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  1. Surgical/utenn/surgical_video_datasets/README.md +163 -0
  2. Surgical/utenn/surgical_video_datasets/meta/episode_stats.jsonl +73 -0
  3. Surgical/utenn/surgical_video_datasets/meta/episodes.jsonl +73 -0
  4. Surgical/utenn/surgical_video_datasets/meta/info.json +137 -0
  5. Surgical/utenn/surgical_video_datasets/meta/tasks.jsonl +7 -0
  6. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/README.md +293 -0
  7. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/episodes.jsonl +0 -0
  8. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/episodes_stats.jsonl +101 -0
  9. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/info.json +139 -0
  10. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000001.json +47 -0
  11. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000003.json +78 -0
  12. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000006.json +78 -0
  13. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000008.json +78 -0
  14. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000012.json +78 -0
  15. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000014.json +47 -0
  16. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000017.json +47 -0
  17. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000018.json +78 -0
  18. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000019.json +78 -0
  19. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000020.json +78 -0
  20. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000023.json +78 -0
  21. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000025.json +47 -0
  22. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000031.json +78 -0
  23. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000032.json +78 -0
  24. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000040.json +78 -0
  25. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000042.json +47 -0
  26. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000044.json +78 -0
  27. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000048.json +78 -0
  28. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000050.json +47 -0
  29. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000052.json +78 -0
  30. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000053.json +47 -0
  31. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000057.json +78 -0
  32. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000059.json +78 -0
  33. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000061.json +47 -0
  34. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000062.json +47 -0
  35. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000063.json +47 -0
  36. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000066.json +47 -0
  37. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000072.json +78 -0
  38. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000077.json +78 -0
  39. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000082.json +109 -0
  40. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000083.json +47 -0
  41. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000085.json +78 -0
  42. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000086.json +78 -0
  43. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000087.json +78 -0
  44. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000088.json +78 -0
  45. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000089.json +78 -0
  46. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000090.json +78 -0
  47. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000092.json +78 -0
  48. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/label_id_maps/episode_000095.json +47 -0
  49. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/tasks.jsonl +7 -0
  50. Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/tool_slot_maps.jsonl +101 -0
Surgical/utenn/surgical_video_datasets/README.md ADDED
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+ <!--
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+ Open-H Embodiment Dataset README Template (v1.0)
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+ Please fill out this template and include it in the ./metadata directory of your LeRobot dataset.
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+ This file helps others understand the context and details of your contribution.
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+ -->
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+
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+ # surgical_actions_v1 - README
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+
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+ ---
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+
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+ ## 📋 At a Glance
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+
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+ *Clip-level labeled clinical endoscopic surgical video segments with RGB, monocular depth, and tool segmentation for surgical action recognition and representation learning.*
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+
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+ ---
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+
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+ ## 📖 Dataset Overview
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+
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+ This dataset contains short clinical surgical video clips packaged in the LeRobot v2.1 format. Each episode corresponds to a short endoscopic clip annotated with a single dominant surgical action label at task level. The dataset is designed to support surgical activity recognition, embodied learning from video, and pretraining of vision-based robotic policies.
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+
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+ Each episode provides synchronized RGB video, monocular depth predictions, and tool segmentation masks generated automatically from the original surgical video.
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+
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+ | | |
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+ | :--- | :--- |
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+ | **Total Trajectories** | 73 |
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+ | **Total Hours** | TBD |
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+ | **Data Type** | ☑ Clinical ☐ Ex-Vivo ☐ Table-Top Phantom ☐ Digital Simulation ☐ Physical Simulation ☐ Other |
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+ | **License** | CC BY 4.0 |
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+ | **Version** | 1.0 |
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+
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+ ---
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+
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+ ## 🎯 Tasks & Domain
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+
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+ ### Domain
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+
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+ - [x] **Surgical Robotics**
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+ - [ ] **Ultrasound Robotics**
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+ - [ ] **Other Healthcare Robotics**
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+
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+ ### Demonstrated Skills
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+
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+ The dataset demonstrates the following clip-level surgical actions:
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+
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+ - CONTACT
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+ - EXPLORATION
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+ - GRASPING
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+ - MOVE
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+ - PULLING
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+ - STATIC
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+ - ZOOM_IN
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+
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+ ---
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+
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+ ## 🔬 Data Collection Details
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+
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+ ### Collection Method
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+
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+ - [x] **Human Teleoperation / Clinical Operation**
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+ - [ ] Programmatic/State-Machine
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+ - [ ] AI Policy / Autonomous
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+ - [ ] Other
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+
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+ ### Operator Details
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+
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+ | | Description |
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+ | :--- | :--- |
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+ | **Operator Count** | Multiple clinical operators (exact count not recorded) |
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+ | **Operator Skill Level** | [x] Expert (surgeon) |
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+ | **Collection Period** | *N/A* |
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+
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+ ### Recovery Demonstrations
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+
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+ - [ ] **Yes**
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+ - [x] **No**
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+
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+ This dataset does not include explicit recovery demonstrations.
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+
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+ ---
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+
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+ ## 💡 Diversity Dimensions
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+
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+ - [x] **Background / Scene**
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+ - [x] **Task Execution**
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+ - [x] **Target Object** (different anatomical structures and tissues)
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+
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+ The dataset contains diverse surgical scenes and multiple execution styles for the same semantic action category.
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+
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+ ---
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+
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+ ## 🛠️ Equipment & Setup
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+
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+ ### Robotic Platform(s)
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+
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+ This dataset contains clinical endoscopic video only and does not include robot state or control signals.
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+
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+ - **Robot 1:** Clinical surgical robotic system (endoscopic video stream only)
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+
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+ ### Sensors & Cameras
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+
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+ | Type | Model/Details |
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+ | :--- | :--- |
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+ | **Primary Camera** | Clinical endoscopic camera (RGB endoscope video) |
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+ | **Medical Imager** | Monocular depth generated offline using VideoDepthAnything (DepthAnything v2 backbone) |
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+ | **Other** | Tool segmentation generated offline using SAM3 (Segment Anything Model v3) |
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+
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+ ---
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+
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+ ## 🎯 Action & State Space Representation
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+
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+ This dataset is intended primarily for visual surgical action understanding and representation learning. It does not include low-level robot commands.
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+
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+ ### Action Space Representation
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+
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+ **Primary Action Representation:**
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+
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+ - [x] **Other** – discrete semantic surgical action label
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+
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+ **Description**
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+
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+ `action.label`: A categorical label indicating the dominant surgical action of the clip. The same label is repeated for all frames within an episode.
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+
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+ ### State Space Representation
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+
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+ **State Information Included:**
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+
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+ - [x] **Other** – no robot state is provided
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+
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+ **Additional visual state representations:**
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+
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+ - RGB video: `observation.images.color`
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+ - Monocular depth: `observation.images.depth`
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+ - Tool segmentation masks: `observation.images.tool_segmentation`
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+
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+ ### 📋 Recommended Additional Representations
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+
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+ Not applicable.
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+ This dataset intentionally focuses on perception-only signals.
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+
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+ ---
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+
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+ ## ⏱️ Data Synchronization Approach
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+
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+ Within each episode, RGB frames, depth predictions, and tool segmentation masks are aligned by frame index.
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+ If video lengths differ across modalities, all modalities are truncated to the minimum available length to preserve temporal alignment.
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+
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+ Each frame includes a timestamp field (`timestamp_s`) expressed in seconds.
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+
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+ ---
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+
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+ ## 👥 Attribution & Contact
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+
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+ | | |
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+ | :--- | :--- |
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+ | **Dataset Lead** | Nan Xiao, Tom Olesch, Farong Wang, Fei Liu, DeLong Jonathan C. |
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+ | **Institution** | University of Tennessee, Knoxville (AURAS Lab); UT Medical Center |
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+ | **Contact Email** | nxiao4@vols.utk.edu |
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+ | **Citation (BibTeX)** | <pre><code>@misc{surgical_actions_v1_2025,<br> author = {Nan Xiao, Tom Olesch, Farong Wang, Fei Liu, DeLong Jonathan C.},<br> title = {surgical\_actions\_v1: Clip-level Surgical Action Dataset},<br> year = {2025},<br> publisher = {Open-H-Embodiment}<br>}</code></pre> |
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+
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+
Surgical/utenn/surgical_video_datasets/meta/episode_stats.jsonl ADDED
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14
+ {"episode_index": 13, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [13.0], "max": [13.0], "mean": [13.0], "std": [0.0], "count": [31]}, "index": {"min": [3268.0], "max": [3298.0], "mean": [3283.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
15
+ {"episode_index": 14, "stats": {"timestamp": {"min": [0.0], "max": [4.766666889190674], "mean": [2.383333444595337], "std": [1.3856072425842285], "count": [144]}, "frame_index": {"min": [0.0], "max": [143.0], "mean": [71.5], "std": [41.56821702535083], "count": [144]}, "episode_index": {"min": [14.0], "max": [14.0], "mean": [14.0], "std": [0.0], "count": [144]}, "index": {"min": [3299.0], "max": [3442.0], "mean": [3370.5], "std": [41.56821823120117], "count": [144]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [144]}}}
16
+ {"episode_index": 15, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [15.0], "max": [15.0], "mean": [15.0], "std": [0.0], "count": [31]}, "index": {"min": [3443.0], "max": [3473.0], "mean": [3458.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
17
+ {"episode_index": 16, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [16.0], "max": [16.0], "mean": [16.0], "std": [0.0], "count": [30]}, "index": {"min": [3474.0], "max": [3503.0], "mean": [3488.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
18
+ {"episode_index": 17, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [17.0], "max": [17.0], "mean": [17.0], "std": [0.0], "count": [31]}, "index": {"min": [3504.0], "max": [3534.0], "mean": [3519.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
19
+ {"episode_index": 18, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [18.0], "max": [18.0], "mean": [18.0], "std": [0.0], "count": [31]}, "index": {"min": [3535.0], "max": [3565.0], "mean": [3550.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
20
+ {"episode_index": 19, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [19.0], "max": [19.0], "mean": [19.0], "std": [0.0], "count": [30]}, "index": {"min": [3566.0], "max": [3595.0], "mean": [3580.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
21
+ {"episode_index": 20, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [20.0], "max": [20.0], "mean": [20.0], "std": [0.0], "count": [30]}, "index": {"min": [3596.0], "max": [3625.0], "mean": [3610.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
22
+ {"episode_index": 21, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [21.0], "max": [21.0], "mean": [21.0], "std": [0.0], "count": [30]}, "index": {"min": [3626.0], "max": [3655.0], "mean": [3640.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
23
+ {"episode_index": 22, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [22.0], "max": [22.0], "mean": [22.0], "std": [0.0], "count": [31]}, "index": {"min": [3656.0], "max": [3686.0], "mean": [3671.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
24
+ {"episode_index": 23, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [23.0], "max": [23.0], "mean": [23.0], "std": [0.0], "count": [31]}, "index": {"min": [3687.0], "max": [3717.0], "mean": [3702.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [31]}}}
25
+ {"episode_index": 24, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [24.0], "max": [24.0], "mean": [24.0], "std": [0.0], "count": [30]}, "index": {"min": [3718.0], "max": [3747.0], "mean": [3732.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
26
+ {"episode_index": 25, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [25.0], "max": [25.0], "mean": [25.0], "std": [0.0], "count": [30]}, "index": {"min": [3748.0], "max": [3777.0], "mean": [3762.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [3.0], "max": [3.0], "mean": [3.0], "std": [0.0], "count": [30]}}}
27
+ {"episode_index": 26, "stats": {"timestamp": {"min": [0.0], "max": [11.199999809265137], "mean": [5.600000381469727], "std": [3.242769718170166], "count": [337]}, "frame_index": {"min": [0.0], "max": [336.0], "mean": [168.0], "std": [97.28309205612247], "count": [337]}, "episode_index": {"min": [26.0], "max": [26.0], "mean": [26.0], "std": [0.0], "count": [337]}, "index": {"min": [3778.0], "max": [4114.0], "mean": [3946.0], "std": [97.28308868408203], "count": [337]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [337]}}}
28
+ {"episode_index": 27, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [27.0], "max": [27.0], "mean": [27.0], "std": [0.0], "count": [30]}, "index": {"min": [4115.0], "max": [4144.0], "mean": [4129.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
29
+ {"episode_index": 28, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [28.0], "max": [28.0], "mean": [28.0], "std": [0.0], "count": [30]}, "index": {"min": [4145.0], "max": [4174.0], "mean": [4159.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
30
+ {"episode_index": 29, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [29.0], "max": [29.0], "mean": [29.0], "std": [0.0], "count": [30]}, "index": {"min": [4175.0], "max": [4204.0], "mean": [4189.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
31
+ {"episode_index": 30, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [30.0], "max": [30.0], "mean": [30.0], "std": [0.0], "count": [30]}, "index": {"min": [4205.0], "max": [4234.0], "mean": [4219.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
32
+ {"episode_index": 31, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [31.0], "max": [31.0], "mean": [31.0], "std": [0.0], "count": [30]}, "index": {"min": [4235.0], "max": [4264.0], "mean": [4249.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
33
+ {"episode_index": 32, "stats": {"timestamp": {"min": [0.0], "max": [1.0666667222976685], "mean": [0.533333420753479], "std": [0.3173968195915222], "count": [33]}, "frame_index": {"min": [0.0], "max": [32.0], "mean": [16.0], "std": [9.521904571390467], "count": [33]}, "episode_index": {"min": [32.0], "max": [32.0], "mean": [32.0], "std": [0.0], "count": [33]}, "index": {"min": [4265.0], "max": [4297.0], "mean": [4281.0], "std": [9.521903991699219], "count": [33]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [33]}}}
34
+ {"episode_index": 33, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [33.0], "max": [33.0], "mean": [33.0], "std": [0.0], "count": [30]}, "index": {"min": [4298.0], "max": [4327.0], "mean": [4312.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
35
+ {"episode_index": 34, "stats": {"timestamp": {"min": [0.0], "max": [1.7000000476837158], "mean": [0.8500000834465027], "std": [0.5002776980400085], "count": [52]}, "frame_index": {"min": [0.0], "max": [51.0], "mean": [25.5], "std": [15.008331019803634], "count": [52]}, "episode_index": {"min": [34.0], "max": [34.0], "mean": [34.0], "std": [0.0], "count": [52]}, "index": {"min": [4328.0], "max": [4379.0], "mean": [4353.5], "std": [15.008331298828125], "count": [52]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [52]}}}
36
+ {"episode_index": 35, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [35.0], "max": [35.0], "mean": [35.0], "std": [0.0], "count": [30]}, "index": {"min": [4380.0], "max": [4409.0], "mean": [4394.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
37
+ {"episode_index": 36, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [36.0], "max": [36.0], "mean": [36.0], "std": [0.0], "count": [31]}, "index": {"min": [4410.0], "max": [4440.0], "mean": [4425.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [31]}}}
38
+ {"episode_index": 37, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [37.0], "max": [37.0], "mean": [37.0], "std": [0.0], "count": [31]}, "index": {"min": [4441.0], "max": [4471.0], "mean": [4456.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [31]}}}
39
+ {"episode_index": 38, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [38.0], "max": [38.0], "mean": [38.0], "std": [0.0], "count": [31]}, "index": {"min": [4472.0], "max": [4502.0], "mean": [4487.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [31]}}}
40
+ {"episode_index": 39, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [39.0], "max": [39.0], "mean": [39.0], "std": [0.0], "count": [31]}, "index": {"min": [4503.0], "max": [4533.0], "mean": [4518.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [31]}}}
41
+ {"episode_index": 40, "stats": {"timestamp": {"min": [0.0], "max": [1.100000023841858], "mean": [0.5500000715255737], "std": [0.32702359557151794], "count": [34]}, "frame_index": {"min": [0.0], "max": [33.0], "mean": [16.5], "std": [9.810708435174291], "count": [34]}, "episode_index": {"min": [40.0], "max": [40.0], "mean": [40.0], "std": [0.0], "count": [34]}, "index": {"min": [4534.0], "max": [4567.0], "mean": [4550.5], "std": [9.810708045959473], "count": [34]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [34]}}}
42
+ {"episode_index": 41, "stats": {"timestamp": {"min": [0.0], "max": [5.866666793823242], "mean": [2.933333396911621], "std": [1.7031561136245728], "count": [177]}, "frame_index": {"min": [0.0], "max": [176.0], "mean": [88.0], "std": [51.094683350292584], "count": [177]}, "episode_index": {"min": [41.0], "max": [41.0], "mean": [41.0], "std": [0.0], "count": [177]}, "index": {"min": [4568.0], "max": [4744.0], "mean": [4656.0], "std": [51.09468460083008], "count": [177]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [177]}}}
43
+ {"episode_index": 42, "stats": {"timestamp": {"min": [0.0], "max": [5.833333492279053], "mean": [2.9166665077209473], "std": [1.6935335397720337], "count": [176]}, "frame_index": {"min": [0.0], "max": [175.0], "mean": [87.5], "std": [50.806003582253936], "count": [176]}, "episode_index": {"min": [42.0], "max": [42.0], "mean": [42.0], "std": [0.0], "count": [176]}, "index": {"min": [4745.0], "max": [4920.0], "mean": [4832.5], "std": [50.80600357055664], "count": [176]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [176]}}}
44
+ {"episode_index": 43, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [43.0], "max": [43.0], "mean": [43.0], "std": [0.0], "count": [30]}, "index": {"min": [4921.0], "max": [4950.0], "mean": [4935.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
45
+ {"episode_index": 44, "stats": {"timestamp": {"min": [0.0], "max": [7.099999904632568], "mean": [3.549999713897705], "std": [2.0591933727264404], "count": [214]}, "frame_index": {"min": [0.0], "max": [213.0], "mean": [106.5], "std": [61.775804324994425], "count": [214]}, "episode_index": {"min": [44.0], "max": [44.0], "mean": [44.0], "std": [0.0], "count": [214]}, "index": {"min": [4951.0], "max": [5164.0], "mean": [5057.5], "std": [61.77580261230469], "count": [214]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [214]}}}
46
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47
+ {"episode_index": 46, "stats": {"timestamp": {"min": [0.0], "max": [9.533333778381348], "mean": [4.766666412353516], "std": [2.7616422176361084], "count": [287]}, "frame_index": {"min": [0.0], "max": [286.0], "mean": [143.0], "std": [82.84926070883192], "count": [287]}, "episode_index": {"min": [46.0], "max": [46.0], "mean": [46.0], "std": [0.0], "count": [287]}, "index": {"min": [5196.0], "max": [5482.0], "mean": [5339.0], "std": [82.84925842285156], "count": [287]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [287]}}}
48
+ {"episode_index": 47, "stats": {"timestamp": {"min": [0.0], "max": [1.8666666746139526], "mean": [0.9333333373069763], "std": [0.5483983159065247], "count": [57]}, "frame_index": {"min": [0.0], "max": [56.0], "mean": [28.0], "std": [16.451950239004088], "count": [57]}, "episode_index": {"min": [47.0], "max": [47.0], "mean": [47.0], "std": [0.0], "count": [57]}, "index": {"min": [5483.0], "max": [5539.0], "mean": [5511.0], "std": [16.451950073242188], "count": [57]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [57]}}}
49
+ {"episode_index": 48, "stats": {"timestamp": {"min": [0.0], "max": [2.566666603088379], "mean": [1.2833333015441895], "std": [0.7504936456680298], "count": [78]}, "frame_index": {"min": [0.0], "max": [77.0], "mean": [38.5], "std": [22.51480994071828], "count": [78]}, "episode_index": {"min": [48.0], "max": [48.0], "mean": [48.0], "std": [0.0], "count": [78]}, "index": {"min": [5540.0], "max": [5617.0], "mean": [5578.5], "std": [22.51481056213379], "count": [78]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [78]}}}
50
+ {"episode_index": 49, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [49.0], "max": [49.0], "mean": [49.0], "std": [0.0], "count": [31]}, "index": {"min": [5618.0], "max": [5648.0], "mean": [5633.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [31]}}}
51
+ {"episode_index": 50, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [50.0], "max": [50.0], "mean": [50.0], "std": [0.0], "count": [30]}, "index": {"min": [5649.0], "max": [5678.0], "mean": [5663.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
52
+ {"episode_index": 51, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [51.0], "max": [51.0], "mean": [51.0], "std": [0.0], "count": [30]}, "index": {"min": [5679.0], "max": [5708.0], "mean": [5693.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
53
+ {"episode_index": 52, "stats": {"timestamp": {"min": [0.0], "max": [5.333333492279053], "mean": [2.6666667461395264], "std": [1.549193263053894], "count": [161]}, "frame_index": {"min": [0.0], "max": [160.0], "mean": [80.0], "std": [46.475800154489], "count": [161]}, "episode_index": {"min": [52.0], "max": [52.0], "mean": [52.0], "std": [0.0], "count": [161]}, "index": {"min": [5709.0], "max": [5869.0], "mean": [5789.0], "std": [46.475799560546875], "count": [161]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [161]}}}
54
+ {"episode_index": 53, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [53.0], "max": [53.0], "mean": [53.0], "std": [0.0], "count": [30]}, "index": {"min": [5870.0], "max": [5899.0], "mean": [5884.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
55
+ {"episode_index": 54, "stats": {"timestamp": {"min": [0.0], "max": [11.166666984558105], "mean": [5.583333492279053], "std": [3.233147144317627], "count": [336]}, "frame_index": {"min": [0.0], "max": [335.0], "mean": [167.5], "std": [96.99441564681271], "count": [336]}, "episode_index": {"min": [54.0], "max": [54.0], "mean": [54.0], "std": [0.0], "count": [336]}, "index": {"min": [5900.0], "max": [6235.0], "mean": [6067.5], "std": [96.99441528320312], "count": [336]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [336]}}}
56
+ {"episode_index": 55, "stats": {"timestamp": {"min": [0.0], "max": [1.1333333253860474], "mean": [0.5666667222976685], "std": [0.33665016293525696], "count": [35]}, "frame_index": {"min": [0.0], "max": [34.0], "mean": [17.0], "std": [10.099504938362077], "count": [35]}, "episode_index": {"min": [55.0], "max": [55.0], "mean": [55.0], "std": [0.0], "count": [35]}, "index": {"min": [6236.0], "max": [6270.0], "mean": [6253.0], "std": [10.099504470825195], "count": [35]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [35]}}}
57
+ {"episode_index": 56, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [56.0], "max": [56.0], "mean": [56.0], "std": [0.0], "count": [30]}, "index": {"min": [6271.0], "max": [6300.0], "mean": [6285.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
58
+ {"episode_index": 57, "stats": {"timestamp": {"min": [0.0], "max": [1.3666666746139526], "mean": [0.6833333373069763], "std": [0.40403062105178833], "count": [42]}, "frame_index": {"min": [0.0], "max": [41.0], "mean": [20.5], "std": [12.120918557051139], "count": [42]}, "episode_index": {"min": [57.0], "max": [57.0], "mean": [57.0], "std": [0.0], "count": [42]}, "index": {"min": [6301.0], "max": [6342.0], "mean": [6321.5], "std": [12.120919227600098], "count": [42]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [42]}}}
59
+ {"episode_index": 58, "stats": {"timestamp": {"min": [0.0], "max": [1.7000000476837158], "mean": [0.8500000834465027], "std": [0.5002776980400085], "count": [52]}, "frame_index": {"min": [0.0], "max": [51.0], "mean": [25.5], "std": [15.008331019803634], "count": [52]}, "episode_index": {"min": [58.0], "max": [58.0], "mean": [58.0], "std": [0.0], "count": [52]}, "index": {"min": [6343.0], "max": [6394.0], "mean": [6368.5], "std": [15.008331298828125], "count": [52]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [52]}}}
60
+ {"episode_index": 59, "stats": {"timestamp": {"min": [0.0], "max": [1.600000023841858], "mean": [0.800000011920929], "std": [0.471404492855072], "count": [49]}, "frame_index": {"min": [0.0], "max": [48.0], "mean": [24.0], "std": [14.142135623730951], "count": [49]}, "episode_index": {"min": [59.0], "max": [59.0], "mean": [59.0], "std": [0.0], "count": [49]}, "index": {"min": [6395.0], "max": [6443.0], "mean": [6419.0], "std": [14.142135620117188], "count": [49]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [49]}}}
61
+ {"episode_index": 60, "stats": {"timestamp": {"min": [0.0], "max": [0.9666666388511658], "mean": [0.4833332896232605], "std": [0.2885147035121918], "count": [30]}, "frame_index": {"min": [0.0], "max": [29.0], "mean": [14.5], "std": [8.65544144839919], "count": [30]}, "episode_index": {"min": [60.0], "max": [60.0], "mean": [60.0], "std": [0.0], "count": [30]}, "index": {"min": [6444.0], "max": [6473.0], "mean": [6458.5], "std": [8.655441284179688], "count": [30]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [30]}}}
62
+ {"episode_index": 61, "stats": {"timestamp": {"min": [0.0], "max": [1.5333333015441895], "mean": [0.7666666507720947], "std": [0.45215535163879395], "count": [47]}, "frame_index": {"min": [0.0], "max": [46.0], "mean": [23.0], "std": [13.564659966250536], "count": [47]}, "episode_index": {"min": [61.0], "max": [61.0], "mean": [61.0], "std": [0.0], "count": [47]}, "index": {"min": [6474.0], "max": [6520.0], "mean": [6497.0], "std": [13.56466007232666], "count": [47]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [47]}}}
63
+ {"episode_index": 62, "stats": {"timestamp": {"min": [0.0], "max": [1.7000000476837158], "mean": [0.8500000834465027], "std": [0.5002776980400085], "count": [52]}, "frame_index": {"min": [0.0], "max": [51.0], "mean": [25.5], "std": [15.008331019803634], "count": [52]}, "episode_index": {"min": [62.0], "max": [62.0], "mean": [62.0], "std": [0.0], "count": [52]}, "index": {"min": [6521.0], "max": [6572.0], "mean": [6546.5], "std": [15.008331298828125], "count": [52]}, "task_index": {"min": [4.0], "max": [4.0], "mean": [4.0], "std": [0.0], "count": [52]}}}
64
+ {"episode_index": 63, "stats": {"timestamp": {"min": [0.0], "max": [22.96666717529297], "mean": [11.483333587646484], "std": [6.63952112197876], "count": [690]}, "frame_index": {"min": [0.0], "max": [689.0], "mean": [344.5], "std": [199.1856336854309], "count": [690]}, "episode_index": {"min": [63.0], "max": [63.0], "mean": [63.0], "std": [0.0], "count": [690]}, "index": {"min": [6573.0], "max": [7262.0], "mean": [6917.5], "std": [199.1856231689453], "count": [690]}, "task_index": {"min": [5.0], "max": [5.0], "mean": [5.0], "std": [0.0], "count": [690]}}}
65
+ {"episode_index": 64, "stats": {"timestamp": {"min": [0.0], "max": [4.800000190734863], "mean": [2.4000000953674316], "std": [1.3952299356460571], "count": [145]}, "frame_index": {"min": [0.0], "max": [144.0], "mean": [72.0], "std": [41.8568990729127], "count": [145]}, "episode_index": {"min": [64.0], "max": [64.0], "mean": [64.0], "std": [0.0], "count": [145]}, "index": {"min": [7263.0], "max": [7407.0], "mean": [7335.0], "std": [41.85689926147461], "count": [145]}, "task_index": {"min": [5.0], "max": [5.0], "mean": [5.0], "std": [0.0], "count": [145]}}}
66
+ {"episode_index": 65, "stats": {"timestamp": {"min": [0.0], "max": [5.766666889190674], "mean": [2.883333444595337], "std": [1.6742881536483765], "count": [174]}, "frame_index": {"min": [0.0], "max": [173.0], "mean": [86.5], "std": [50.22864388639879], "count": [174]}, "episode_index": {"min": [65.0], "max": [65.0], "mean": [65.0], "std": [0.0], "count": [174]}, "index": {"min": [7408.0], "max": [7581.0], "mean": [7494.5], "std": [50.22864532470703], "count": [174]}, "task_index": {"min": [5.0], "max": [5.0], "mean": [5.0], "std": [0.0], "count": [174]}}}
67
+ {"episode_index": 66, "stats": {"timestamp": {"min": [0.0], "max": [13.5], "mean": [6.75], "std": [3.9067249298095703], "count": [406]}, "frame_index": {"min": [0.0], "max": [405.0], "mean": [202.5], "std": [117.20174913370533], "count": [406]}, "episode_index": {"min": [66.0], "max": [66.0], "mean": [66.0], "std": [0.0], "count": [406]}, "index": {"min": [7582.0], "max": [7987.0], "mean": [7784.5], "std": [117.20175170898438], "count": [406]}, "task_index": {"min": [5.0], "max": [5.0], "mean": [5.0], "std": [0.0], "count": [406]}}}
68
+ {"episode_index": 67, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [67.0], "max": [67.0], "mean": [67.0], "std": [0.0], "count": [31]}, "index": {"min": [7988.0], "max": [8018.0], "mean": [8003.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [6.0], "max": [6.0], "mean": [6.0], "std": [0.0], "count": [31]}}}
69
+ {"episode_index": 68, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [68.0], "max": [68.0], "mean": [68.0], "std": [0.0], "count": [31]}, "index": {"min": [8019.0], "max": [8049.0], "mean": [8034.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [6.0], "max": [6.0], "mean": [6.0], "std": [0.0], "count": [31]}}}
70
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71
+ {"episode_index": 70, "stats": {"timestamp": {"min": [0.0], "max": [1.2999999523162842], "mean": [0.6499999761581421], "std": [0.3847798705101013], "count": [40]}, "frame_index": {"min": [0.0], "max": [39.0], "mean": [19.5], "std": [11.543396380615196], "count": [40]}, "episode_index": {"min": [70.0], "max": [70.0], "mean": [70.0], "std": [0.0], "count": [40]}, "index": {"min": [8081.0], "max": [8120.0], "mean": [8100.5], "std": [11.54339599609375], "count": [40]}, "task_index": {"min": [6.0], "max": [6.0], "mean": [6.0], "std": [0.0], "count": [40]}}}
72
+ {"episode_index": 71, "stats": {"timestamp": {"min": [0.0], "max": [1.0], "mean": [0.4999999701976776], "std": [0.29814237356185913], "count": [31]}, "frame_index": {"min": [0.0], "max": [30.0], "mean": [15.0], "std": [8.94427190999916], "count": [31]}, "episode_index": {"min": [71.0], "max": [71.0], "mean": [71.0], "std": [0.0], "count": [31]}, "index": {"min": [8121.0], "max": [8151.0], "mean": [8136.0], "std": [8.9442720413208], "count": [31]}, "task_index": {"min": [6.0], "max": [6.0], "mean": [6.0], "std": [0.0], "count": [31]}}}
73
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Surgical/utenn/surgical_video_datasets/meta/episodes.jsonl ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {"episode_index": 0, "tasks": ["CONTACT"], "length": 79}
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+ {"episode_index": 1, "tasks": ["CONTACT"], "length": 29}
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+ {"episode_index": 61, "tasks": ["PULLING"], "length": 47}
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+ {"episode_index": 62, "tasks": ["PULLING"], "length": 52}
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+ {"episode_index": 63, "tasks": ["STATIC"], "length": 690}
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+ {"episode_index": 64, "tasks": ["STATIC"], "length": 145}
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+ {"episode_index": 65, "tasks": ["STATIC"], "length": 174}
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+ {"episode_index": 66, "tasks": ["STATIC"], "length": 406}
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+ {"episode_index": 67, "tasks": ["ZOOM_IN"], "length": 31}
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+ {"episode_index": 68, "tasks": ["ZOOM_IN"], "length": 31}
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+ {"episode_index": 69, "tasks": ["ZOOM_IN"], "length": 31}
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+ {"episode_index": 70, "tasks": ["ZOOM_IN"], "length": 40}
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+ {"episode_index": 71, "tasks": ["ZOOM_IN"], "length": 31}
73
+ {"episode_index": 72, "tasks": ["ZOOM_IN"], "length": 31}
Surgical/utenn/surgical_video_datasets/meta/info.json ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "codebase_version": "v2.1",
3
+ "robot_type": "davinci_xi",
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+ "total_episodes": 73,
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+ "total_frames": 8183,
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+ "total_tasks": 7,
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+ "total_videos": 219,
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+ "total_chunks": 1,
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+ "chunks_size": 1000,
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+ "fps": 30.0,
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+ "splits": {
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+ "train": "0:65",
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+ "val": "65:68",
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+ "test": "68:73"
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+ },
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+ "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
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+ "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
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+ "features": {
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+ "observation.images.color": {
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+ "dtype": "video",
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+ "shape": [
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+ 480,
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+ 720,
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+ 3
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+ ],
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+ "names": [
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+ "observation.images.color"
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+ ],
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+ "info": {
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+ "video.height": 480,
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+ "video.width": 720,
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+ "video.codec": "h264",
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+ "video.pix_fmt": "yuv420p",
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+ "video.fps": 30.0,
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+ "video.channels": 3,
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+ "has_audio": false,
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+ "video.is_depth_map": false
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+ }
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+ },
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+ "observation.images.depth": {
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+ "dtype": "video",
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+ "shape": [
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+ 480,
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+ 720,
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+ 3
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+ ],
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+ "names": [
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+ "observation.images.depth"
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+ ],
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+ "info": {
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+ "video.height": 480,
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+ "video.width": 720,
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+ "video.codec": "h264",
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+ "video.pix_fmt": "yuv420p",
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+ "video.fps": 30.0,
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+ "video.channels": 3,
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+ "has_audio": false,
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+ "video.is_depth_map": false
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+ }
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+ },
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+ "observation.images.tool_segmentation": {
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+ "dtype": "video",
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+ "shape": [
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+ 480,
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+ 720,
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+ 3
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+ ],
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+ "names": [
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+ "observation.images.tool_segmentation"
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+ ],
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+ "info": {
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+ "video.height": 480,
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+ "video.width": 720,
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+ "video.codec": "h264",
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+ "video.pix_fmt": "yuv420p",
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+ "video.fps": 30.0,
77
+ "video.channels": 3,
78
+ "has_audio": false,
79
+ "video.is_depth_map": false
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+ }
81
+ },
82
+ "action.label": {
83
+ "dtype": "string",
84
+ "shape": [
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+ 1
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+ ],
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+ "names": [
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+ "action.label"
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+ ]
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+ },
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+ "timestamp": {
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+ "dtype": "float32",
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+ "shape": [
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+ 1
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+ ],
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+ "names": null
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+ },
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+ "frame_index": {
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+ "dtype": "int64",
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+ "shape": [
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+ 1
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+ ],
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+ "names": null
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+ },
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+ "episode_index": {
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+ ],
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+ "names": null
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+ },
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+ "index": {
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+ "dtype": "int64",
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+ "shape": [
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+ 1
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+ ],
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+ "names": null
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+ },
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+ "task_index": {
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+ "dtype": "int64",
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+ "shape": [
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+ 1
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+ ],
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+ "names": null
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+ }
126
+ },
127
+ "notes": {
128
+ "repo_id": "UTK_AURAS/surgical_actions_v1",
129
+ "license": "CC BY 4.0",
130
+ "rgb_root": "/home/nan/Desktop/openH/clips_cutting_v1/clips_by_label",
131
+ "depth_root": "/home/nan/Desktop/openH/clips_cutting_v1/clips_by_label_depth",
132
+ "seg_root": "/home/nan/Desktop/openH/clips_cutting_v1/segmented_by_label",
133
+ "depth_convention": "<label>/<clip_id>/<clip_id>_src.mp4",
134
+ "seg_convention": "<label>/<clip_id>/tool_mask.mp4",
135
+ "truncation_policy": "n_steps = min(rgb, depth_src, tool_mask) estimated frame counts"
136
+ }
137
+ }
Surgical/utenn/surgical_video_datasets/meta/tasks.jsonl ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {"task_index": 0, "task": "CONTACT"}
2
+ {"task_index": 1, "task": "EXPLORATION"}
3
+ {"task_index": 2, "task": "GRASPING"}
4
+ {"task_index": 3, "task": "MOVE"}
5
+ {"task_index": 4, "task": "PULLING"}
6
+ {"task_index": 5, "task": "STATIC"}
7
+ {"task_index": 6, "task": "ZOOM_IN"}
Surgical/utenn/surgical_video_datasets_round2_with_part_seg/README.md ADDED
@@ -0,0 +1,293 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!--
2
+ Open-H Embodiment Dataset README Template (v1.0)
3
+ Please fill out this template and include it in the ./metadata directory of your LeRobot dataset.
4
+ This file helps others understand the context and details of your contribution.
5
+ -->
6
+
7
+ # surgical_video_datasets_round2 - README
8
+
9
+ ---
10
+
11
+ ## 📋 At a Glance
12
+
13
+ **Video-only Open-H/LeRobot export of labeled endoscopic surgical clips**, with aligned RGB, optional relative-depth visualization, tool-part segmentation (color + lossless integer IDs), and per-frame 2D tool pose state.
14
+
15
+ ---
16
+
17
+ ## 📖 Dataset Overview
18
+
19
+ This dataset packages DeLong’s labeled surgical clips into a consistent **Open-H / LeRobot v2.1-style** layout for downstream learning (representation learning, imitation learning on vision + state, etc.).
20
+ Each **episode** corresponds to one clip and is aligned by **frame index** across all available modalities.
21
+
22
+ **What it includes (per episode):**
23
+ - **RGB endoscopic video** (MP4 copy)
24
+ - **Relative depth visualization video** (MP4 copy, if available; *not metric depth*)
25
+ - **Tool-part segmentation (colorized)** (MP4; derived from label IDs for quick viewing)
26
+ - **Tool-part segmentation IDs (lossless)** (MKV+FFV1; integer labels preserved exactly, optionally packed into channels)
27
+ - **2D tool pose state** per frame (`observation.state`), fixed-length over tool slots
28
+
29
+ | | |
30
+ | :--- | :--- |
31
+ | **Total Trajectories** | `101` |
32
+ | **Total Hours** | `292 seconds` |
33
+ | **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other` |
34
+ | **License** | CC BY 4.0 *(confirm if different)* |
35
+ | **Version** | `2.1-ish` *(export format version)* |
36
+
37
+
38
+ ---
39
+
40
+ ## 🎯 Tasks & Domain
41
+
42
+ ### Domain
43
+
44
+ - [x] **Surgical Robotics**
45
+ - [ ] **Ultrasound Robotics**
46
+ - [ ] **Other Healthcare Robotics** (Please specify: )
47
+
48
+ ### Demonstrated Skills
49
+
50
+ The dataset uses **clip-level action labels** as tasks (one task per episode).
51
+ The labels present include (non-exhaustive):
52
+ - ENERGY_WORK: Energy-dominant actions such as electrocautery/coagulation/cutting using energy devices (e.g., “electrical-cutting” mapped here).
53
+ - PREPARE: Non-operative preparation steps before a clear operative action, e.g., positioning instruments, approaching the target area, adjusting view for readiness.
54
+ - PULLING: Traction/retraction action where tissue is pulled to expose or tension an area (continuous pulling/holding under tension).
55
+ - SPLITTING: The surgeon uses a tool to push along a tissue surface to separate layers/planes, commonly pushing fatty tissue (yellow) aside to open a space.
56
+ - STATIC: The view is essentially stable: camera stays on a site, tools are almost not moving, typically waiting/pausing (e.g., surgeon waiting, assessing, or holding position).
57
+ - ZOOM_IN: Camera zoom-in to magnify the worksite or improve visibility.
58
+ - ZOOM_OUT: Camera zoom-out to widen the field of view (e.g., to regain context or re-orient).
59
+
60
+ **Authoritative mapping:** `metadata/tasks.jsonl` (task_index → task string).
61
+
62
+ ---
63
+
64
+ ## 🔬 Data Collection Details
65
+
66
+ ### Collection Method
67
+
68
+ - [x] **Human Teleoperation (Real Surgery Video)** *(source videos are teleoperated surgical clips)*
69
+ - [ ] **Programmatic/State-Machine**
70
+ - [ ] **AI Policy / Autonomous**
71
+ - [ ] **Other** (Please specify: )
72
+
73
+ ### Operator Details
74
+
75
+ | | Description |
76
+ | :--- | :--- |
77
+ | **Operator Count** | Unknown / not provided in source package |
78
+ | **Operator Skill Level** | `[x] Expert` <br> `[ ] Intermediate` <br> `[ ] Novice` <br> `[] N/A` |
79
+ | **Collection Period** | Unknown / not provided in source package |
80
+
81
+ ### Recovery Demonstrations
82
+
83
+ - [ ] **Yes**
84
+ - [x] **No (not explicitly labeled)**
85
+
86
+ *(If you later add recovery/failure subsets, encode them in `metadata/info.json` under `splits`, e.g., `recovery`, `failure`.)*
87
+
88
+ ---
89
+
90
+ ## 💡 Diversity Dimensions
91
+
92
+ - [ ] **Camera Position / Angle**
93
+ - [ ] **Lighting Conditions**
94
+ - [ ] **Target Object**
95
+ - [ ] **Spatial Layout**
96
+ - [ ] **Robot Embodiment**
97
+ - [ ] **Task Execution**
98
+ - [ ] **Background / Scene**
99
+ - [x] **Other** (Please specify: natural variation across clips)
100
+
101
+ **Elaboration:** Clips vary naturally across action label, camera motion (zoom in/out), tool visibility/occlusion, and interaction patterns.
102
+
103
+ ---
104
+
105
+ ## 🛠️ Equipment & Setup
106
+
107
+ ### Robotic Platform(s)
108
+
109
+ - **Robot 1:** da Vinci surgical system (endoscopic view) *(exact model not encoded in export; update if known)*
110
+ - **Robot 2:** N/A
111
+
112
+ ### Sensors & Cameras
113
+
114
+ | Type | Model/Details |
115
+ | :--- | :--- |
116
+ | **Primary Camera** | Endoscopic RGB video (resolution & fps stored per episode in `metadata/episodes.jsonl`) |
117
+ | **Room/3rd Person Camera** | N/A |
118
+ | **Force/Torque Sensor** | N/A |
119
+ | **Medical Imager** | N/A |
120
+ | **Other** | Optional relative depth *visualization* video (`*_vis.mp4` from upstream depth pipeline) |
121
+
122
+ ---
123
+
124
+ ## 🎯 Action & State Space Representation
125
+
126
+ This is a **video-first** export. There is **no true robot action/command stream** included.
127
+
128
+ ### Action Space Representation
129
+
130
+ **Primary Action Representation:**
131
+ - [ ] **Absolute Cartesian**
132
+ - [ ] **Relative Cartesian**
133
+ - [ ] **Joint Space**
134
+ - [x] **Other**: *No actions available; `action` is a placeholder vector of zeros.*
135
+
136
+ **Orientation Representation:**
137
+ - [ ] **Quaternions**
138
+ - [ ] **Euler Angles**
139
+ - [ ] **Axis-Angle**
140
+ - [ ] **Rotation Matrix**
141
+ - [x] **Other**: N/A
142
+
143
+ **Reference Frame:**
144
+ - [ ] **Robot Base Frame**
145
+ - [ ] **Tool/End-Effector Frame**
146
+ - [ ] **World/Global Frame**
147
+ - [x] **Camera Frame** *(for 2D pose state)*
148
+ - [ ] **Other**
149
+
150
+ **Action Dimensions:**
151
+ ```text
152
+ action: [a0, a1, ..., a_{D-1}]
153
+ - All entries are 0.0 (placeholder).
154
+ - D is set by exporter flag --action_dim (default D=1).
155
+ ```
156
+
157
+ ### State Space Representation
158
+
159
+ **State Information Included:**
160
+ - [ ] **Joint Positions**
161
+ - [ ] **Joint Velocities**
162
+ - [ ] **End-Effector Pose**
163
+ - [ ] **Force/Torque Readings**
164
+ - [ ] **Gripper State**
165
+ - [x] **Other**: 2D tool pose in image space (per tool slot)
166
+
167
+ **State Dimensions:**
168
+
169
+ `observation.state` is a fixed-length float32 vector of size `max_tools * 4`:
170
+
171
+ ```text
172
+ observation.state = [
173
+ tool0.tx_px, tool0.ty_px, tool0.theta_rad, tool0.visible,
174
+ tool1.tx_px, tool1.ty_px, tool1.theta_rad, tool1.visible,
175
+ tool2.tx_px, tool2.ty_px, tool2.theta_rad, tool2.visible
176
+ ]
177
+ ```
178
+
179
+ **Units & conventions**
180
+ - `tx_px`, `ty_px`: pixel coordinates in the **image coordinate system**
181
+ - origin: top-left
182
+ - x increases to the right, y increases downward
183
+ - `theta_rad`: in-plane rotation in **radians** (as provided by upstream `tool_poses.json`)
184
+ - `visible`: `1.0` if pose is valid/visible at that frame, else `0.0`
185
+ - If not visible: pose values may be stored as `NaN` and `visible=0.0`.
186
+
187
+ **Tool slot ordering (important):**
188
+ - Tool slot order can differ by episode.
189
+ - Decode slots using `metadata/tool_slot_maps.jsonl`.
190
+ - Each parquet row stores `observation.meta.tool_slot_map_id` which equals the episode index as a string.
191
+
192
+ ---
193
+
194
+ ## ⏱️ Data Synchronization Approach
195
+
196
+ All modalities are aligned by **frame index** during export.
197
+
198
+ - The exporter probes the RGB video for `num_frames = N` and `fps`.
199
+ - For each frame index `t ∈ [0, N-1]`, it writes one parquet row with:
200
+ - `timestamp = t / fps`
201
+ - video references (path + timestamp) for available modalities
202
+
203
+ If a modality is missing for an episode (e.g., depth not available), the corresponding parquet field may be absent for that episode.
204
+
205
+ ---
206
+
207
+ ## 📦 Dataset Layout & Field Semantics
208
+
209
+ ### Directory structure
210
+
211
+ ```text
212
+ surgical_video_datasets_round2_with_part_seg/
213
+ ├── data/
214
+ │ └── chunk-000/
215
+ │ ├── episode_000000.parquet
216
+ │ ├── episode_000001.parquet
217
+ │ └── ...
218
+ ├── videos/
219
+ │ └── chunk-000/
220
+ │ ├── observation.images.rgb/
221
+ │ │ ├── episode_000000.mp4
222
+ │ │ └── ...
223
+ │ ├── observation.images.depth/ (optional)
224
+ │ │ ├── episode_000000.mp4
225
+ │ │ └── ...
226
+ │ ├── observation.images.tool_segmentation/ (colorized)
227
+ │ │ ├── episode_000000.mp4
228
+ │ │ └── ...
229
+ │ └── observation.images.tool_segmentation_id/ (optional, lossless IDs)
230
+ │ ├── episode_000000.mkv
231
+ │ └── ...
232
+ ├── metadata/
233
+ │ ├── README.md (this file)
234
+ │ ├── info.json
235
+ │ ├── tasks.jsonl
236
+ │ ├── episodes.jsonl
237
+ │ ├── episodes_stats.jsonl
238
+ │ ├── tool_slot_maps.jsonl
239
+ │ └── label_id_maps/
240
+ │ ├── episode_000000.json
241
+ │ └── ...
242
+ └── raw/ (optional; if --write_raw)
243
+ └── ... (provenance / audit-only metadata; no per-frame images)
244
+ ```
245
+
246
+ ### Parquet schema (per timestep)
247
+
248
+ Each row in `data/chunk-000/episode_XXXXXX.parquet` includes:
249
+
250
+ - `index` *(int)*: global row index across dataset
251
+ - `episode_index` *(int)*
252
+ - `frame_index` *(int)*
253
+ - `timestamp` *(float, seconds)*: `frame_index / fps`
254
+ - `task_index` *(int)*: index into `metadata/tasks.jsonl`
255
+ - `instruction.text` *(string)*: task/action label (repeated per frame)
256
+ - `action` *(list[float])*: placeholder zeros, length = `action_dim`
257
+ - `observation.state` *(list[float])*: tool pose vector (see “State Space”)
258
+ - `observation.meta.tool_slot_map_id` *(string)*: key for `tool_slot_maps.jsonl`
259
+ - `observation.images.rgb` *(dict)*: `{"path": "...", "timestamp": ...}`
260
+ Optional (may be missing per episode):
261
+ - `observation.images.depth` *(dict)*: relative depth visualization video reference
262
+ - `observation.images.tool_segmentation` *(dict)*: colorized segmentation video reference
263
+ - `observation.images.tool_segmentation_id` *(dict)*: lossless integer ID video reference
264
+ - `next.done` *(bool)*: True on last timestep of episode
265
+
266
+ ### Segmentation ID decoding (lossless)
267
+
268
+ If `videos/.../observation.images.tool_segmentation_id/episode_XXXXXX.mkv` exists:
269
+
270
+ - Container/codec: **MKV + FFV1** (lossless)
271
+ - Each frame is stored as a **3-channel uint8** image that encodes integer label IDs:
272
+
273
+ **Case A: max_id ≤ 255**
274
+ - `B = G = R = id`
275
+
276
+ **Case B: max_id > 255**
277
+ - Packed uint16:
278
+ - `id = B + 256 * G`
279
+ - `R` is unused (0)
280
+
281
+ Decode semantic meaning (tool/part names) using:
282
+ - `metadata/label_id_maps/episode_XXXXXX.json`
283
+
284
+ ---
285
+
286
+ ## 👥 Attribution & Contact
287
+
288
+ | | |
289
+ | :--- | :--- |
290
+ | **Dataset Lead** | Nan Xiao, Tom Olesch, Farong Wang, Fei Liu, DeLong Jonathan C. |
291
+ | **Institution** | University of Tennessee, Knoxville (AURAS Lab) |
292
+ | **Contact Email** | *nxiao4@vols.utk.edu* |
293
+ | **Citation (BibTeX)** | <pre><code>@misc{surgical_video_datasets_round2_2026,<br> author = {Xiao, Nan and collaborators},<br> title = {surgical\_video\_datasets\_round2: Open-H/LeRobot Export of Labeled Endoscopic Clips},<br> year = {2026},<br> publisher = {Open-H-Embodiment},<br> note = {LeRobot v2.1-style export; schema in metadata/info.json}<br>}</code></pre> |
Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/episodes.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/episodes_stats.jsonl ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {"episode_index": 2, "stats": {"observation.state": {"min": [384.3041076660156, 264.95355224609375, -2.7846603393554688, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [413.5807189941406, 272.15057373046875, -2.766784429550171, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [401.7784118652344, 268.3266906738281, -2.7773540019989014, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [8.697540283203125, 1.9888360500335693, 0.004604976624250412, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 3, "stats": {"observation.state": {"min": [356.8326416015625, 425.7341613769531, -0.8033699989318848, 1.0, 357.8116760253906, 260.297119140625, -3.0486528873443604, 1.0, NaN, NaN, NaN, 0.0], "max": [376.4953918457031, 446.25537109375, -0.669549822807312, 1.0, 420.44244384765625, 330.8212585449219, -2.9575674533843994, 1.0, NaN, NaN, NaN, 0.0], "mean": [365.9012145996094, 438.2554931640625, -0.766288697719574, 1.0, 382.5849609375, 302.1181335449219, -2.9860658645629883, 1.0, NaN, NaN, NaN, 0.0], "std": [5.178049564361572, 7.171562671661377, 0.03356735035777092, 0.0, 22.64530372619629, 23.132892608642578, 0.019902152940630913, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 4, "stats": {"observation.state": {"min": [59.880001068115234, 27.814590454101562, 0.7698649764060974, 1.0, 265.8669738769531, 318.6214599609375, -2.4383859634399414, 1.0, NaN, NaN, NaN, 0.0], "max": [65.319091796875, 37.81387710571289, 0.8407058119773865, 1.0, 371.28619384765625, 354.6600036621094, -2.0304176807403564, 1.0, NaN, NaN, NaN, 0.0], "mean": [63.80950927734375, 28.967124938964844, 0.7883103489875793, 1.0, 297.2059631347656, 334.4215393066406, -2.1598594188690186, 1.0, NaN, NaN, NaN, 0.0], "std": [0.9012919068336487, 1.6684775352478027, 0.010292603634297848, 0.0, 33.05772399902344, 11.784405708312988, 0.07008719444274902, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 6, "stats": {"observation.state": {"min": [55.703887939453125, 26.76626968383789, 0.7763140201568604, 1.0, 247.34889221191406, 243.47108459472656, -2.4320077896118164, 1.0, NaN, NaN, NaN, 0.0], "max": [64.65381622314453, 29.16176414489746, 0.8003109693527222, 1.0, 327.2525939941406, 308.16729736328125, -2.262880802154541, 1.0, NaN, NaN, NaN, 0.0], "mean": [60.826934814453125, 27.84726905822754, 0.7887111306190491, 1.0, 293.78509521484375, 280.497314453125, -2.3681375980377197, 1.0, NaN, NaN, NaN, 0.0], "std": [3.6087276935577393, 0.5685663819313049, 0.006976544391363859, 0.0, 24.623638153076172, 18.702613830566406, 0.03592967987060547, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 8, "stats": {"observation.state": {"min": [56.00348663330078, 29.342945098876953, 0.7827764749526978, 1.0, 285.26141357421875, 260.1290283203125, -2.4899532794952393, 1.0, NaN, NaN, NaN, 0.0], "max": [73.16712188720703, 38.76329040527344, 0.8388797044754028, 1.0, 324.6043395996094, 294.1772766113281, -2.3963773250579834, 1.0, NaN, NaN, NaN, 0.0], "mean": [63.96027755737305, 32.84423065185547, 0.8270089030265808, 1.0, 303.14178466796875, 280.6202697753906, -2.4329566955566406, 1.0, NaN, NaN, NaN, 0.0], "std": [5.861673355102539, 2.4232540130615234, 0.01040208525955677, 0.0, 11.7455472946167, 9.33225154876709, 0.02500530332326889, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 9, "stats": {"observation.state": {"min": [258.6244201660156, 50.73966598510742, -2.7920920848846436, 1.0, 230.6536407470703, 188.94366455078125, -2.5026261806488037, 1.0, NaN, NaN, NaN, 0.0], "max": [272.95135498046875, 56.91394805908203, -2.7200443744659424, 1.0, 300.8736877441406, 209.03485107421875, -2.4443652629852295, 1.0, NaN, NaN, NaN, 0.0], "mean": [264.7134704589844, 55.005043029785156, -2.7472639083862305, 1.0, 253.27362060546875, 202.35647583007812, -2.4703683853149414, 1.0, NaN, NaN, NaN, 0.0], "std": [4.537676811218262, 1.4990803003311157, 0.019037974998354912, 0.0, 20.085912704467773, 5.344117164611816, 0.01734999381005764, 0.0, NaN, NaN, NaN, 0.0]}}}
11
+ {"episode_index": 10, "stats": {"observation.state": {"min": [347.7539367675781, 113.36476135253906, -2.7359015941619873, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [506.32025146484375, 292.0652160644531, -2.482787847518921, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [412.84661865234375, 216.3289031982422, -2.5831878185272217, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [32.502201080322266, 47.21479797363281, 0.07253286242485046, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 11, "stats": {"observation.state": {"min": [191.6622772216797, 303.9662780761719, -2.2450010776519775, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [265.5035095214844, 317.421142578125, -2.0503323078155518, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [256.32574462890625, 310.4576110839844, -2.1991569995880127, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [12.015328407287598, 2.093907117843628, 0.0483187660574913, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 12, "stats": {"observation.state": {"min": [50.36623764038086, 29.669710159301758, 0.8033019304275513, 1.0, 228.9422607421875, 275.4979248046875, -2.4785022735595703, 1.0, NaN, NaN, NaN, 0.0], "max": [54.86330032348633, 38.26660919189453, 0.8322877883911133, 1.0, 283.4683532714844, 296.03936767578125, -2.4393770694732666, 1.0, NaN, NaN, NaN, 0.0], "mean": [51.74681854248047, 32.61457061767578, 0.8152251243591309, 1.0, 249.5734405517578, 283.7017822265625, -2.4546327590942383, 1.0, NaN, NaN, NaN, 0.0], "std": [1.2399615049362183, 2.1189420223236084, 0.00826579611748457, 0.0, 16.68489646911621, 6.749054431915283, 0.007726580370217562, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 13, "stats": {"observation.state": {"min": [239.19729614257812, 293.97125244140625, -2.218303680419922, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [317.9524230957031, 308.8317565917969, -1.9554225206375122, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [270.1346130371094, 305.5314636230469, -2.141136884689331, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [21.940916061401367, 4.21353006362915, 0.07739460468292236, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
15
+ {"episode_index": 14, "stats": {"observation.state": {"min": [280.7657165527344, 184.92335510253906, -2.7738559246063232, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [364.3451843261719, 320.9526062011719, -2.4396750926971436, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [315.6929626464844, 251.97373962402344, -2.598022699356079, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [27.22176742553711, 36.11315155029297, 0.08322672545909882, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 15, "stats": {"observation.state": {"min": [630.4649658203125, 165.64134216308594, -2.7344138622283936, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [667.6350708007812, 242.122314453125, -2.306856632232666, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [638.1639404296875, 191.48663330078125, -2.578819513320923, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [7.241974353790283, 27.176237106323242, 0.11722011864185333, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
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+ {"episode_index": 17, "stats": {"observation.state": {"min": [69.49676513671875, 312.0105285644531, -2.7091004848480225, 0.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "max": [361.7476501464844, 454.7865295410156, 0.9727065563201904, 1.0, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "mean": [193.3723602294922, 384.4645080566406, -0.9169466495513916, 0.9794871807098389, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0], "std": [97.87049865722656, 28.514907836914062, 1.1672794818878174, 0.1417464017868042, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, 0.0]}}}
19
+ {"episode_index": 18, "stats": {"observation.state": {"min": [41.71875, 312.329345703125, 1.343963861465454, 1.0, 403.30584716796875, 171.25390625, -3.141575574874878, 1.0, NaN, NaN, NaN, 0.0], "max": [112.67349243164062, 321.3931579589844, 2.9236526489257812, 1.0, 590.3750610351562, 212.8740692138672, 3.1043829917907715, 1.0, NaN, NaN, NaN, 0.0], "mean": [66.08668518066406, 316.5172424316406, 1.9619240760803223, 1.0, 500.392578125, 186.23545837402344, -0.274508535861969, 1.0, NaN, NaN, NaN, 0.0], "std": [23.344104766845703, 2.2489073276519775, 0.5422849059104919, 0.0, 78.65516662597656, 14.069660186767578, 2.973236322402954, 0.0, NaN, NaN, NaN, 0.0]}}}
20
+ {"episode_index": 19, "stats": {"observation.state": {"min": [282.94586181640625, 237.79579162597656, -3.0087831020355225, 0.0, 85.15013122558594, 252.7935333251953, 2.9652645587921143, 1.0, NaN, NaN, NaN, 0.0], "max": [665.725830078125, 429.89056396484375, 3.141515016555786, 1.0, 85.97078704833984, 253.81900024414062, 2.9847841262817383, 1.0, NaN, NaN, NaN, 0.0], "mean": [417.4256591796875, 320.01556396484375, -2.3589627742767334, 0.98591548204422, 85.59146881103516, 253.15773010253906, 2.977030038833618, 1.0, NaN, NaN, NaN, 0.0], "std": [103.02722930908203, 50.33049392700195, 1.1297963857650757, 0.11783939599990845, 0.21495221555233002, 0.28124964237213135, 0.0038752113468945026, 0.0, NaN, NaN, NaN, 0.0]}}}
21
+ {"episode_index": 20, "stats": {"observation.state": {"min": [114.37278747558594, 67.64935302734375, -2.9805848598480225, 1.0, 379.906494140625, 308.8392639160156, -2.9282586574554443, 1.0, NaN, NaN, NaN, 0.0], "max": [199.82643127441406, 190.47694396972656, 2.558408737182617, 1.0, 633.015625, 435.4185485839844, -2.3009326457977295, 1.0, NaN, NaN, NaN, 0.0], "mean": [166.4593505859375, 150.20794677734375, -1.7910466194152832, 1.0, 538.6959838867188, 366.42486572265625, -2.7574076652526855, 1.0, NaN, NaN, NaN, 0.0], "std": [20.691904067993164, 37.00544357299805, 1.0953340530395508, 0.0, 74.23902893066406, 30.44778823852539, 0.15810023248195648, 0.0, NaN, NaN, NaN, 0.0]}}}
22
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6
+ "3": "scissors:left_gripper",
7
+ "4": "scissors:right_gripper"
8
+ },
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+ "label_to_obj_id": {
10
+ "scissors:tool_body": 1,
11
+ "scissors:wrist": 2,
12
+ "scissors:left_gripper": 3,
13
+ "scissors:right_gripper": 4
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+ },
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+ "tool_to_obj_ids": {
16
+ "scissors": {
17
+ "tool_body": 1,
18
+ "wrist": 2,
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+ "left_gripper": 3,
20
+ "right_gripper": 4
21
+ }
22
+ },
23
+ "obj_id_to_tool": {
24
+ "1": "scissors",
25
+ "2": "scissors",
26
+ "3": "scissors",
27
+ "4": "scissors"
28
+ },
29
+ "obj_id_to_part": {
30
+ "1": "tool_body",
31
+ "2": "wrist",
32
+ "3": "left_gripper",
33
+ "4": "right_gripper"
34
+ },
35
+ "tools": [
36
+ {
37
+ "name": "scissors",
38
+ "parts": [
39
+ "tool_body",
40
+ "wrist",
41
+ "left_gripper",
42
+ "right_gripper"
43
+ ]
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+ }
45
+ ],
46
+ "saved_at_unix": 1769840766.7221808
47
+ }
Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/tasks.jsonl ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {"task_index": 0, "task": "ENERGY_WORK"}
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+ {"task_index": 1, "task": "PREPARE"}
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+ {"task_index": 2, "task": "PULLING"}
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+ {"task_index": 3, "task": "SPLITTING"}
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+ {"task_index": 4, "task": "STATIC"}
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+ {"task_index": 5, "task": "ZOOM_IN"}
7
+ {"task_index": 6, "task": "ZOOM_OUT"}
Surgical/utenn/surgical_video_datasets_round2_with_part_seg/meta/tool_slot_maps.jsonl ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {"episode_index": 0, "clip_id": "1_3zEyp5OT", "task": "ENERGY_WORK", "tool_order": ["grasper", "hook"], "tool_name_to_slot": {"grasper": 0, "hook": 1}}
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+ {"episode_index": 1, "clip_id": "1_7fJfrPJ7", "task": "ENERGY_WORK", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 2, "clip_id": "1_Dzr3eDeE", "task": "ENERGY_WORK", "tool_order": ["hook"], "tool_name_to_slot": {"hook": 0}}
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+ {"episode_index": 3, "clip_id": "1_LyrCBW26", "task": "ENERGY_WORK", "tool_order": ["grasper", "hook"], "tool_name_to_slot": {"grasper": 0, "hook": 1}}
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+ {"episode_index": 4, "clip_id": "1_XHjbXQTb", "task": "ENERGY_WORK", "tool_order": ["grasper", "scissors"], "tool_name_to_slot": {"grasper": 0, "scissors": 1}}
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+ {"episode_index": 5, "clip_id": "1_ZSjL3pNG", "task": "ENERGY_WORK", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 6, "clip_id": "1_eCfO9PIA", "task": "ENERGY_WORK", "tool_order": ["grasper", "scissors"], "tool_name_to_slot": {"grasper": 0, "scissors": 1}}
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+ {"episode_index": 7, "clip_id": "1_ihEhr17I", "task": "ENERGY_WORK", "tool_order": ["grasper", "scissors"], "tool_name_to_slot": {"grasper": 0, "scissors": 1}}
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+ {"episode_index": 8, "clip_id": "1_ilwQwWVG", "task": "ENERGY_WORK", "tool_order": ["grasper", "scissors"], "tool_name_to_slot": {"grasper": 0, "scissors": 1}}
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+ {"episode_index": 11, "clip_id": "1_lk4MzDLS", "task": "ENERGY_WORK", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 12, "clip_id": "1_wbNZsjBI", "task": "ENERGY_WORK", "tool_order": ["grasper", "scissors"], "tool_name_to_slot": {"grasper": 0, "scissors": 1}}
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+ {"episode_index": 13, "clip_id": "1_y6nO7JJA", "task": "ENERGY_WORK", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 14, "clip_id": "1_1jlvzsGo", "task": "PREPARE", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 15, "clip_id": "1_DpVk2qHP", "task": "PREPARE", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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+ {"episode_index": 16, "clip_id": "1_IOGIBkGA", "task": "PREPARE", "tool_order": ["scissors"], "tool_name_to_slot": {"scissors": 0}}
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
+ {"episode_index": 40, "clip_id": "1_5vXB30KW", "task": "SPLITTING", "tool_order": ["grasper", "hook"], "tool_name_to_slot": {"grasper": 0, "hook": 1}}
42
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43
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47
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50
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68
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70
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75
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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