode_000200/side_cam.mp4) · [官方轨迹](trajectories/task_000135/episode_000200.parquet) + +## task_index=196 · Long Horizon · entire set of objects + +**Instruction:** Pick up the entire set of objects and place it in/on the box. + +**Description:** Official DOM simulation scenario long-horizon_franka_apple14d_O03_00888109_ee3b-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "box", + "pink box", + "pink plastic box", + "plastic box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=400** · 205 frames / 8.20 s + +![Camera preview](media/task_000196/episode_000400/preview.jpg) + +[同步 MP4](media/task_000196/episode_000400/synchronized.mp4) · [opst_cam MP4](media/task_000196/episode_000400/opst_cam.mp4) · [wrist_cam MP4](media/task_000196/episode_000400/wrist_cam.mp4) · [side_cam MP4](media/task_000196/episode_000400/side_cam.mp4) · [官方轨迹](trajectories/task_000196/episode_000400.parquet) + +## task_index=246 · Long Horizon · fusiform avocado + +**Instruction:** Pick up the fusiform avocado and place it in/on the box. + +**Description:** Official DOM simulation scenario long-horizon_franka_avocado00d_O03_00769598_7aed-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "fusiform avocado", + "the object that is closer to the robot's right at the start", + "the object moving in the robot's forward-left direction", + "green avocado", + "green fusiform avocado", + "avocado", + "the moving object with the highest initial velocity" + ], + "containers": [ + "box", + "green box", + "plastic box", + "green plastic box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=600** · 220 frames / 8.80 s + +![Camera preview](media/task_000246/episode_000600/preview.jpg) + +[同步 MP4](media/task_000246/episode_000600/synchronized.mp4) · [opst_cam MP4](media/task_000246/episode_000600/opst_cam.mp4) · [wrist_cam MP4](media/task_000246/episode_000600/wrist_cam.mp4) · [side_cam MP4](media/task_000246/episode_000600/side_cam.mp4) · [官方轨迹](trajectories/task_000246/episode_000600.parquet) + +## task_index=80 · Long Horizon · entire set of objects + +**Instruction:** Pick up the entire set of objects and place it in/on the box with Tencent Logo. + +**Description:** Official DOM simulation scenario long-horizon_franka_avocado04d_O03_00878095_8d75-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "box with Tencent Logo", + "plastic box", + "box", + "white box", + "white box with Tencent Logo" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=800** · 232 frames / 9.28 s + +![Camera preview](media/task_000080/episode_000800/preview.jpg) + +[同步 MP4](media/task_000080/episode_000800/synchronized.mp4) · [opst_cam MP4](media/task_000080/episode_000800/opst_cam.mp4) · [wrist_cam MP4](media/task_000080/episode_000800/wrist_cam.mp4) · [side_cam MP4](media/task_000080/episode_000800/side_cam.mp4) · [官方轨迹](trajectories/task_000080/episode_000800.parquet) + +## task_index=70 · Long Horizon · entire set of objects + +**Instruction:** Pick up the entire set of objects and place it in/on the white box. + +**Description:** Official DOM simulation scenario long-horizon_franka_beer01d_O03_00794991_f96b-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "white box", + "white box with Tencent Logo", + "box", + "box with Tencent Logo", + "plastic box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=1000** · 209 frames / 8.36 s + +![Camera preview](media/task_000070/episode_001000/preview.jpg) + +[同步 MP4](media/task_000070/episode_001000/synchronized.mp4) · [opst_cam MP4](media/task_000070/episode_001000/opst_cam.mp4) · [wrist_cam MP4](media/task_000070/episode_001000/wrist_cam.mp4) · [side_cam MP4](media/task_000070/episode_001000/side_cam.mp4) · [官方轨迹](trajectories/task_000070/episode_001000.parquet) + +## task_index=10235 · Pick · the moving object with the lower initial velocity + +**Instruction:** Pick up the the moving object with the lower initial velocity. + +**Description:** Official DOM simulation scenario pick_franka_avocado01d_O03_01140437_873f-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "the moving object with the lower initial velocity", + "green avocado", + "avocado", + "green long fusiform avocado", + "long fusiform avocado", + "the object that is closer to the robot's right at the start" + ], + "containers": [ + "tray", + "white tray with CapitaLand logo", + "white tray", + "tray with CapitaLand logo" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=16460** · 83 frames / 3.32 s + +![Camera preview](media/task_010235/episode_016460/preview.jpg) + +[同步 MP4](media/task_010235/episode_016460/synchronized.mp4) · [opst_cam MP4](media/task_010235/episode_016460/opst_cam.mp4) · [wrist_cam MP4](media/task_010235/episode_016460/wrist_cam.mp4) · [side_cam MP4](media/task_010235/episode_016460/side_cam.mp4) · [官方轨迹](trajectories/task_010235/episode_016460.parquet) + +## task_index=10588 · Pick · the object in the middle from left to right at the start + +**Instruction:** Pick up the the object in the middle from left to right at the start. + +**Description:** Official DOM simulation scenario pick_franka_avocado02d_O04_01042912_e0d2-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "the object in the middle from left to right at the start", + "green avocado", + "avocado", + "pear-shaped long avocado", + "green pear-shaped long avocado", + "the object farthest from the robot mounting edge at the start" + ], + "containers": [ + "square placemat features a bold, stylized illustration of a tui", + "Square placemat with a bold, stylized tui", + "square placemat", + "square placemat with yellow background" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=16837** · 89 frames / 3.56 s + +![Camera preview](media/task_010588/episode_016837/preview.jpg) + +[同步 MP4](media/task_010588/episode_016837/synchronized.mp4) · [opst_cam MP4](media/task_010588/episode_016837/opst_cam.mp4) · [wrist_cam MP4](media/task_010588/episode_016837/wrist_cam.mp4) · [side_cam MP4](media/task_010588/episode_016837/side_cam.mp4) · [官方轨迹](trajectories/task_010588/episode_016837.parquet) + +## task_index=42175 · Pick · the object moving in the robot's forward-left direction + +**Instruction:** Pick up the the object moving in the robot's forward-left direction. + +**Description:** Official DOM simulation scenario pick_franka_lime00d_O03_01174264_30d7-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "the object moving in the robot's forward-left direction", + "dark green lime", + "the object that is closer to the robot's right at the start", + "the object closer to the robot mounting edge at the start", + "lime" + ], + "containers": [ + "tray", + "tray with Meta logo", + "white tray", + "white tray with Meta logo" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=51235** · 87 frames / 3.48 s + +![Camera preview](media/task_042175/episode_051235/preview.jpg) + +[同步 MP4](media/task_042175/episode_051235/synchronized.mp4) · [opst_cam MP4](media/task_042175/episode_051235/opst_cam.mp4) · [wrist_cam MP4](media/task_042175/episode_051235/wrist_cam.mp4) · [side_cam MP4](media/task_042175/episode_051235/side_cam.mp4) · [官方轨迹](trajectories/task_042175/episode_051235.parquet) + +## task_index=42560 · Pick · lime + +**Instruction:** Pick up the lime. + +**Description:** Official DOM simulation scenario pick_franka_lime00d_O05_01312751_5a1e-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "lime", + "dark green lime" + ], + "containers": [ + "the box with the larger volume", + "the box closer to the robot at the start", + "the box that is closer to the robot's right at the start", + "the box with the larger area", + "red box", + "the box farther from the robot mounting edge at the start", + "box with MMLab At NTU words", + "plastic box", + "the taller box", + "red box with MMLab At NTU words", + "box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=51658** · 87 frames / 3.48 s + +![Camera preview](media/task_042560/episode_051658/preview.jpg) + +[同步 MP4](media/task_042560/episode_051658/synchronized.mp4) · [opst_cam MP4](media/task_042560/episode_051658/opst_cam.mp4) · [wrist_cam MP4](media/task_042560/episode_051658/wrist_cam.mp4) · [side_cam MP4](media/task_042560/episode_051658/side_cam.mp4) · [官方轨迹](trajectories/task_042560/episode_051658.parquet) + +## task_index=60569 · Pick · the object that is closest to the robot's left at the start + +**Instruction:** Pick up the the object that is closest to the robot's left at the start. + +**Description:** Official DOM simulation scenario pick_franka_tangerine00d_O04_01057450_ee6d-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "the object that is closest to the robot's left at the start", + "tangerine" + ], + "containers": [ + "pink plastic box", + "pink box", + "box", + "plastic box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=71104** · 95 frames / 3.80 s + +![Camera preview](media/task_060569/episode_071104/preview.jpg) + +[同步 MP4](media/task_060569/episode_071104/synchronized.mp4) · [opst_cam MP4](media/task_060569/episode_071104/opst_cam.mp4) · [wrist_cam MP4](media/task_060569/episode_071104/wrist_cam.mp4) · [side_cam MP4](media/task_060569/episode_071104/side_cam.mp4) · [官方轨迹](trajectories/task_060569/episode_071104.parquet) + +## task_index=60993 · Pick · the object that is closest to the robot's left at the start + +**Instruction:** Pick up the the object that is closest to the robot's left at the start. + +**Description:** Official DOM simulation scenario pick_franka_tangerine03d_O04_01097900_93cd-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "pick", + "objects": [ + "the object that is closest to the robot's left at the start", + "tangerine" + ], + "containers": [ + "tray", + "white tray", + "tray with NTU logo", + "white tray with NTU logo" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=71578** · 85 frames / 3.40 s + +![Camera preview](media/task_060993/episode_071578/preview.jpg) + +[同步 MP4](media/task_060993/episode_071578/synchronized.mp4) · [opst_cam MP4](media/task_060993/episode_071578/opst_cam.mp4) · [wrist_cam MP4](media/task_060993/episode_071578/wrist_cam.mp4) · [side_cam MP4](media/task_060993/episode_071578/side_cam.mp4) · [官方轨迹](trajectories/task_060993/episode_071578.parquet) + +## task_index=77322 · Place · moving-forward object + +**Instruction:** Pick up the moving-forward object and place it in/on the ceramic plate with blue floral patterns. + +**Description:** Official DOM simulation scenario place_franka_beer03d_O02_00082428_f57d-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "moving-forward object", + "light color beer bottle with Heineken carvings", + "beer bottle with Heineken carvings", + "beer bottle", + "moving-backward-left object", + "moving-backward-right object", + "moving-right object", + "light color beer bottle", + "moving-backward object", + "moving-left object" + ], + "containers": [ + "ceramic plate with blue floral patterns", + "ceramic plate", + "plate", + "plate with blue floral patterns" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=98470** · 111 frames / 4.44 s + +![Camera preview](media/task_077322/episode_098470/preview.jpg) + +[同步 MP4](media/task_077322/episode_098470/synchronized.mp4) · [opst_cam MP4](media/task_077322/episode_098470/opst_cam.mp4) · [wrist_cam MP4](media/task_077322/episode_098470/wrist_cam.mp4) · [side_cam MP4](media/task_077322/episode_098470/side_cam.mp4) · [官方轨迹](trajectories/task_077322/episode_098470.parquet) + +## task_index=77667 · Place · beer bottle + +**Instruction:** Pick up the beer bottle and place it in/on the the placemat that is closer to the robot's left at the start. + +**Description:** Official DOM simulation scenario place_franka_beer03d_O04_00340177_6e45-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "beer bottle", + "the object that is closer to the robot's left at the start", + "beer bottle with Heineken carvings", + "light color beer bottle", + "the moving object with the highest initial velocity", + "light color beer bottle with Heineken carvings" + ], + "containers": [ + "the placemat that is closer to the robot's left at the start", + "green square placemat", + "the placemat closer to the robot mounting edge at the start", + "square placemat" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=98947** · 117 frames / 4.68 s + +![Camera preview](media/task_077667/episode_098947/preview.jpg) + +[同步 MP4](media/task_077667/episode_098947/synchronized.mp4) · [opst_cam MP4](media/task_077667/episode_098947/opst_cam.mp4) · [wrist_cam MP4](media/task_077667/episode_098947/wrist_cam.mp4) · [side_cam MP4](media/task_077667/episode_098947/side_cam.mp4) · [官方轨迹](trajectories/task_077667/episode_098947.parquet) + +## task_index=108733 · Place · lemon + +**Instruction:** Pick up the lemon and place it in/on the tray. + +**Description:** Official DOM simulation scenario place_franka_lemon06d_O02_00026504_29ce-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "lemon" + ], + "containers": [ + "tray", + "white tray with HSBC logo", + "tray with HSBC logo", + "white tray" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=153474** · 108 frames / 4.32 s + +![Camera preview](media/task_108733/episode_153474/preview.jpg) + +[同步 MP4](media/task_108733/episode_153474/synchronized.mp4) · [opst_cam MP4](media/task_108733/episode_153474/opst_cam.mp4) · [wrist_cam MP4](media/task_108733/episode_153474/wrist_cam.mp4) · [side_cam MP4](media/task_108733/episode_153474/side_cam.mp4) · [官方轨迹](trajectories/task_108733/episode_153474.parquet) + +## task_index=111207 · Place · lemon + +**Instruction:** Pick up the lemon and place it in/on the the placemat farther from the robot mounting edge at the start. + +**Description:** Official DOM simulation scenario place_franka_lemon06d_O04_00340971_5a64-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "lemon", + "the object that is closer to the robot's right at the start", + "the object moving in the robot's backward-left direction", + "the moving object with the highest initial velocity" + ], + "containers": [ + "the placemat farther from the robot mounting edge at the start", + "green square placemat" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=154278** · 110 frames / 4.40 s + +![Camera preview](media/task_111207/episode_154278/preview.jpg) + +[同步 MP4](media/task_111207/episode_154278/synchronized.mp4) · [opst_cam MP4](media/task_111207/episode_154278/opst_cam.mp4) · [wrist_cam MP4](media/task_111207/episode_154278/wrist_cam.mp4) · [side_cam MP4](media/task_111207/episode_154278/side_cam.mp4) · [官方轨迹](trajectories/task_111207/episode_154278.parquet) + +## task_index=140594 · Place · water bottle + +**Instruction:** Pick up the water bottle and place it in/on the bowl. + +**Description:** Official DOM simulation scenario place_franka_wbottle23d_O02_00046150_50a0-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "water bottle", + "cylinder water bottle", + "cylinder water bottle with grey and orange lid", + "black cylinder water bottle", + "black cylinder water bottle with grey and orange lid", + "black water bottle", + "black water bottle with grey and orange lid", + "water bottle with grey and orange lid" + ], + "containers": [ + "bowl", + "wooden bowl", + "shallow bowl", + "wooden shallow bowl" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=206737** · 124 frames / 4.96 s + +![Camera preview](media/task_140594/episode_206737/preview.jpg) + +[同步 MP4](media/task_140594/episode_206737/synchronized.mp4) · [opst_cam MP4](media/task_140594/episode_206737/opst_cam.mp4) · [wrist_cam MP4](media/task_140594/episode_206737/wrist_cam.mp4) · [side_cam MP4](media/task_140594/episode_206737/side_cam.mp4) · [官方轨迹](trajectories/task_140594/episode_206737.parquet) + +## task_index=140471 · Place · black cylinder water bottle + +**Instruction:** Pick up the black cylinder water bottle and place it in/on the black box. + +**Description:** Official DOM simulation scenario place_franka_wbottle23d_O02_00120780_166a-tr. Object/container aliases are preserved in the metadata. + +**Success / evaluation:** Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ. + +[官方说明](https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py) + +结构化原始 instruction（完整别名）： + +```json +{ + "task": "place", + "objects": [ + "black cylinder water bottle", + "cylinder water bottle with grey and orange lid", + "black water bottle", + "water bottle", + "black cylinder water bottle with grey and orange lid", + "cylinder water bottle", + "black water bottle with grey and orange lid", + "water bottle with grey and orange lid" + ], + "containers": [ + "black box", + "box", + "plastic box", + "black plastic box" + ] +} +``` + +Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored. + +**episode_index=207108** · 105 frames / 4.20 s + +![Camera preview](media/task_140471/episode_207108/preview.jpg) + +[同步 MP4](media/task_140471/episode_207108/synchronized.mp4) · [opst_cam MP4](media/task_140471/episode_207108/opst_cam.mp4) · [wrist_cam MP4](media/task_140471/episode_207108/wrist_cam.mp4) · [side_cam MP4](media/task_140471/episode_207108/side_cam.mp4) · [官方轨迹](trajectories/task_140471/episode_207108.parquet) + +## 来源、许可与复现 + +官方数据：[原仓库](https://huggingface.co/datasets/hzxie/DOM) · 固定 revision `bbf6baa15f7253746d271a6e958e037afa537b1e`。 + +官方代码：[GitHub](https://github.com/hzxie/DynamicVLA) · revision `434dc7105cf6d559018d15af5fecbcdad44f036e`。 + +遵循上游 S-Lab License 1.0，仅允许非商业用途；见 LICENSE。 + +选择性提取记录、原始时间区间、task/episode 对应、相机元数据和校验均在 `provenance/`。不下载场景资产、模型权重或完整数据仓库。 + +复现脚本：`scripts/build_examples.py`。浏览页源码另见 `browser/` 和 `scripts/build_browser.py`。 diff --git a/data/all.parquet b/data/all.parquet new file mode 100644 index 0000000000000000000000000000000000000000..a7c14531211ea3135f1bde5d98b5137edb9869f5 --- /dev/null +++ b/data/all.parquet @@ -0,0 +1,3 @@ +version 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orange lid\", \"cylinder water bottle\", \"black water bottle with grey and orange lid\", \"water bottle with grey and orange lid\"], \"containers\": [\"black box\", \"box\", \"plastic box\", \"black plastic box\"]}" + }, + "camera_metadata": { + "episode_index": 207108, + "filename": "place_franka_wbottle23d_O02_00120780_166a-tr", + "cameras": [ + { + "name": "wrist_cam", + "width": 480, + "height": 360, + "offset": { + "pos": [ + 0.065, + 0.0, + 0.0 + ], + "rot": [ + 0, + 0.7071068, + 0.7071068, + 0 + ], + "convention": "opengl" + }, + "data_types": [ + "rgb", + "semantic_segmentation" + ], + "focal": 2.3 + }, + { + "name": "opst_cam", + "width": 480, + "height": 360, + "offset": { + "pos": [ + 1, + 0, + 0.6 + ], + "rot": [ + 0.6123724356957946, + 0.3535533905932737, + 0.35355339059327373, + 0.6123724356957945 + ], + "convention": "opengl" + }, + "data_types": [ + "rgb", + "semantic_segmentation" + ], + "focal": 2.3 + }, + { + "name": "side_cam", + "width": 480, + "height": 360, + "offset": { + "pos": [ + 0.5, + 1, + 0.35 + ], + "rot": [ + 4.329780281177467e-17, + -4.329780281177466e-17, + 0.7071067811865475, + 0.7071067811865476 + ], + "convention": "opengl" + }, + "data_types": [ + "rgb", + "semantic_segmentation" + ], + "focal": 2.3 + } + ] + }, + "source_filename": "place_franka_wbottle23d_O02_00120780_166a-tr", + "views": [ + { + "camera": "opst_cam", + "label": "OPPOSITE CAMERA", + "path": "media/task_140471/episode_207108/opst_cam.mp4", + "source_path": "videos/chunk-207/observation.images.opst_cam/episode_207108.mp4", + "source_sha256": "7c0bdfd29a0215f637b098cd7f53996db3068543c8a3e213c4abf359dc6f5045", + "source_bytes": 511596, + "output_sha256": "a81f28840bfa06e8ce7e6d8025b8e21410ed1fe6d39a888613775024f4f5367f" + }, + { + "camera": "wrist_cam", + "label": "WRIST CAMERA", + "path": "media/task_140471/episode_207108/wrist_cam.mp4", + "source_path": "videos/chunk-207/observation.images.wrist_cam/episode_207108.mp4", + "source_sha256": "bec11a988f555bf8ec16fc52307eb9ff284269dc588af91e2cb9310d7ac15fed", + "source_bytes": 244255, + "output_sha256": "9daff536f1a24adf791975312c9924e8603167e8e73823b4a976121e67021bfc" + }, + { + "camera": "side_cam", + "label": "SIDE CAMERA", + "path": "media/task_140471/episode_207108/side_cam.mp4", + "source_path": "videos/chunk-207/observation.images.side_cam/episode_207108.mp4", + "source_sha256": "e410903147a1eba4dda7312154314e9998e0a7bb8c1bd3acd865fe2187311af3", + "source_bytes": 432500, + "output_sha256": "7b44b97f25972a2e2d8fc5edd90e9672bc87e13512ad4fcd115847e9f5954e58" + } + ], + "trajectory_source_path": "data/chunk-207/episode_207108.parquet", + "trajectory_path": "trajectories/task_140471/episode_207108.parquet", + "composite_path": "media/task_140471/episode_207108/synchronized.mp4", + "preview_path": "media/task_140471/episode_207108/preview.jpg" + } +] \ No newline at end of file diff --git a/provenance/source.json b/provenance/source.json new file mode 100644 index 0000000000000000000000000000000000000000..31df346e5f9088fa41fa1627089f5b8ea177fb63 --- /dev/null +++ b/provenance/source.json @@ -0,0 +1,17 @@ +{ + "benchmark": "DynamicVLA", + "repo_id": "hzxie/DOM", + "revision": "bbf6baa15f7253746d271a6e958e037afa537b1e", + "code_repository": "hzxie/DynamicVLA", + "code_revision": "434dc7105cf6d559018d15af5fecbcdad44f036e", + "note": "DOM has 3 task families, not 3 task_index values: 140,932 structured instruction IDs and 207,306 episodes. This subset selects 6 demonstrations per family (18 total), preserving actual task_index/episode_index and structured instructions. All 3 published cameras are single-arm cameras: opposite, wrist, side, not top/left/right arms.", + "date": "2026-10-03", + "video_policy": "Complete selected episodes, converted to browser-friendly H.264 MP4 at original 25 FPS. No generated simulation rollouts.", + "actual_episode_score": "Not published in released LeRobot features; null, not assumed successful.", + "episode_selection": "Two episodes from three spaced metadata windows for pick/place, six spaced long-horizon scenarios.", + "camera_order": [ + "opst_cam", + "wrist_cam", + "side_cam" + ] +} \ No newline at end of file diff --git a/provenance/tasks.json b/provenance/tasks.json new file mode 100644 index 0000000000000000000000000000000000000000..0bf22c119caa3fe4103bf5cf42248d832610bd27 --- /dev/null +++ b/provenance/tasks.json @@ -0,0 +1,514 @@ +[ + { + "task_index": 0, + "key": "task_000000", + "name": "Long Horizon · entire set of objects", + "category": "Long-Horizon", + "instruction": "Pick up the entire set of objects and place it in/on the white box.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "white box", + "box with Tesla Logo", + "white box with Tesla Logo", + "plastic box", + "box" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_apple00d_O03_00772319_1dc5-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_apple00d_O03_00772319_1dc5-tr", + "published_episode_count": null + }, + { + "task_index": 135, + "key": "task_000135", + "name": "Long Horizon · entire set of objects", + "category": "Long-Horizon", + "instruction": "Pick up the entire set of objects and place it in/on the plastic box.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "plastic box", + "pink box", + "pink plastic box", + "box" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_apple08d_O03_00819030_04bc-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_apple08d_O03_00819030_04bc-tr", + "published_episode_count": null + }, + { + "task_index": 196, + "key": "task_000196", + "name": "Long Horizon · entire set of objects", + "category": "Long-Horizon", + "instruction": "Pick up the entire set of objects and place it in/on the box.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "box", + "pink box", + "pink plastic box", + "plastic box" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_apple14d_O03_00888109_ee3b-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_apple14d_O03_00888109_ee3b-tr", + "published_episode_count": null + }, + { + "task_index": 246, + "key": "task_000246", + "name": "Long Horizon · fusiform avocado", + "category": "Long-Horizon", + "instruction": "Pick up the fusiform avocado and place it in/on the box.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "fusiform avocado", + "the object that is closer to the robot's right at the start", + "the object moving in the robot's forward-left direction", + "green avocado", + "green fusiform avocado", + "avocado", + "the moving object with the highest initial velocity" + ], + "containers": [ + "box", + "green box", + "plastic box", + "green plastic box" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_avocado00d_O03_00769598_7aed-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_avocado00d_O03_00769598_7aed-tr", + "published_episode_count": null + }, + { + "task_index": 80, + "key": "task_000080", + "name": "Long Horizon · entire set of objects", + "category": "Long-Horizon", + "instruction": "Pick up the entire set of objects and place it in/on the box with Tencent Logo.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "box with Tencent Logo", + "plastic box", + "box", + "white box", + "white box with Tencent Logo" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_avocado04d_O03_00878095_8d75-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_avocado04d_O03_00878095_8d75-tr", + "published_episode_count": null + }, + { + "task_index": 70, + "key": "task_000070", + "name": "Long Horizon · entire set of objects", + "category": "Long-Horizon", + "instruction": "Pick up the entire set of objects and place it in/on the white box.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "long_horizon", + "objects": [ + "entire set of objects" + ], + "containers": [ + "white box", + "white box with Tencent Logo", + "box", + "box with Tencent Logo", + "plastic box" + ] + }, + "description": "Official DOM simulation scenario long-horizon_franka_beer01d_O03_00794991_f96b-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "long-horizon_franka_beer01d_O03_00794991_f96b-tr", + "published_episode_count": null + }, + { + "task_index": 10235, + "key": "task_010235", + "name": "Pick · the moving object with the lower initial velocity", + "category": "Pick", + "instruction": "Pick up the the moving object with the lower initial velocity.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "the moving object with the lower initial velocity", + "green avocado", + "avocado", + "green long fusiform avocado", + "long fusiform avocado", + "the object that is closer to the robot's right at the start" + ], + "containers": [ + "tray", + "white tray with CapitaLand logo", + "white tray", + "tray with CapitaLand logo" + ] + }, + "description": "Official DOM simulation scenario pick_franka_avocado01d_O03_01140437_873f-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_avocado01d_O03_01140437_873f-tr", + "published_episode_count": null + }, + { + "task_index": 10588, + "key": "task_010588", + "name": "Pick · the object in the middle from left to right at the start", + "category": "Pick", + "instruction": "Pick up the the object in the middle from left to right at the start.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "the object in the middle from left to right at the start", + "green avocado", + "avocado", + "pear-shaped long avocado", + "green pear-shaped long avocado", + "the object farthest from the robot mounting edge at the start" + ], + "containers": [ + "square placemat features a bold, stylized illustration of a tui", + "Square placemat with a bold, stylized tui", + "square placemat", + "square placemat with yellow background" + ] + }, + "description": "Official DOM simulation scenario pick_franka_avocado02d_O04_01042912_e0d2-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_avocado02d_O04_01042912_e0d2-tr", + "published_episode_count": null + }, + { + "task_index": 42175, + "key": "task_042175", + "name": "Pick · the object moving in the robot's forward-left direction", + "category": "Pick", + "instruction": "Pick up the the object moving in the robot's forward-left direction.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "the object moving in the robot's forward-left direction", + "dark green lime", + "the object that is closer to the robot's right at the start", + "the object closer to the robot mounting edge at the start", + "lime" + ], + "containers": [ + "tray", + "tray with Meta logo", + "white tray", + "white tray with Meta logo" + ] + }, + "description": "Official DOM simulation scenario pick_franka_lime00d_O03_01174264_30d7-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_lime00d_O03_01174264_30d7-tr", + "published_episode_count": null + }, + { + "task_index": 42560, + "key": "task_042560", + "name": "Pick · lime", + "category": "Pick", + "instruction": "Pick up the lime.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "lime", + "dark green lime" + ], + "containers": [ + "the box with the larger volume", + "the box closer to the robot at the start", + "the box that is closer to the robot's right at the start", + "the box with the larger area", + "red box", + "the box farther from the robot mounting edge at the start", + "box with MMLab At NTU words", + "plastic box", + "the taller box", + "red box with MMLab At NTU words", + "box" + ] + }, + "description": "Official DOM simulation scenario pick_franka_lime00d_O05_01312751_5a1e-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_lime00d_O05_01312751_5a1e-tr", + "published_episode_count": null + }, + { + "task_index": 60569, + "key": "task_060569", + "name": "Pick · the object that is closest to the robot's left at the start", + "category": "Pick", + "instruction": "Pick up the the object that is closest to the robot's left at the start.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "the object that is closest to the robot's left at the start", + "tangerine" + ], + "containers": [ + "pink plastic box", + "pink box", + "box", + "plastic box" + ] + }, + "description": "Official DOM simulation scenario pick_franka_tangerine00d_O04_01057450_ee6d-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_tangerine00d_O04_01057450_ee6d-tr", + "published_episode_count": null + }, + { + "task_index": 60993, + "key": "task_060993", + "name": "Pick · the object that is closest to the robot's left at the start", + "category": "Pick", + "instruction": "Pick up the the object that is closest to the robot's left at the start.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "pick", + "objects": [ + "the object that is closest to the robot's left at the start", + "tangerine" + ], + "containers": [ + "tray", + "white tray", + "tray with NTU logo", + "white tray with NTU logo" + ] + }, + "description": "Official DOM simulation scenario pick_franka_tangerine03d_O04_01097900_93cd-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "pick_franka_tangerine03d_O04_01097900_93cd-tr", + "published_episode_count": null + }, + { + "task_index": 77322, + "key": "task_077322", + "name": "Place · moving-forward object", + "category": "Place", + "instruction": "Pick up the moving-forward object and place it in/on the ceramic plate with blue floral patterns.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "place", + "objects": [ + "moving-forward object", + "light color beer bottle with Heineken carvings", + "beer bottle with Heineken carvings", + "beer bottle", + "moving-backward-left object", + "moving-backward-right object", + "moving-right object", + "light color beer bottle", + "moving-backward object", + "moving-left object" + ], + "containers": [ + "ceramic plate with blue floral patterns", + "ceramic plate", + "plate", + "plate with blue floral patterns" + ] + }, + "description": "Official DOM simulation scenario place_franka_beer03d_O02_00082428_f57d-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.", + "doc_url": "https://github.com/hzxie/DynamicVLA/blob/434dc7105cf6d559018d15af5fecbcdad44f036e/simulations/configs/termination_cfg.py", + "source_filename": "place_franka_beer03d_O02_00082428_f57d-tr", + "published_episode_count": null + }, + { + "task_index": 77667, + "key": "task_077667", + "name": "Place · beer bottle", + "category": "Place", + "instruction": "Pick up the beer bottle and place it in/on the the placemat that is closer to the robot's left at the start.", + "instruction_note": "Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.", + "instruction_metadata": { + "task": "place", + "objects": [ + "beer bottle", + "the object that is closer to the robot's left at the start", + "beer bottle with Heineken carvings", + "light color beer bottle", + "the moving object with the highest initial velocity", + "light color beer bottle with Heineken carvings" + ], + "containers": [ + "the placemat that is closer to the robot's left at the start", + "green square placemat", + "the placemat closer to the robot mounting edge at the start", + "square placemat" + ] + }, + "description": "Official DOM simulation scenario place_franka_beer03d_O04_00340177_6e45-tr. Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. 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Object/container aliases are preserved in the metadata.", + "success_rule": "Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. 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480, + "height": 360, + "duration": "4.200000", + "nb_frames": "105" + }, + { + "path": "media/task_140471/episode_207108/wrist_cam.mp4", + "codec_name": "h264", + "width": 480, + "height": 360, + "duration": "4.200000", + "nb_frames": "105" + }, + { + "path": "media/task_140471/episode_207108/side_cam.mp4", + "codec_name": "h264", + "width": 480, + "height": 360, + "duration": "4.200000", + "nb_frames": "105" + }, + { + "path": "media/task_140471/episode_207108/synchronized.mp4", + "codec_name": "h264", + "width": 960, + "height": 268, + "duration": "4.200000", + "nb_frames": "105" + } + ] + } + ] +} \ No newline at end of file diff --git a/reference/LICENSE b/reference/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..db6e4acb20f5a797c784ae4682d714f693a2c25a --- /dev/null +++ b/reference/LICENSE @@ -0,0 +1,35 @@ +S-Lab License 1.0 + +Copyright 2026 S-Lab + +Redistribution and use for non-commercial purpose in source and +binary forms, with or without modification, are permitted provided +that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. diff --git a/reference/README.md b/reference/README.md new file mode 100644 index 0000000000000000000000000000000000000000..80ad4316d044260f8526352a20f21e7d7af2105e --- /dev/null +++ b/reference/README.md @@ -0,0 +1,239 @@ + + +# DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation + +[Haozhe Xie](https://haozhexie.com), [Beichen Wen](https://wenbc21.github.io/), Jiarui Zheng, [Zhaoxi Chen](https://frozenburning.github.io/), [Fangzhou Hong](https://hongfz16.github.io/), [Haiwen Diao](https://paranioar.github.io/), [Ziwei Liu](https://liuziwei7.github.io/) + +S-Lab, Nanyang Technological University + +[![codefactor badge](https://www.codefactor.io/repository/github/hzxie/DynamicVLA/badge)](https://www.codefactor.io/repository/github/hzxie/DynamicVLA) +![Counter](https://api.infinitescript.com/badgen/count?name=hzxie/DynamicVLA) +[![arXiv](https://img.shields.io/badge/arXiv-2601.22153-b31b1b.svg)](https://arxiv.org/abs/2601.22153) +[![YouTube](https://img.shields.io/badge/Spotlight%20Video-%23FF0000.svg?logo=YouTube&logoColor=white)](https://youtu.be/NmJnHcI04_Q) +[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-DOM%20Dataset-orange)](https://huggingface.co/datasets/hzxie/DOM) + +![Teaser](https://github.com/user-attachments/assets/ffc2071a-c4b8-4ebf-9a41-870de65bb3da) + +## Changelog🔥 + +- [2026/04/26] Released training and testing code. +- [2026/01/26] Repository created. + +## Cite this work📝 + +``` +@inproceedings{xie2026dynamicvla, + title = {{DynamicVLA:} A Vision-Language-Action Model for Dynamic Object Manipulation}, + author = {Xie, Haozhe and + Wen, Beichen and + Zheng, Jiarui and + Chen, Zhaoxi and + Hong, Fangzhou and + Diao, Haiwen and + Liu, Ziwei}, + booktitle = {NeurIPS}, + year = {2026} +} +``` + +## Dataset and Pretrained Models 🛢️ + +### DOM Dataset + +- [DOM Training Set](https://huggingface.co/datasets/hzxie/DOM) – for training DynamicVLA +- [DOM Testing Set](https://gateway.infinitescript.com/?f=DOM-Test) – for benchmarking; includes test configurations and a subset of 3D scenes +- [DOM 3D Objects](https://gateway.infinitescript.com/?f=DOM-3D-Objects) – assets for data generation and benchmarking +- [DOM 3D Scenes](https://gateway.infinitescript.com/?f=DOM-3D-Scenes) – full scene assets for data generation + +### Pretrained Models + +- [DynamicVLA (trained on DOM)](https://huggingface.co/hzxie/dynamic-vla-DOM) + + +## Installation 📥 + +We recommend using **conda** to create two separate environments: + +- one for **model training & inference** +- one for **Isaac Lab simulation & evaluation** + +### PyTorch Environment + +- Install **Python 3.10** and **PyTorch 2.7.1** *(Other versions should work, but are not fully tested)* +- Install dependencies: + +```bash +pip install -r requirements.txt +``` + +### Isaac Lab Environment + +- Install **Python 3.11** *(Other versions should work, but are not fully tested)* +- Install **Isaac Sim 5.1.0** and **Isaac Lab 2.3.2** + Follow the official guide: https://isaac-sim.github.io/IsaacLab/v2.3.2/source/setup/installation/index.html +- Install additional dependencies: + +```bash +pip install shapely pyzmq h5py +``` + +## Benchmarking Your Policy 🏅 + +### Prepare scenes and objects + +Download [DOM Testing Set](#dom-dataset) (including a subset of DOM 3D scenes) and [DOM 3D Objects](#dom-dataset). + +``` +PROJECT_ROOT/ +├── objects/ # Put DOM 3D Objects here +├── scenes/ # Put DOM 3D Scenes here +| └── textures # Put the textures of 3D scenes here +| └── *.usd # Put the USD files of 3D scenes here +├── tests/ # Put DOM Testing Set here +| └── *.json +|── test-envs.txt # The list of test environments (included in DOM Testing Set) +├── datasets/ # Generated simulated datasets will be stored here +└── dynamic-vla/ # git clone https://github.com/hzxie/DynamicVLA dynamic-vla + └── runs # Create folder for evaluation and checkpoints output +``` + +### Run Policy Evaluation Server + +> ⚠️ This step requires the **Isaac Lab environment** + +From the `PROJECT_ROOT/dynamic-vla` directory, run: + +```bash +python3 simulations/evaluate.py \ + --scene_dir ../scenes \ + --output_dir ../output/evaluation \ + --env_cfg ../test-envs.txt \ + --enable_cameras --headless -n 20 --save +``` + +**Arguments:** + +- `test-envs.txt` are provided by [DOM Testing Set](#dom-dataset) +- `-n 20`: run 20 trials per environment +- `--save`: save evaluation videos to `output_dir` +- `--headless`: run without GUI +- `--enable_cameras`: enable visual observations + +### Run Policy Inference + +> ⚠️ This step requires the **PyTorch environment** + +From the `PROJECT_ROOT/dynamic-vla` directory, run: + +```bash +python3 scripts/inference.py \ + -p /path/to/vla-checkpoint \ + -r euler -d -s +``` + +**Arguments:** + +- `-p`: path to the trained model checkpoint +- `-r euler`: use Euler angles for rotation representation +- `-d`: enable **delta actions** *(actions are relative to current state)* +- `-s`: enable **contiguous inference** *(if supported by the model)* + +## Simulated Dataset Generation 🧪 + +> ⚠️ This step requires the **Isaac Lab environment** + +### IsaacSim Simulation + +From the `PROJECT_ROOT/dynamic-vla` directory, you can generate synthetic data using: + +```bash +python3 simulations/simulate.py \ + --headless --enable_cameras --seed 42 --save --task place +``` + +Example configuration file: `simulations/configs/sim_cfg.yaml` + +**Arguments:** + +- `--task` : task type to simulate. Options: `pick`, `place`, `long-horizon`. +- `--robot`: robot type used in simulation _(default: `franka`, also supports `piper`)_. +- `--headless`: run simulation without GUI. +- `--enable_cameras`: include visual observations in the output dataset. +- `--debug`: enable debug mode and render trajectories as `.mp4` videos. +- `--seed`: random seed for simulation _(automatically increments for each run if specified)_. +- `-n`, `--n_simulations`: number of simulation episodes to generate *(default: `10,000`)*. +- `--save`: save generated simulation data in HDF5 format. + +### Trajectory Replay + +After data generation, convert the trajectories into a format compatible with VLA training: + +```bash +python3 scripts/translate_dataset_seq.py \ + --dataset_dir ../datasets --output_dir ../datasets-tr \ + --enable_cameras --headless --save +``` + +**Arguments:** + +- `--dataset_dir`: directory containing the raw simulation datasets. +- `--output_dir`: directory to store the processed trajectories. +- `--enable_cameras`: include visual observations in the output dataset. +- `--headless`: run simulation without GUI. +- `--save`: save generated simulation data in HDF5 format. + +### Convert LeRobot Dataset + +We provide a script to convert the generated `.h5` files into the LeRobot dataset *(v2.1 format)*, using **Euler angles** as the rotation representation: + +```bash +python3 scripts/create_lerobot_dataset.py \ + --dataset_dir ../datasets-tr --repo hzxie/DOM --rotation euler +``` + +This will create lerobot dataset using all the hdf5 datasets in the default output directory. + +## Training 👩🏽‍💻 + +> ⚠️ This step requires the **PyTorch environment** + +From the `PROJECT_ROOT/dynamic-vla` directory, run: + +```bash +torchrun --nnodes=1 --nproc_per_node=8 --standalone run.py \ + -c configs/dynamicvla.yaml \ + -p /path/to/pretrained/model + -d hzxie/DOM +``` + +**Arguments:** + +- `--nnodes`: number of compute nodes (machines) used for distributed training +- `--nproc_per_node`: number of GPUs per node +- `-c`: path to the training config file +- `-p`: path to the pretrained model checkpoint *(optional)* +- `-d`: name of the LeRobot dataset *(v2.1 format)* + +### Checkpoint Evaluation + +> ⚠️ This step requires the **PyTorch environment** + +During training, you can automatically evaluate checkpoints using the following script: + +```bash +python3 scripts/eval_checkpoints.py \ + -r euler -d -s -p "*fvit*46k*" \ + --ckpt_dir ./runs/checkpoints/ \ +``` + +**Arguments:** + +- `--ckpt_dir`: directory containing the checkpoints to be evaluated. +- `-p`: pattern used to match checkpoint filenames *(supports wildcard patterns)*. +- `--host`: host address of the evaluation server *(default: `127.0.0.1`)*. +- `--img_port`: port used for the image stream on the evaluation server *(default: `3186`)*. +- `--act_port`: port used for the action stream on the evaluation server *(default: `3188`)*. + +## License 🗒️ + +This project is licensed under [NTU S-Lab License 1.0](https://github.com/hzxie/DynamicVLA/blob/master/LICENSE). Redistribution and use should follow this license. diff --git a/reference/simulations/configs/sim_cfg.yaml b/reference/simulations/configs/sim_cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0739bd509a2c3f84b8a1e6eb6108aae5a876fdcd --- /dev/null +++ b/reference/simulations/configs/sim_cfg.yaml @@ -0,0 +1,102 @@ +scene: + cameras: # Configure camera position and pose + - name: "opst_cam" + position: [1, 0, 0.6] + rotation: [0, 60, 90] + prim_path: "/Robot/OppositeCamera" + - name: "side_cam" + position: [0.5, 1, 0.35] + rotation: [-90, 0, 180] + prim_path: "/Robot/SideCamera" + objects: # Configure object + n_objects: 1 # number of objects in simulation + mass: 0.05 # mass of object + prob_rnd_quat: 0.85 # probability of object having random orientation. Otherwise it will have default orientation. + prob_static: 0.5 # probability of object being static. Otherwise it will be moving. + moving_speed: [0.15, 0.75] # range of speed for moving objects, generated uniformly random + # moving_speed: [0.05, 0.25] + friction: [0.5, 1.5] # range of friction for objects, generated uniformly random + perturbation: # configure perturbation for objects. Delete this if you don't intend to introduce perturbation for objects. + # force: [0.001, 0.005] + # torque: [0.0005, 0.0025] + tag_thresholds: # configure thresholds for tag generation. If the value difference between two objects is less than the threshold, the value is considered same, thus also the rank. + height: 0.1 + area: 0.1 + volume: 0.1 + position_from_left: 0.1 + position_from_bottom: 0.1 + distance_from_robot: 0.3 + velocity: 0.2 + categories: # configure available categories of objects. All objects are randomly selected from available items whose category is in this list. Must exist in the objects directory. + - apple + - avocado + - beer + - bottle + - can + - cup + - egg + - kiwi + - lemon + - lime + - onion + - orange + - peach + - potato + - tangerine + - tomato + # - unseen + containers: # Configure containers + n_containers: 1 # number of containers + mass: 0.1 # mass of container + categories: # configure available categories of containers. Must exist in the objects directory. + - bowl + - box + - plate + - placemat + - tray + +camera: + width: 480 + height: 360 + fps: 25 + focal_length: 2.3 + focus_distance: 400 + horizontal_aperture: 4.6 + clip: + near: 0.01 + far: 10000 + data_types: + - rgb + - semantic_segmentation + # - depth + +lighting: # lighting configuration, config for each scene is selected randomly in the respective range. + temperature: [4000, 8000] + intensity: [150, 750] + position: + x: [-50, 50] + y: [-50, 50] + z: [10, 20] + +tasks: # configure task behaviour + pick: + sm: "state_machines.pick_sm.PickStateMachine" # selects the state machine to run when generating scenes executing the respective task + episode_length: 10 # max episode length, terminating with failure if exceeded + place: + sm: "state_machines.place_sm.PlaceStateMachine" + episode_length: 12 + long-horizon: + sm: "state_machines.place_sm.PlaceStateMachine" + episode_length: 20 + +robots: # configure robots in simulation + franka: + init_pose: [0.465906, 0.0, 0.382970, 0.008583, 0.921765, 0.020404, 0.387116] + final_pose: [0.3, 0, 0.3, -1, 0, 0, 0] + gripper_length: 0.045 + max_reach_dist: 0.75 + piper: + init_pose: [0.373, 0.0, 0.271, 0.0, 0.9739, 0.0, 0.227] + final_pose: [0.373, 0.0, 0.271, -1, 0, 0, 0] + gripper_length: 0.09 + max_reach_dist: 0.55 diff --git a/reference/simulations/configs/termination_cfg.py b/reference/simulations/configs/termination_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..e7b6eac9a42752f6aaf0cce87b4695c7be1f0558 --- /dev/null +++ b/reference/simulations/configs/termination_cfg.py @@ -0,0 +1,226 @@ +# -*- coding: utf-8 -*- +# +# @File: termination_cfg.py +# @Author: Haozhe Xie +# @Date: 2025-09-26 10:24:59 +# @Last Modified by: Haozhe Xie +# @Last Modified at: 2025-12-11 06:53:23 +# @Email: root@haozhexie.com + +from typing import Dict + +import torch +from isaaclab.envs import ManagerBasedRLEnv +from isaaclab.managers import SceneEntityCfg, TerminationTermCfg +from isaaclab.utils import configclass +from isaaclab_tasks.manager_based.manipulation.lift import mdp + +from simulations import helpers + + +def is_object_picked( + env: ManagerBasedRLEnv, + goal_position: torch.Tensor, + tolerance: float, + objects: list[str] = ["object"], + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + assert len(objects) == 1, "Only single object picking is supported." + object = env.scene[objects[0]] + ee_frame = env.scene[ee_frame_cfg.name] + robot = env.scene[robot_cfg.name] + + object_position_w = object.data.root_pos_w + eef_position_w = ee_frame.data.target_pos_w[..., 0, :] + + object_eef_dist = torch.norm(eef_position_w - object_position_w, dim=1) + goal_position_r = goal_position.to(device=robot.data.root_pos_w.device) + eef_position_r = helpers.get_robot_relative_position( + ee_frame.data.target_pos_w[..., 0, :] - robot.data.root_pos_w, + robot.data.root_quat_w, + ) + eef_goal_dist = torch.norm(goal_position_r - eef_position_r, dim=1) + return object_eef_dist < tolerance and eef_goal_dist < tolerance + + +def are_objects_placed( + env: ManagerBasedRLEnv, + goal_position: torch.Tensor, + objects: list[str], + object_sizes: Dict[str, torch.Tensor], + container_size: torch.Tensor, + tolerance: float, + container_cfg: SceneEntityCfg = SceneEntityCfg("container"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + objects_placed = torch.ones(env.num_envs, dtype=torch.bool, device=env.device) + container = env.scene[container_cfg.name] + ee_frame = env.scene[ee_frame_cfg.name] + robot = env.scene[robot_cfg.name] + env_origins = robot.data.root_pos_w + robot_quat = robot.data.root_quat_w + container_position = helpers.get_robot_relative_position( + container.data.root_pos_w - env_origins, robot_quat + ) + containier_size = helpers.get_object_relative_bbox( + container_size, container.data.root_quat_w, robot_quat + ) + for obj in objects: + object = env.scene[obj] + object_position = helpers.get_robot_relative_position( + object.data.root_pos_w - env_origins, robot_quat + ) + object_size = helpers.get_object_relative_bbox( + object_sizes[obj], object.data.root_quat_w, robot_quat + ) + objects_placed = torch.logical_and( + objects_placed, + helpers.is_object_placed( + object_position, + object_size, + container_position, + containier_size, + ), + ) + + goal_position_r = goal_position.to(device=env_origins.device) + eef_position_r = helpers.get_robot_relative_position( + ee_frame.data.target_pos_w[..., 0, :] - env_origins, robot_quat + ) + eef_goal_dist = torch.norm(goal_position_r - eef_position_r, dim=1) + return torch.logical_and(objects_placed, eef_goal_dist < tolerance) + + +def are_objects_dropped( + env: ManagerBasedRLEnv, + minimum_height: float, + objects: list[str], +) -> torch.Tensor: + object_dropped = torch.zeros(env.num_envs, dtype=torch.bool, device=env.device) + for obj in objects: + _dropped = mdp.root_height_below_minimum( + env, minimum_height, SceneEntityCfg(obj) + ) + object_dropped = torch.logical_or(object_dropped, _dropped) + + return object_dropped + + +def are_objects_unreachable( + env: ManagerBasedRLEnv, + max_reach_dist: float, + objects: list[str], + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + object_unreachable = torch.zeros(env.num_envs, dtype=torch.bool, device=env.device) + robot = env.scene[robot_cfg.name] + env_origins = robot.data.root_pos_w + robot_quat = robot.data.root_quat_w + for obj in objects: + object = env.scene[obj] + object_position = helpers.get_robot_relative_position( + object.data.root_pos_w - env_origins, robot_quat + ) + obj_dist = torch.norm(object_position) + object_unreachable = torch.logical_or( + object_unreachable, obj_dist > max_reach_dist + ) + + return object_unreachable + + +def get_done_term(terms: list[str]) -> str | None: + DONE_TERMS = ["object_picked", "objects_placed"] + + for term in DONE_TERMS: + if term in terms: + return term + + return None + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = TerminationTermCfg(func=mdp.time_out, time_out=True) + object_dropping = TerminationTermCfg( + func=are_objects_dropped, + params={ + "minimum_height": 0.1, + "objects": ["object"], + }, + time_out=True, + ) + # object_unreachable = TerminationTermCfg( + # func=are_objects_unreachable, + # params={ + # "max_reach_dist": 0, + # "objects": ["object"], + # }, + # time_out=True, + # ) + + +@configclass +class PickTerminationsCfg(TerminationsCfg): + """Termination terms for the Pick task.""" + + object_picked = TerminationTermCfg( + func=is_object_picked, + params={"goal_position": None, "tolerance": 0.015}, + time_out=False, + ) + + +@configclass +class PlaceTerminationsCfg(TerminationsCfg): + """Termination terms for the Pick task.""" + + objects_placed = TerminationTermCfg( + func=are_objects_placed, + params={ + "goal_position": None, + "objects": None, + "object_sizes": None, + "container_size": None, + "tolerance": 0.015, + }, + time_out=False, + ) + container_dropping = TerminationTermCfg( + func=mdp.root_height_below_minimum, + params={ + "minimum_height": 0.1, + "asset_cfg": SceneEntityCfg("container"), + }, + time_out=True, + ) + + +def get_termination_cfg(task: str, args: dict = {}) -> TerminationsCfg: + done_term = None + if task == "pick": + cfg = PickTerminationsCfg() + done_term = cfg.object_picked + elif task in ["place", "long-horizon"]: + cfg = PlaceTerminationsCfg() + done_term = cfg.objects_placed + else: + cfg = TerminationsCfg() + + for k, v in args.items(): + if k in cfg.object_dropping.params: + cfg.object_dropping.params[k] = v + # if k in cfg.object_unreachable.params: + # cfg.object_unreachable.params[k] = v + + # Update the parameters of the done term + if done_term is not None: + for k, v in args.items(): + if k in done_term.params: + done_term.params[k] = v + + return cfg diff --git a/reference/simulations/helpers.py b/reference/simulations/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..99b10f8516d88dffe02ffbf10ad040caac63709c --- /dev/null +++ b/reference/simulations/helpers.py @@ -0,0 +1,319 @@ +# -*- coding: utf-8 -*- +# +# @File: helpers.py +# @Author: Haozhe Xie +# @Date: 2025-10-03 19:04:52 +# @Last Modified by: Haozhe Xie +# @Last Modified at: 2025-12-07 14:26:51 +# @Email: root@haozhexie.com + +import math + +import numpy as np +import torch +from PIL import Image +from scipy.spatial.transform import Rotation as R + + +def get_semantic_tags(): + KNOWN_TAGS = {"ROBOT": 1, "OBJECT_MAIN": 2, "CONTAINER_MAIN": 3} + for i in range(8): # Support up to 8 background objects/containers + KNOWN_TAGS["OBJECT%02d" % (i + 1)] = 4 + i + KNOWN_TAGS["CONTAINER%02d" % (i + 1)] = 12 + i + + return KNOWN_TAGS + + +def get_semantic_map(mask): + PALETTE = np.array([[i, i, i] for i in range(256)]) + PALETTE[:16] = np.array( + [ + [0, 0, 0], + [128, 0, 0], + [0, 128, 0], + [128, 128, 0], + [0, 0, 128], + [128, 0, 128], + [0, 128, 128], + [128, 128, 128], + [64, 0, 0], + [191, 0, 0], + [64, 128, 0], + [191, 128, 0], + [64, 0, 128], + [191, 0, 128], + [64, 128, 128], + [191, 128, 128], + ] + ) + mask = Image.fromarray(mask.astype(np.uint8), mode="P") + mask.putpalette(PALETTE.reshape(-1).tolist()) + return np.array(mask.convert("RGB")) + + +def get_object_relative_bbox(object_size, object_quat_w, robot_quat): + from isaaclab.utils.math import quat_apply + + batch_size = object_quat_w.size(0) + object_size_rot = torch.eye(3, device=object_size.device) * object_size + # object_size_rot = torch.eye(3, device=object_size.device).unsqueeze(0) * object_size.unsqueeze(-1) + object_size_x_rot = quat_apply( + object_quat_w, + object_size_rot[0:1, :].repeat(batch_size, 1), + ) + object_size_y_rot = quat_apply( + object_quat_w, + object_size_rot[1:2, :].repeat(batch_size, 1), + ) + object_size_z_rot = quat_apply( + object_quat_w, + object_size_rot[2:3, :].repeat(batch_size, 1), + ) + return torch.cat( + [ + get_robot_relative_position(object_size_x_rot, robot_quat).unsqueeze(1), + get_robot_relative_position(object_size_y_rot, robot_quat).unsqueeze(1), + get_robot_relative_position(object_size_z_rot, robot_quat).unsqueeze(1), + ], + dim=1, + ) + + +def get_robot_relative_position(point, robot_quat): + from isaaclab.utils.math import quat_apply, quat_inv + + # inv_quat = scipy.spatial.transform.Rotation.from_quat(robot_quat).inv() + # inv_offset = inv_quat.apply(point) + return quat_apply(quat_inv(robot_quat), point) + + +def is_object_placed( + object_position: torch.Tensor, + object_projected_size: torch.Tensor, + container_position: torch.Tensor, + container_projected_size: torch.Tensor, + tolerance: float = 0.015, +) -> torch.Tensor: + # Horizonal + container_relative_size = container_projected_size / 2 + tolerance + container_axis_lengths = torch.norm(container_relative_size, dim=2) + container_axis_dirs = container_relative_size / container_axis_lengths.unsqueeze(2) + + object_container_rela = object_position - container_position + object_container_projections = torch.matmul( + container_axis_dirs, object_container_rela.unsqueeze(-1) + ).squeeze(-1) + + object_container_projections_xy = object_container_projections[:, :2] + container_axis_lengths_xy = container_axis_lengths[:, :2] + is_horizonal_in_container = torch.all( + torch.abs(object_container_projections_xy) <= container_axis_lengths_xy, dim=1 + ) + + # Vertical + object_relative_size = object_projected_size / 2 + object_lowest_z = object_position[:, 2] - torch.sum( + torch.abs(object_relative_size[:, :, 2]), dim=1 + ) + container_highest_z = container_position[:, 2] + torch.sum( + torch.abs(container_relative_size[:, :, 2]), dim=1 + ) + is_vertical_in_container = object_lowest_z <= container_highest_z + + return torch.logical_and(is_horizonal_in_container, is_vertical_in_container) + + +def get_object_tags(object_type, object_states, robot_pose, skip_tags, tag_thresholds): + robot_quat_xyzw = np.roll(robot_pose["quat"].astype(np.float32), -1) + _get_relative_pos = lambda point: R.from_quat(robot_quat_xyzw).apply( + point - robot_pose["pos"], inverse=True + ) + TAG_FUNCTIONS = { + "HEIGHT": lambda x: x["pos"][2], + "AREA": lambda x: x["size"][0] * x["size"][1], + "VOLUME": lambda x: np.prod(x["size"]), + "POSITION_FROM_LEFT": lambda x: _get_relative_pos(x["pos"])[1], + "POSITION_FROM_BOTTOM": lambda x: -_get_relative_pos(x["pos"])[0], + "DISTANCE_FROM_ROBOT": lambda x: -np.linalg.norm(x["pos"] - robot_pose["pos"]), + } + assert object_type in [ + "objects", + "containers", + ], f"Unknown object type: {object_type}" + + if len(object_states) > 1: + for tag, func in TAG_FUNCTIONS.items(): + if skip_tags is None or tag not in skip_tags: + object_states = _get_state_tag( + object_type, object_states, tag, func, tag_thresholds + ) + # Generate additional direction tags + if skip_tags is None or "VELOCITY" not in skip_tags: + object_states = _get_direction_tags( + object_type, object_states, robot_quat_xyzw + ) + object_states = _get_velocity_tags( + object_type, object_states, tag_thresholds + ) + # Remove duplicate tags (causing confusion in instruction generation) + return _get_unique_tags([os["tags"] for os in object_states]) + + +def _get_state_tag(object_type, object_states, tag_name, tag_func, tag_thresholds): + RANK_TAGS = { + "FIRST": { + "HEIGHT": "the tallest %s", + "AREA": "the %s with the largest area", + "VOLUME": "the %s with the largest volume", + "POSITION_FROM_LEFT": "the %s that is closest to the robot's left at the start", + "POSITION_FROM_BOTTOM": "the %s closest to the robot mounting edge at the start", + "DISTANCE_FROM_ROBOT": "the %s closest to the robot at the start", + }, + "LAST": { + "HEIGHT": "the shortest %s", + "AREA": "the %s with the smallest area", + "VOLUME": "the %s with the smallest volume", + "POSITION_FROM_LEFT": "the %s that is closest to the robot's right at the start", + "POSITION_FROM_BOTTOM": "the %s farthest from the robot mounting edge at the start", + "DISTANCE_FROM_ROBOT": "the %s farthest from the robot at the start", + }, + "MEDIUM": { + "HEIGHT": "the %s of medium height", + "AREA": "the %s with medium area", + "VOLUME": "the %s with medium volume", + "POSITION_FROM_LEFT": "the %s in the middle from left to right at the start", + "POSITION_FROM_BOTTOM": "the %s with medium distance to the robot mounting edge at the start", + "DISTANCE_FROM_ROBOT": "the %s with medium distance to the robot at the start", + }, + } + + n = len(object_states) + sorted_states = sorted(object_states, key=tag_func, reverse=True) + last_value = tag_func(sorted_states[-1]) + cur_rank = 1 + for i, state in enumerate(sorted_states): + object_name = ( + object_type.rstrip("s") if object_type == "objects" else state["category"] + ) + cur_value = tag_func(state) + if abs(cur_value - last_value) > tag_thresholds.get(tag_name.lower()): + cur_rank = i + 1 + if cur_rank == 1: + state["tags"].append(RANK_TAGS["FIRST"][tag_name] % object_name) + elif cur_rank == n: + state["tags"].append(RANK_TAGS["LAST"][tag_name] % object_name) + elif cur_rank == 2 and n == 3: + state["tags"].append(RANK_TAGS["MEDIUM"][tag_name] % object_name) + + if n == 2: # e.g., "tallest" -> "taller" + state["tags"][-1] = state["tags"][-1].replace("est", "er") + + last_value = cur_value + + return object_states + + +def _get_velocity_tags(object_type, object_states, tag_thresholds): + RANK_TAGS = { + "FIRST": "the moving %s with the highest initial velocity", + "LAST": "the moving %s with the lowest initial velocity", + "MEDIUM": "the moving %s with medium initial velocity", + } + tag_func = lambda x: (np.linalg.norm(x["lin_vel"]) if "lin_vel" in x else 0) + + sorted_states = sorted( + [obj for obj in object_states if tag_func(obj) >= 0.01], + key=tag_func, + reverse=True, + ) + n = len(sorted_states) + if n > 0: + last_value = 0.01 - tag_thresholds.get("velocity") + cur_rank = 1 + for i, state in enumerate(sorted_states): + object_name = ( + object_type.rstrip("s") + if object_type == "objects" + else state["category"] + ) + cur_value = tag_func(state) + if abs(cur_value - last_value) > tag_thresholds.get("velocity"): + cur_rank = i + 1 + if cur_rank == 1: + state["tags"].append(RANK_TAGS["FIRST"] % object_name) + elif cur_rank == n: + state["tags"].append(RANK_TAGS["LAST"] % object_name) + elif cur_rank == 2 and n == 3: + state["tags"].append(RANK_TAGS["MEDIUM"] % object_name) + + if n == 2: # e.g., "tallest" -> "taller" + state["tags"][-1] = state["tags"][-1].replace("est", "er") + + last_value = cur_value + + return object_states + + +def _get_direction_tags(object_type, object_states, robot_quat): + DIRECTION_TAGS = [ + "the %s moving in the robot's forward direction", + "the %s moving in the robot's forward-left direction", + "the %s moving in the robot's left direction", + "the %s moving in the robot's backward-left direction", + "the %s moving in the robot's backward direction", + "the %s moving in the robot's backward-right direction", + "the %s moving in the robot's right direction", + "the %s moving in the robot's forward-right direction", + ] + for state in object_states: + object_name = ( + object_type.rstrip("s") if object_type == "objects" else state["category"] + ) + if "lin_vel" not in state or np.linalg.norm(state["lin_vel"]) < 0.01: + state["tags"].append("stationary %s" % object_name) + continue + + idx = get_direction_index(state["lin_vel"], robot_quat) + state["tags"].append(DIRECTION_TAGS[idx] % object_name) + + return object_states + + +def get_direction_index(linear_velocity, robot_quat=None, inverse=True): + if robot_quat is not None: + linear_velocity = R.from_quat(robot_quat).apply( + linear_velocity, inverse=inverse + ) + + angle = math.degrees(math.atan2(linear_velocity[1], linear_velocity[0])) % 360 + # idx = int((angle + 22.5) // 45) % 8 # Old version + # front: [345°, 360) U [0°, 15°); back: [165°, 195°); left: [75°, 105°); + # right: [255, 285°) + if angle >= 345 or angle < 15: + idx = 0 # front (20°) + elif angle >= 15 and angle < 75: + idx = 1 # front-left + elif angle >= 75 and angle < 105: + idx = 2 # left (20°) + elif angle >= 105 and angle < 165: + idx = 3 # back-left + elif angle >= 165 and angle < 195: + idx = 4 # back (20°) + elif angle >= 195 and angle < 255: + idx = 5 # back-right + elif angle >= 255 and angle < 285: + idx = 6 # right (20°) + elif angle >= 285 and angle < 345: + idx = 7 # front-right + + return idx + + +def _get_unique_tags(object_tags): + assert isinstance(object_tags, list) + if len(object_tags) == 0: + return [] + + target_tags = set(object_tags[0]) + other_tags = set(tag for obj in object_tags[1:] for tag in obj) + return list(target_tags - other_tags) diff --git a/reference/utils/instruction_generator.py b/reference/utils/instruction_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..42aa2af9ff1fd1b0719e83c861eff79cf9e0c579 --- /dev/null +++ b/reference/utils/instruction_generator.py @@ -0,0 +1,38 @@ +# -*- coding: utf-8 -*- +# +# @File: instruction_generator.py +# @Author: Haozhe Xie +# @Date: 2025-05-31 19:42:51 +# @Last Modified by: Haozhe Xie +# @Last Modified at: 2026-01-11 20:40:43 +# @Email: root@haozhexie.com + +import json +import random + + +class InstructionGenerator: + @staticmethod + def generate_instruction(inst_metadata): + if isinstance(inst_metadata, str): + inst_metadata = json.loads(inst_metadata) + + tmpl = InstructionGenerator._get_instruction_template(inst_metadata["task"]) + object_desc = random.choice(inst_metadata.get("objects", [""])) + container_desc = random.choice(inst_metadata.get("containers", [""])) + return tmpl.format_map({"object": object_desc, "container": container_desc}) + + @staticmethod + def _get_instruction_template(task): + pick_action = random.choice( + ["pick up", "grasp", "catch", "grab", "get hold of"] + ) + place_action = random.choice( + ["place on", "put on", "set on", "position on", "return to", "deposit in"] + ) + if task == "pick": + return f"{pick_action} the {{object}}." + elif task in ["place", "long-horizon"]: + return f"{pick_action} the {{object}} and {place_action} the {{container}}." + else: + raise ValueError(f"Unknown task: {task}") diff --git a/scripts/build_examples.py b/scripts/build_examples.py new file mode 100644 index 0000000000000000000000000000000000000000..da67591217d3714311c1e0daf9db5288063ef615 --- /dev/null +++ b/scripts/build_examples.py @@ -0,0 +1,268 @@ +"""Selective official benchmark examples. Run: prepare, download, render, package, publish. +The same implementation supports ReflexBench and DynamicVLA using the workspace name. +""" +from pathlib import Path +import ast, collections, concurrent.futures as cf, hashlib, io, json, math, shutil, subprocess, sys, time +from fractions import Fraction +import requests +from requests.adapters import HTTPAdapter +from urllib3.util.retry import Retry +import pyarrow as pa +import pyarrow.parquet as pq + +ROOT=Path(__file__).resolve().parents[1] +NAME=ROOT.name +REFLEX=NAME=='ReflexBench' +REPO='cyx337/ReflexBench_dataset' if REFLEX else 'hzxie/DOM' +DEST=f'Travor278/{NAME}-Task-Examples' +WORK=ROOT/'work';OUT=ROOT/'dataset' +CODE=WORK/'official-code' +GITHUB='LxRoboticsLab/ReflexBench' if REFLEX else 'hzxie/DynamicVLA' +SESSION=requests.Session() +SESSION.mount('https://',HTTPAdapter(max_retries=Retry(total=4,backoff_factor=.6,status_forcelist=[429,500,502,503,504]))) + +def read(p):return json.loads(p.read_text(encoding='utf-8')) +def write(p,x):p.parent.mkdir(parents=True,exist_ok=True);p.write_text(json.dumps(x,indent=2,ensure_ascii=False),encoding='utf-8') +def url(p):return f'https://huggingface.co/datasets/{REPO}/resolve/{SOURCE_REV}/{p}' +def download_file(p,dst): + if dst.exists():return + dst.parent.mkdir(parents=True,exist_ok=True) + with SESSION.get(url(p),stream=True,timeout=(20,120)) as r: + r.raise_for_status() + with dst.with_suffix(dst.suffix+'.part').open('wb') as f: + for b in r.iter_content(1024*1024):f.write(b) + dst.with_suffix(dst.suffix+'.part').replace(dst) + +class RangeReader(io.RawIOBase): + """Seekable HTTP range reader: no full video-shard download or hidden HF cache.""" + def __init__(self,path,size,block=262144): + self.path=path;self.size=size;self.block=block;self.pos=0;self.cache=collections.OrderedDict();self.transferred=0 + def readable(self):return True + def seekable(self):return True + def tell(self):return self.pos + def seek(self,offset,whence=0): + self.pos=offset if whence==0 else self.pos+offset if whence==1 else self.size+offset + if self.pos<0:raise ValueError('negative seek') + return self.pos + def read(self,n=-1): + if n<0:n=self.size-self.pos + n=min(n,self.size-self.pos);out=[] + while n>0: + index=self.pos//self.block + if index not in self.cache: + start=index*self.block;end=min(start+self.block,self.size)-1 + r=SESSION.get(url(self.path),headers={'Range':f'bytes={start}-{end}'},timeout=(20,90));r.raise_for_status() + if r.status_code!=206 or not r.headers.get('Content-Range','').startswith(f'bytes {start}-'): + raise ValueError('Source did not honor bounded HTTP range') + self.cache[index]=r.content;self.transferred+=len(r.content) + if len(self.cache)>24:self.cache.popitem(last=False) + b=self.cache[index];offset=self.pos%self.block;amount=min(n,len(b)-offset) + if amount<=0:raise EOFError('truncated HTTP range') + out.append(b[offset:offset+amount]);self.pos+=amount;n-=amount + return b''.join(out) + +def lookup_jsonl(path,target,field,size): + """Locate a sorted metadata record using small byte ranges, retaining only matched rows.""" + low=0;high=size;low_id=0;high_id=(207306 if field=='episode_index' else 140932) + for attempt in range(22): + guess=int(low+(high-low)*max(0,min(1,(target-low_id)/max(1,high_id-low_id)))) + start=max(0,min(size-131072,guess-32768));end=min(size-1,start+131071) + r=SESSION.get(url(path),headers={'Range':f'bytes={start}-{end}'},timeout=60);r.raise_for_status();assert r.status_code==206 + raw=r.content;position=start;records=[] + for line in raw.splitlines(keepends=True): + try: + row=json.loads(line);records.append((row[field],position,row)) + except (ValueError,KeyError):pass + position+=len(line) + assert records + for index,pos,row in records: + if index==target:return row + if targetrecords[-1][0]:low=records[-1][1];low_id=records[-1][0] + else:raise ValueError(f'Missing official metadata record {field}={target}') + raise ValueError(f'Lookup failed: {path} {target}') + +def keep_source(path): + dst=OUT/'reference'/path;dst.parent.mkdir(parents=True,exist_ok=True);shutil.copy2(CODE/path,dst) + +def prepare(): + from huggingface_hub import HfApi + api=HfApi();info=api.dataset_info(REPO);global SOURCE_REV;SOURCE_REV=info.sha + code_rev=subprocess.check_output(['git','-C',str(CODE),'rev-parse','HEAD'],text=True).strip() + tasks=[];samples=[] + if REFLEX: + table=pq.read_table(WORK/'episodes-file-000.parquet') + columns=[c for c in table.column_names if not c.startswith('stats/')] + eps=table.select(columns).to_pylist();official=pq.read_table(WORK/'tasks.parquet').to_pylist() + keys=['ball_catching','conveyor_belt_pick_and_place','whack_a_mole','rolling_ball_interception','ball_throwing','rotating_peg_insertion'] + descriptions=['Catch a launched ball with a held container.','Pick from the moving conveyor and release into the bin.','Strike the active mole using the closed gripper during its popup window.','Intercept the ball or orange rolling down the ramp.','Throw the pre-grasped ball into the target bin.','Time peg insertion into the moving hole of a rotating disc.'] + rules=[ + 'Episode success is task_phase == 4. The ball must remain in the virtual catch zone below the end effector for the configured consecutive-step threshold.', + 'Success requires the object inside the bin in environment-local coordinates: x in [-0.2,0.2], y in [-0.6,-0.3], z in [0.0,0.08] m.', + 'Unified evaluation success is valid_hits > 0. A valid hit requires proximity to the active mole, closed gripper and the configured dwell. Current eval profile uses 2.0 s popup, 0.3 s gap, 0 s initial delay and 3.0 s evaluation length. Completing the window schedule alone is not success.', + 'Episode success is task_phase == 4. The rolling object must stay in the virtual catch zone for the configured hold threshold.', + 'Success is task_phase == 2, meaning the ball entered the target bin. Documentation bounds relative to bin center: |x-cx| <= 0.13, |y-cy| <= 0.09, z in [0.02,0.18] m.', + 'Success is task_phase == 4. Peg tip enters the hole within its radius tolerance, descends at least 0.02 m below the disc surface, and holds for at least 5 control steps (task README).'] + profiles_source=(CODE/'scripts/evaluation/task_profiles.py').read_text(encoding='utf-8') + profiles=next(ast.literal_eval(n.value) for n in ast.parse(profiles_source).body if isinstance(n,ast.AnnAssign) and getattr(n.target,'id','')=='TASK_PROFILES') + for item,key,desc,rule in zip(official,keys,descriptions,rules): + index=item['task_index'];local=[e for e in eps if e['tasks']==[item['task']]] + relative=f'source/reflexbench/reflexbench/tasks/manager_based/{key}/README.md' + task=dict(task_index=index,key=key,name=key.replace('_',' ').title(),category='Reaction-critical',instruction=item['task'],description=desc,success_rule=rule,profile=profiles[key],official_gym_id=profiles[key]['gym_id'],doc_url=f'https://github.com/{GITHUB}/blob/{code_rev}/{relative}',published_episode_count=len(local)) + tasks.append(task);keep_source(relative) + for j in [0,len(local)//2,len(local)-1]: + ep=local[j];eid=ep['episode_index'];views=[] + for camera,label in [('fixed_cam','FIXED CAMERA'),('wrist_cam','WRIST CAMERA')]: + prefix=f'videos/observation.images.{camera}' + path=f"{prefix}/chunk-{ep[prefix+'/chunk_index']:03d}/file-{ep[prefix+'/file_index']:03d}.mp4" + obj=api.get_paths_info(REPO,[path],repo_type='dataset',revision=SOURCE_REV)[0] + views.append(dict(camera=camera,label=label,path=f'media/{key}/episode_{eid:06d}/{camera}.mp4',source_path=path,source_bytes=obj.size,from_timestamp=ep[prefix+'/from_timestamp'],to_timestamp=ep[prefix+'/to_timestamp'])) + samples.append(dict(task_index=index,task_key=key,episode_index=eid,frame_count=ep['length'],fps=25,instruction=item['task'],metadata=ep,views=views,trajectory_path=f'trajectories/{key}/episode_{eid:06d}.parquet',composite_path=f'media/{key}/episode_{eid:06d}/synchronized.mp4',preview_path=f'media/{key}/episode_{eid:06d}/preview.jpg')) + for f in ['scripts/evaluation/task_profiles.py','scripts/data_collection/task_prompts.py']:keep_source(f) + shutil.copy2(WORK/'tasks.parquet',OUT/'provenance'/'official_tasks.parquet') + note='Six official task_index values 0–5. Single Franka arm; only fixed_cam and wrist_cam exist. Do not relabel the wrist as left/right. Success rules describe evaluation, not an unprovided per-demo score.' + else: + # Metadata-only range sampling. Two episodes from each distant window. + windows={'long_horizon':['episode-window-0.json'], 'pick':['episode-window-0.08.json','episode-window-0.3.json','episode-window-0.42.json'],'place':['episode-window-0.54.json','episode-window-0.78.json','episode-window-0.999.json']} + selected=[] + for kind,files in windows.items(): + for filename in files: + xs=read(WORK/filename) + positions=[0,200,400,600,800,1000] if kind=='long_horizon' else [50,len(xs)-51] + selected.extend(xs[i] for i in positions) + for ep in selected: + eid=ep['episode_index'];path=f'data/chunk-{eid//1000:03d}/episode_{eid:06d}.parquet';local=WORK/f'episode_{eid:06d}.parquet';download_file(path,local);table=pq.read_table(local);ids=set(table['task_index'].to_pylist());assert len(ids)==1;index=ids.pop();assert set(table['episode_index'].to_pylist())=={eid};assert table.num_rows==ep['length'] + task_meta=lookup_jsonl('meta/tasks.jsonl',index,'task_index',63111962);assert task_meta['task']==ep['tasks'];meta=json.loads(ep['tasks']);kind=meta['task'];cam=lookup_jsonl('meta/camera.jsonl',eid,'episode_index',173353120) + obj=meta['objects'][0];container=meta.get('containers',[''])[0] + instruction=f'Pick up the {obj}.' if kind=='pick' else f'Pick up the {obj} and place it in/on the {container}.' + # This deterministic sentence is a readable rendering, not a recorded literal prompt. + rule='Success requires object-to-end-effector distance < tolerance and end-effector-to-configured-goal distance < tolerance. The current default tolerance is 0.015 m; per-scene overrides may differ.' if kind=='pick' else 'Success requires ALL target objects to satisfy the container-relative geometric placement check, and the end effector to return within tolerance of the configured goal. Current default return tolerance is 0.015 m; per-scene overrides may differ.' + key=f'task_{index:06d}' + task=dict(task_index=index,key=key,name=f'{kind.replace("_"," ").title()} · {obj}',category={'pick':'Pick','place':'Place','long_horizon':'Long-Horizon'}[kind],instruction=instruction,instruction_note='Readable deterministic rendering of structured official instruction metadata; the exact generated natural-language variant is not stored.',instruction_metadata=meta,description=f'Official DOM simulation scenario {cam["filename"]}. Object/container aliases are preserved in the metadata.',success_rule=rule,doc_url=f'https://github.com/{GITHUB}/blob/{code_rev}/simulations/configs/termination_cfg.py',source_filename=cam['filename'],published_episode_count=None) + if index not in {t['task_index'] for t in tasks}:tasks.append(task) + views=[dict(camera=c,label=label,path=f'media/{key}/episode_{eid:06d}/{c}.mp4',source_path=f'videos/chunk-{eid//1000:03d}/observation.images.{c}/episode_{eid:06d}.mp4') for c,label in [('opst_cam','OPPOSITE CAMERA'),('wrist_cam','WRIST CAMERA'),('side_cam','SIDE CAMERA')]] + samples.append(dict(task_index=index,task_key=key,episode_index=eid,frame_count=ep['length'],fps=25,instruction=instruction,instruction_metadata=meta,official_task_record=task_meta,camera_metadata=cam,source_filename=cam['filename'],views=views,trajectory_source_path=path,trajectory_path=f'trajectories/{key}/episode_{eid:06d}.parquet',composite_path=f'media/{key}/episode_{eid:06d}/synchronized.mp4',preview_path=f'media/{key}/episode_{eid:06d}/preview.jpg')) + print(f'Mapped {kind} task_index={index} episode_index={eid} ({cam["filename"]})',flush=True) + for f in ['simulations/configs/termination_cfg.py','utils/instruction_generator.py','simulations/helpers.py','simulations/configs/sim_cfg.yaml']:keep_source(f) + note='DOM has 3 task families, not 3 task_index values: 140,932 structured instruction IDs and 207,306 episodes. This subset selects 6 demonstrations per family (18 total), preserving actual task_index/episode_index and structured instructions. All 3 published cameras are single-arm cameras: opposite, wrist, side, not top/left/right arms.' + keep_source('LICENSE');keep_source('README.md') + shutil.copy2(CODE/'LICENSE',OUT/'LICENSE') + write(OUT/'provenance/tasks.json',tasks);write(OUT/'provenance/samples.json',samples) + source=dict(benchmark=NAME,repo_id=REPO,revision=SOURCE_REV,code_repository=GITHUB,code_revision=code_rev,note=note,date='2026-10-03',video_policy='Complete selected episodes, converted to browser-friendly H.264 MP4 at original 25 FPS. No generated simulation rollouts.',actual_episode_score='Not published in released LeRobot features; null, not assumed successful.',episode_selection='First/middle/last episode per official task' if REFLEX else 'Two episodes from three spaced metadata windows for pick/place, six spaced long-horizon scenarios.',camera_order=[v['camera'] for v in samples[0]['views']]) + write(OUT/'provenance/source.json',source);shutil.copy2(WORK/'info.json',OUT/'provenance/official_info.json');print(note,flush=True) + +def encode_frames(frames,path): + import av + path.parent.mkdir(parents=True,exist_ok=True) + with av.open(str(path),'w') as output: + stream=None;count=0 + for frame in frames: + if stream is None: + stream=output.add_stream('libx264',rate=25);stream.width=frame.width;stream.height=frame.height;stream.pix_fmt='yuv420p';stream.options={'crf':'18','preset':'fast'} + frame=frame.reformat(format='yuv420p');frame.pts=count;frame.time_base=Fraction(1,25) + for packet in stream.encode(frame):output.mux(packet) + count+=1 + if stream is None:raise ValueError('No frames extracted') + for packet in stream.encode():output.mux(packet) + return count + +def download(): + import av + source=read(OUT/'provenance/source.json');samples=read(OUT/'provenance/samples.json');summary=[] + if REFLEX: + path='data/chunk-000/file-000.parquet';download_file(path,WORK/'trajectories.parquet');data=pq.read_table(WORK/'trajectories.parquet') + for s in samples: + table=data.filter(pa.compute.equal(data['episode_index'],s['episode_index']));assert table.num_rows==s['frame_count'];assert set(table['task_index'].to_pylist())=={s['task_index']};target=OUT/s['trajectory_path'];target.parent.mkdir(parents=True,exist_ok=True);pq.write_table(table,target) + for view_index in range(2): + model=samples[0]['views'][view_index];reader=RangeReader(model['source_path'],model['source_bytes']);container=av.open(reader);stream=container.streams.video[0] + for s in sorted(samples,key=lambda s:s['episode_index']): + view=s['views'][view_index];target=OUT/view['path'] + if target.exists():continue + start=view['from_timestamp'];container.seek(int(start/stream.time_base),stream=stream,backward=True) + def frames(): + found=0 + for f in container.decode(stream): + if f.time+0.00001=s['frame_count']:break + yield f;found+=1 + assert found==s['frame_count'],(s['episode_index'],found,s['frame_count']) + n=encode_frames(frames(),target);view['output_sha256']=hashlib.sha256(target.read_bytes()).hexdigest();print('Extracted',view['camera'],s['episode_index'],n,'frames',flush=True) + container.close();summary.append({'source_path':model['source_path'],'source_shard_size':reader.size,'range_bytes_received':reader.transferred}) + else: + def run(s): + target=OUT/s['trajectory_path'];target.parent.mkdir(parents=True,exist_ok=True);shutil.copy2(WORK/f"episode_{s['episode_index']:06d}.parquet",target) + for v in s['views']: + original=WORK/f"{s['episode_index']:06d}-{v['camera']}.mp4";download_file(v['source_path'],original) + dst=OUT/v['path'];dst.parent.mkdir(parents=True,exist_ok=True) + if not dst.exists(): + subprocess.run(['ffmpeg','-v','error','-y','-i',str(original),'-c:v','libx264','-crf','18','-preset','fast','-pix_fmt','yuv420p','-movflags','+faststart',str(dst)],check=True,capture_output=True) + v['source_sha256']=hashlib.sha256(original.read_bytes()).hexdigest();v['source_bytes']=original.stat().st_size;v['output_sha256']=hashlib.sha256(dst.read_bytes()).hexdigest() + print('Downloaded/transcoded',s['episode_index'],flush=True) + with cf.ThreadPoolExecutor(max_workers=4) as ex:list(ex.map(run,samples)) + summary=[dict(source_video_files=sum(len(s['views']) for s in samples),source_video_bytes=sum(v['source_bytes'] for s in samples for v in s['views']))] + write(OUT/'provenance/samples.json',samples);write(OUT/'provenance/download_report.json',dict(details=summary,whole_repo_downloaded=False,assets_downloaded=False,checkpoints_downloaded=False)) + +def render(): + from PIL import Image,ImageDraw + samples=read(OUT/'provenance/samples.json');n=len(samples[0]['views']);labels=Image.new('RGB',(320*n,28),'#14251e');draw=ImageDraw.Draw(labels) + for i,v in enumerate(samples[0]['views']):draw.text((i*320+10,8),v['label'],fill='white') + label_path=WORK/'labels.png';labels.save(label_path) + def run(s): + target=OUT/s['composite_path'];target.parent.mkdir(parents=True,exist_ok=True) + if not target.exists(): + height=320 if REFLEX else 240;cmd=['ffmpeg','-v','error','-y'] + for v in s['views']:cmd+=['-i',str(OUT/v['path'])] + cmd+=['-loop','1','-i',str(label_path)] + filters=';'.join(f'[{i}:v]scale=320:{height},setsar=1[c{i}]' for i in range(n))+f';'+''.join(f'[c{i}]' for i in range(n))+f'hstack=inputs={n}[v];[{n}:v]format=yuv420p[l];[l][v]vstack=inputs=2:shortest=1[out]' + cmd+=['-filter_complex',filters,'-map','[out]','-c:v','libx264','-crf','21','-preset','fast','-pix_fmt','yuv420p','-frames:v',str(s['frame_count']),'-r','25','-movflags','+faststart',str(target)];subprocess.run(cmd,check=True,capture_output=True) + subprocess.run(['ffmpeg','-v','error','-y','-i',str(target),'-frames:v','1','-q:v','2',str(OUT/s['preview_path'])],check=True,capture_output=True) + with cf.ThreadPoolExecutor(max_workers=3) as ex:list(ex.map(run,samples)) + checks=[] + for s in samples: + table=pq.read_table(OUT/s['trajectory_path']);assert table.num_rows==s['frame_count'];assert set(table['task_index'].to_pylist())=={s['task_index']} + frames=[] + for path in [v['path'] for v in s['views']]+[s['composite_path']]: + r=json.loads(subprocess.check_output(['ffprobe','-v','error','-select_streams','v:0','-show_entries','stream=codec_name,width,height,nb_frames,duration','-of','json',str(OUT/path)],text=True))['streams'][0] + assert r['codec_name']=='h264' and int(r['nb_frames'])==s['frame_count'],(path,r,s['frame_count']);frames.append(dict(path=path,**r)) + checks.append(dict(task_index=s['task_index'],episode_index=s['episode_index'],videos=frames)) + write(OUT/'provenance/validation.json',dict(passed=True,episodes=len(samples),checks=checks));print('Verified full frame counts, task IDs and H.264 MP4:',len(samples),'episodes',flush=True) + +def package(): + import yaml + from datasets import Dataset,Features,Value,Video,Image + tasks=read(OUT/'provenance/tasks.json');samples=read(OUT/'provenance/samples.json');source=read(OUT/'provenance/source.json');rows=[] + for s in samples: + t=next(t for t in tasks if t['task_index']==s['task_index']) + row=dict(task_index=s['task_index'],task_name=t['name'],category=t['category'],episode_index=s['episode_index'],synchronized= {'path':f'hf://datasets/{DEST}@main/'+s['composite_path'],'bytes':None}) + for v in s['views']:row[v['camera']]={'path':f'hf://datasets/{DEST}@main/'+v['path'],'bytes':None} + row.update(instruction=s['instruction'],instruction_metadata=json.dumps(s.get('instruction_metadata'),ensure_ascii=False),success_rule=t['success_rule'],actual_episode_success=None,frame_count=s['frame_count'],fps=25,duration_seconds=s['frame_count']/25,source_repo=REPO,source_revision=source['revision'],trajectory_url=f'https://huggingface.co/datasets/{DEST}/resolve/main/'+s['trajectory_path'],official_doc=t['doc_url'],source_filename=s.get('source_filename',''),camera_metadata=json.dumps(s.get('camera_metadata'),ensure_ascii=False)) + rows.append(row) + video_columns=['synchronized']+source['camera_order'];features=Features({k:Video(decode=False) if k in video_columns else Value('int64') if k in ['task_index','episode_index','frame_count','fps'] else Value('float64') if k=='duration_seconds' else Value('bool') if k=='actual_episode_success' else Value('string') for k in rows[0]}) + ds=Dataset.from_list(rows,features=features);(OUT/'data').mkdir(exist_ok=True);configs=[] + def subset(name,indices,default=False): + p=f'data/{name}.parquet';ds.select(indices).to_parquet(str(OUT/p),batch_size=32);entry=dict(config_name=name,data_files=[dict(split='examples',path=p)]) + if default:entry['default']=True + configs.append(entry) + subset('all',list(range(len(rows))),True) + for t in tasks:subset(f"task_{t['task_index']:06d}",[i for i,r in enumerate(rows) if r['task_index']==t['task_index']]) + for category in sorted({t['category'] for t in tasks}):subset(category.lower().replace('-','_'),[i for i,r in enumerate(rows) if r['category']==category]) + header=dict(language=['en','zh'],license='bsd-3-clause' if REFLEX else 'other',pretty_name=NAME+' Simulation Task Examples',configs=configs,tags=['robotics','video','simulation']) + if not REFLEX:header.update(license_name='slab-license',license_link='LICENSE') + browser=f'https://travor278-{NAME.lower()}-task-examples.static.hf.space' + lines=['---',yaml.safe_dump(header,sort_keys=False,allow_unicode=True).rstrip(),'---',f'# {NAME} Simulation Task Examples',f'\n[打开 MP4 任务浏览页]({browser}/?task={tasks[0]["task_index"]})', '\n'+source['note'], + '\n所有示例是完整 H.264 MP4。单臂相机保留官方名称：'+ ' / '.join(source['camera_order'])+'。每条有同步合并视频、各路原视频与官方动作/状态 Parquet。', + '\n评估标准根据官方代码和文档整理；发布数据未保存逐条示教 success 标签，故 actual_episode_success 为 null。本文不编造 0–100 得分。', + '\n| 官方 task_index | Task / instruction scenario | 分类 | 直接浏览 |','|---|---|---|---|'] + for t in sorted(tasks,key=lambda t:t['task_index']):lines.append(f"| {t['task_index']} | {t['name']} | {t['category']} | [打开]({browser}/?task={t['task_index']}) |") + for t in tasks: + lines.extend([f"\n## task_index={t['task_index']} · {t['name']}",f"\n**Instruction:** {t['instruction']}",f"\n**Description:** {t['description']}",f"\n**Success / evaluation:** {t['success_rule']}",f"\n[官方说明]({t['doc_url']})"]) + if t.get('instruction_metadata'):lines.extend(['\n结构化原始 instruction（完整别名）：','\n```json',json.dumps(t['instruction_metadata'],ensure_ascii=False,indent=2),'```','\n'+t['instruction_note']]) + if t.get('official_gym_id'):lines.append(f"\n**Gym ID:** `{t['official_gym_id']}`") + for s in [s for s in samples if s['task_index']==t['task_index']]:lines.extend([f"\n**episode_index={s['episode_index']}** · {s['frame_count']} frames / {s['frame_count']/25:.2f} s",f"\n![Camera preview]({s['preview_path']})",f"\n[同步 MP4]({s['composite_path']}) · "+' · '.join(f"[{v['camera']} MP4]({v['path']})" for v in s['views'])+f" · [官方轨迹]({s['trajectory_path']})"]) + lines.extend(['\n## 来源、许可与复现',f'\n官方数据：[原仓库](https://huggingface.co/datasets/{REPO}) · 固定 revision `{source["revision"]}`。',f'\n官方代码：[GitHub](https://github.com/{GITHUB}) · revision `{source["code_revision"]}`。','\n保留上游 LICENSE 和来源文件在 `reference/`。ReflexBench 官方数据未单列许可证元数据；保留官方实现 BSD-3-Clause 来源，不扩大作者权利。' if REFLEX else '\n遵循上游 S-Lab License 1.0，仅允许非商业用途；见 LICENSE。','\n选择性提取记录、原始时间区间、task/episode 对应、相机元数据和校验均在 `provenance/`。不下载场景资产、模型权重或完整数据仓库。','\n复现脚本：`scripts/build_examples.py`。浏览页源码另见 `browser/` 和 `scripts/build_browser.py`。']) + (OUT/'README.md').write_text('\n'.join(lines)+'\n',encoding='utf-8');(OUT/'scripts').mkdir(exist_ok=True);shutil.copy2(__file__,OUT/'scripts/build_examples.py');print('Packaged',len(rows),'rows,',len(configs),'native HF subsets',flush=True) + +if __name__=='__main__': + WORK.mkdir(exist_ok=True);(OUT/'provenance').mkdir(parents=True,exist_ok=True) + if (OUT/'provenance/source.json').exists():SOURCE_REV=read(OUT/'provenance/source.json')['revision'] + globals()[sys.argv[1]]() diff --git a/trajectories/task_000000/episode_000000.parquet b/trajectories/task_000000/episode_000000.parquet new file mode 100644 index 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