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

{
  "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

{
  "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'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).

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):

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.