| # RoboChallenge Table30 v2 Dataset |
|
|
| ## Tasks and Embodiments |
|
|
| The dataset includes 30 diverse manipulation tasks (Table30 v2) across 4 embodiments: |
|
|
| ### Available Tasks |
|
|
| - `put_the_books_back` - Place the books back onto the bookshelf. |
| - `tie_a_knot` - Tie a knot with the string on the table. |
| - `stamp_positioning` - Stamp the signature area on the paper. |
| - `tidy_up_the_makeup_table` - Sort and organize the cosmetics on the table. |
| - `paint_jam` - Spread the bread with jam. |
| - `pack_the_items` - Box up the tablet and its accessories. |
| - `wrap_with_a_soft_cloth` - Bundle the objects together using the cloth on the table. |
| - `put_in_pen_container` - Put the pens on the desk into the pen holder. |
| - `put_the_pencil_case_into_the_schoolbag` - Put the pencil case into the backpack. |
| - `put_the_shoes_back` - Pair the two pairs of shoes on the desk and place them on the shoe rack |
| - `untie_the_shoelaces` - Remove the laces from the shoes, then place them on the table. |
| - `scoop_with_a_small_spoon` - Scoop beans and place them into the empty bowl. |
| - `wipe_the_blackboard` - Wipe the balckboard clean. |
| - `lint_roller_remove_dirt` - Use a lint remover to clean the debris on the clothe. |
| - `turn_on_the_light_switch` - Turn on the lamp. |
| - `hold_the_tray_with_both_hands` - Place the ball on the desk onto the small tray, and then move it to the large tray. |
| - `fold_the_clothes` - Fold the T-shirts and stack them neatly in the upper-left corner of the table. |
| - `pack_the_toothbrush_holder` - Put the toothbrush and toothpaste into the toiletries case in sequence, close the case, and then place it into the basket. |
| - `place_objects_into_desk_drawer` - Open the drawer, put the bottle opener inside, and close the drawer. |
| - `sweep_the_trash` - Sweep the trash on the table into the dustpan. |
| - `arrange_flowers` - Put the 4 flowers into the vase. |
| - `press_the_button` - Press the buttons in the following sequence: pink, blue, green, and then yellow. |
| - `pick_out_the_green_blocks` - Find all the green blocks and put them into the basket. |
| - `hang_the_cup` - Hang the cup on the rack. |
| - `water_the_flowers` - Water the potted plants. |
| - `wipe_the_table` - Wipe the stains off the desk with a rag. |
| - `arrange_fruits` - Arrange the fruit in the basket. |
| - `shred_paper` - Put the paper into the shredder. |
| - `item_classification` - Place the stationery in the yellow box and the electronics in the blue box. |
| - `stack_bowls` - Put the blue bowl into the beige bowl, and put the green bowl into the blue bowl. |
|
|
| ### Embodiments |
|
|
| - **ARX5** - Single-arm with triple camera setup (wrist + global + right-side views) |
| - **UR5** - Single-arm with dual camera setup (wrist + global views) |
| - **ALOHA** - Dual-arm with triple wrist camera setup (left wrist + right wrist + global views) |
| - **DOS-W1** - Dual-arm with triple wrist camera setup (left wrist + right wrist + global views) |
|
|
| ## Dataset Structure |
|
|
| ### Hierarchy |
|
|
| The dataset is organized by tasks, with each task containing multiple demonstration episodes: |
|
|
| ``` |
| . |
| ├── <task_name>/ # e.g., arrange_the_flowers, fold_t_shirt |
| │ ├── task_desc.json # Task description |
| │ ├── meta/ # Task-level metadata |
| │ │ ├── task_info.json |
| │ └── data/ # Episode data |
| │ ├── episode_000000/ # Individual episode |
| │ │ ├── meta/ |
| │ │ │ └── episode_meta.json # Episode metadata |
| │ │ ├── states/ |
| │ │ │ # for single-arm (ARX5, UR5) |
| │ │ │ ├── states.jsonl # Single-arm robot states |
| │ │ │ # for dual-arm (ALOHA, DOS-W1) |
| │ │ │ ├── left_states.jsonl # Left arm states |
| │ │ │ └── right_states.jsonl # Right arm states |
| │ │ └── videos/ |
| │ │ # Video configurations varies by robot model: |
| │ │ # ARX5 |
| │ │ ├── cam_arm_rgb.mp4 # Wrist view |
| │ │ ├── cam_global_rgb.mp4 # Global view |
| │ │ └── cam_side_rgb.mp4 # Side view |
| │ │ # UR5 |
| │ │ ├── cam_global_rgb.mp4 # Global view |
| │ │ └── cam_arm_rgb.mp4 # Wrist view |
| │ │ # ALOHA |
| │ │ ├── cam_high_rgb.mp4 # Global view |
| │ │ ├── cam_left_wrist_rgb.mp4 # Left wrist view |
| │ │ └── cam_right_wrist_rgb.mp4 # Right wrist view |
| │ │ # DOS-W1 |
| │ │ ├── cam_high_rgb.mp4 # Global view |
| │ │ ├── cam_left_wrist_rgb.mp4 # Left wrist view |
| │ │ └── cam_right_wrist_rgb.mp4 # Right wrist view |
| │ ├── episode_000001/ |
| │ └── ... |
| ├── convert_to_lerobot.py # Conversion script |
| └── README.md |
| ``` |
|
|
| ### Metadata Schema |
|
|
| `task_info.json` |
|
|
| ```json |
| { |
| "task_desc": { |
| "task_name": "arrange_flowers", // Task identifier |
| "prompt": "Put the 4 flowers into the vase.", |
| "description": "...", |
| "scoring": "...", // Scoring criteria |
| "task_tag": [ // Task characteristics |
| "repeated", |
| "single-arm", |
| "ARX5", |
| "precise3d" |
| ] |
| }, |
| "video_info": { |
| "fps": 30, // Video frame rate |
| "ext": "mp4", // Video format |
| "encoding": { |
| "vcodec": "libx264", // Video codec |
| "pix_fmt": "yuv420p" // Pixel format |
| } |
| } |
| } |
| ``` |
|
|
| `episode_meta.json` |
|
|
| ```json |
| { |
| "start_time": 1750405586.3430033, // Unix timestamp (start) |
| "end_time": 1750405642.5247612, // Unix timestamp (end) |
| "frames": 1672, // Total video frames |
| "robot_id": "rc_arx5_5", // Robot identifier |
| "features": { |
| "cam_global": { // Camera name info |
| "intrinsics": [], // Intrinsics |
| "extrinsics": { // Extrinsics |
| "arms": { |
| "arm": [] // Extrinsic relative to arm |
| } |
| } |
| } |
| } |
| } |
| ``` |
|
|
| ### Robot States Schema |
|
|
| Each episode contains states data stored in JSONL format. Depending on the embodiment, the structure differs slightly: |
|
|
| - **Single-arm robots (ARX5, UR5)** → `states.jsonl` |
| - **Dual-arm robots (ALOHA, DOS-W1)** → `left_states.jsonl` and `right_states.jsonl` |
|
|
| Each file records the robot’s proprioceptive signals per frame, including joint angles, |
| end-effector poses, gripper states, and timestamps. The exact field definitions and coordinate conventions vary by |
| platform, |
| as summarized below. |
|
|
| #### ARX5 |
|
|
| | Data Name | Data Key | Shape | Semantics | |
| |:----------------:|:----------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | Joint control | joint_positions | (6,) | Joint angle (in radians) from the base to the end effector. | |
| | Joint velocity | joint_velocities | (6,) | Speed of 6 joint. | |
| | Joint effort | efforts | (7,) | Effort of 6 joints and gripper. (Provided by official API, precision not guaranteed. | |
| | Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. | |
| | Gripper control | gripper_width | (1,) | Actual gripper width measurement in meter. | |
| | Gripper velocity | gripper_velocity | (1,) | Speed of gripper. | |
| | Time stamp | timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | |
| |
| #### UR5 |
| |
| | Data Name | Data Key | Shape | Semantics | |
| |:---------------:|:---------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | Joint control | joint_positions | (6,) | Joint angle (in radians) from the base to the end effector. | |
| | Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. | |
| | Gripper control | gripper_width | (1,) | Actual gripper width measurement in meter. | |
| | Time stamp | timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | |
|
|
| #### DOS-W1 |
|
|
| | Data Name | Data Key | Shape | Semantics | |
| |:---------------:|:---------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | Joint control | joint_positions | (6,) | Joint angle (in radians) from the base to the end effector. | |
| | Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. | |
| | Gripper control | gripper_width | (1,) | Actual gripper width measurement in meter. | |
| | Time stamp | timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | |
| |
| #### ALOHA |
| |
| | Data Name | Data Key | Shape | Semantics | |
| |:--------------------:|:----------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
| | Joint control | joint_positions | (6,) | Puppet joint angle (in radians) from the base to the end effector. | |
| | Joint velocity | joint_velocities | (7,) | Speed of 6 joint. | |
| | Gripper control | gripper_width | (1,) | Actual gripper width measurement in meter. | |
| | Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. | |
| | Joint effort | efforts | (7,) | Effort of 6 joints and gripper. (Provided by official API, precision not guaranteed. | |
| | Time stamp | timestamp | (1,) | Floating point timestamp (in mileseconds) of each frame. | |
| |
| ## Convert to LeRobot |
| |
| While you can implement a custom Dataset class to read RoboChallenge data directly, **we strongly recommend converting |
| to LeRobot format** to take advantage of [LeRobot](https://github.com/huggingface/lerobot)'s comprehensive data |
| processing and loading utilities. |
| |
| The example script **`convert_to_lerobot.py`** converts **ARX5** data to the LeRobot dataset as a example. For other |
| robot embodiments (UR5, ALOHA, DOS-W1), you can adapt the script accordingly. |
| |
| ### Prerequisites |
| |
| - Python 3.9+ with the following packages: |
| - `lerobot@0cf864870cf29f4738d3ade893e6fd13fbd7cdb5` |
| - `opencv-python` |
| - `numpy` |
| - Configure `$HF_LEROBOT_HOME` (defaults to `~/.cache/huggingface/lerobot` if unset). |
| |
| ```bash |
| pip install git+https://github.com/huggingface/lerobot@0cf864870cf29f4738d3ade893e6fd13fbd7cdb5 opencv-python numpy |
| export HF_LEROBOT_HOME="/path/to/lerobot_home" |
| ``` |
| |
| ### Usage |
| |
| Run the converter from the repository root (or provide an absolute path): |
| |
| ```bash |
| python convert_to_lerobot.py \ |
| --repo-name example_repo \ |
| --raw-dataset /path/to/example_dataset \ |
| --frame-interval 1 |
| ``` |
| |
| ### Output |
| |
| - Frames and metadata are saved to `$HF_LEROBOT_HOME/<repo-name>`. |
| - At the end, the script calls `dataset.consolidate(run_compute_stats=False)`. If you require aggregated statistics, run |
| it with `run_compute_stats=True` or execute a separate stats job. |
| |
