RoboChallenge/Table30v2

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.
Master Joint effort	master_efforts	(7,)	Effort of 6 master joints and gripper. (Provided by official API, precision not guaranteed.
Master joint control	master_qpos	(6,)	Master joint angle (in radians) from the base to the end effector.
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==0.1.0
  • opencv-python
  • numpy
• Configure $LEROBOT_HOME (defaults to ~/.lerobot if unset).

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):

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.