| # 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 | ee_positions | (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 | (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. | |