|
|
| # RoboChallenge Dataset |
| ## Tasks and Embodiments |
| The dataset includes 30 diverse manipulation tasks (Table30) across 4 embodiments: |
|
|
| ### Available Tasks |
| - `arrange_flowers` |
| - `arrange_fruits_in_basket` |
| - `arrange_paper_cups` |
| - `clean_dining_table` |
| - `fold_dishcloth` |
| - `hang_toothbrush_cup` |
| - `make_vegetarian_sandwich` |
| - `move_objects_into_box` |
| - `open_the_drawer` |
| - `place_shoes_on_rack` |
| - `plug_in_network_cable` |
| - `pour_fries_into_plate` |
| - `press_three_buttons` |
| - `put_cup_on_coaster` |
| - `put_opener_in_drawer` |
| - `put_pen_into_pencil_case` |
| - `scan_QR_code` |
| - `search_green_boxes` |
| - `set_the_plates` |
| - `shred_scrap_paper` |
| - `sort_books` |
| - `sort_electronic_products` |
| - `stack_bowls` |
| - `stack_color_blocks` |
| - `stick_tape_to_box` |
| - `sweep_the_rubbish` |
| - `turn_on_faucet` |
| - `turn_on_light_switch` |
| - `water_potted_plant` |
| - `wipe_the_table` |
| ### Embodiments |
| - **ARX5** - Single-arm with triple camera setup (wrist + global + right-side views) |
| - **UR5** - Single-arm with dual camera setup (wrist + global views) |
| - **FRANKA** - Single-arm with triple perspective setup (wrist + main + side views) |
| - **ALOHA** - 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_flowers, fold_dishcloth |
| │ ├── 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, Franka) |
| │ │ │ ├── states.jsonl # Single-arm robot states |
| │ │ │ # for dual-arm (ALOHA) |
| │ │ │ ├── left_states.jsonl # Left arm states |
| │ │ │ └── right_states.jsonl # Right arm states |
| │ │ └── videos/ |
| │ │ # Video configurations vary by robot model: |
| │ │ # ARX5 |
| │ │ ├── arm_realsense_rgb.mp4 # Wrist view |
| │ │ ├── global_realsense_rgb.mp4 # Global view |
| │ │ └── right_realsense_rgb.mp4 # Side view |
| │ │ # UR5 |
| │ │ ├── global_realsense_rgb.mp4 # Global view |
| │ │ └── handeye_realsense_rgb.mp4 # Wrist view |
| │ │ # Franka |
| │ │ ├── handeye_realsense_rgb.mp4 # Wrist view |
| │ │ ├── main_realsense_rgb.mp4 # Global view |
| │ │ └── side_realsense_rgb.mp4 # Side view |
| │ │ # ALOHA |
| │ │ ├── cam_high_rgb.mp4 # Global view |
| │ │ ├── cam_wrist_left_rgb.mp4 # Left wrist view |
| │ │ └── cam_wrist_right_rgb.mp4 # Right wrist view |
| │ ├── episode_000001/ |
| │ └── ... |
| ├── convert_to_lerobot.py # Conversion script |
| └── README.md |
| ``` |
| ### Metadata Schema |
| `task_info.json` |
| ```json |
| { |
| "robot_id": "arx5_1", // Robot model identifier |
| "task_desc": { |
| "task_name": "arrange_flowers", // Task identifier |
| "prompt": "insert the three flowers on the table into the vase one by one", |
| "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 |
| { |
| "episode_index": 0, // Episode number |
| "start_time": 1750405586.3430033, // Unix timestamp (start) |
| "end_time": 1750405642.5247612, // Unix timestamp (end) |
| "frames": 1672 // Total video frames |
| } |
| ``` |
|
|
| ### 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, Franka)** → `states.jsonl` |
| - **Dual-arm robots (ALOHA)** → `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. | |
| | Pose control | end_effector_pose | (6,) | End effector pose (tx, ty, tz, roll, pitch, yaw), where (roll, pitch, yaw) is relative euler angles from the arm base coordinate. X : back to front; Y: right to left; Z: down to up. | |
| | Gripper control |gripper_width | (1,) | Actual gripper width measurement in meter. | |
| | 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. X : front to back; Y: left to right; Z: down to up. | |
| | Gripper control |gripper | (1,) | Gripper closing angle, 0 for fully open, 255 for fully closed. | |
| | Time stamp |timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | |
|
|
| #### Franka |
| | Data Name | Data Key |Shape | Semantics | |
| |:---------:|:-----:|:----:|:----:| |
| | Joint control |joint_positions | (7,) | 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. X : back to front; Y: right to left; Z: down to up. | |
| | Gripper control |gripper | (2,) | Gripper trigger signals in the (close_button, open_button) order. | |
| | Gripper width |gripper_width | (1,) | Actual gripper width measurement | |
| | Time stamp |timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | |
| |
| |
| #### ALOHA |
| | Data Name | Data Key |Shape | Semantics | |
| |:---------:|:-----:|:----:|:----:| |
| | Master joint control |joint_positions | (6,) | Maste joint angle (in radians) from the base to the end effector. | |
| |Joint velocity| joint_vel | (7,) | Speed of 6 joint and gripper | |
| | Puppet joint control |qpos | (6,) | Puppet joint angle (in radians) from the base to the end effector. | |
| | Puppet pose control | ee_pose_quaternion | (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. X : back to front; Y: right to left ; Z: down to up. | |
| | Puppet pose control | ee_pose_rpy | (6,) | End effector pose (tx, ty, tz, rr, rp, ry), where (tx, ty, tz) is relative position from the arm base coordinate , (rr, rp, ry) is euler (in radians). X : back to front; Y: right to left ; Z: down to up. | |
| | Gripper control |gripper | (1,) | Actual gripper width measurement in meter.| |
| | 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, Franka, ALOHA), you can adapt the script accordingly. |
| |
| ### Prerequisites |
| - Python 3.9+ with the following packages: |
| - `lerobot==0.1.0` |
| - `opencv-python` |
| - `numpy` |
| - Configure `$LEROBOT_HOME` (defaults to `~/.lerobot` if unset). |
|
|
| ```bash |
| pip install lerobot==0.1.0 opencv-python numpy |
| export LEROBOT_HOME="/path/to/lerobot_home" |
| ``` |
|
|
| ### Usage |
|
|
| Run the converter from the repository root (or provide an absolute path): |
|
|
| ```bash |
| python convert_to_lerobot.py \ |
| --repo-name example_repo \ |
| --raw-dataset /path/to/example_dataset \ |
| --frame-interval 1 |
| ``` |
|
|
| ### Output |
| - Frames and metadata are saved to `$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. |
|
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