LejuRobotics/let_dataset

LET:Full-Size Humanoid Robot Real-World Dataset

中文| [English]

LET Dataset is collected based on the full-size humanoid robot Kuavo 4 Pro covering real-world multi-task data across multiple scenarios and operation types. It is designed for robot manipulation, mobility, and interaction tasks, supporting scalable robot learning in real environments.

📋 Table of Contents

• Key Features
• Hardware Platform
• Usage Guide
  • Tool Repository
• Tasks and Data Overview
  • Semantic Labels
  • Data Statistics
• Dataset
  • Dataset Directory Structure
  • Data Format
  • Label Format
• Data Access
• Data Communication Group
• Citation
• License

✨ Key Features

• Large-scale, real-world, full-size humanoid robot multi-view, multi-modal data, continuously updated
• Covers multiple domains including industry, home, medical, and service, with 31 sub-task scenarios
• Includes 117 atomic skills such as grasping, bimanual operation, tool use, with a total duration of over 1000 hours
• Expert-labeled and human-verified data to ensure high quality
• Provides a complete toolchain from data conversion, model training to inference and validation

Assembly line sorting	Daily table cleaning	Assembly line sorting (dexterous hand)
Left hand camera view	Head camera view	Right hand camera view

🤖 Hardware Platform

The main hardware platform is Kuavo 4 Pro and its wheeled version, with the following features:

• Robot parameters: Height 1.66 m, weight 55 kg, supports hot-swappable batteries
• Motion control: 40 degrees of freedom, max walking speed 7 km/h, supports bipedal autonomous SLAM
• Generalization: Supports multi-modal large models (e.g., Pangu, DeepSeek, ChatGPT), with 20+ atomic skills

🚀 Usage Guide

Tool Repository

We provide a complete tool repository, including:

• Data conversion tool (rosbag2lerobot): Convert rosbag files to formats suitable for model training
• Two imitation learning models: Diffusion Policy and ACT
• Model training scripts
• Code and deployment instructions for both real robots and simulation environments

For details, see the open-source repository: kuavo_data_challenge 🔥

🎬 Tasks and Data Overview

This dataset covers various scenarios such as automobile factories, FMCG, hotel services, 3C factories, life services, logistics, etc., including multi-modal observations (RGB, Depth, joints, etc.) and a rich set of atomic skills (grasping, bimanual operation, tool use, etc.).

Consumer goods sorting

Simulation data demonstration

Assembly feeding

Semantic Labels

The LET dataset decomposes complex tasks into a series of atomic action steps with clear semantics, using standardized annotation methods to provide sub-task level timelines and natural language annotations for each task.

Each data entry is accompanied by multi-dimensional semantic label information, including:

• Object labels: industrial parts, tableware, daily utensils, medicines, etc.
• Skill labels: grasp, place, rotate, push, pull, press, etc.
• Task and scene identifiers: unified task name coding, scene dimension distinguishes operation context semantics
• End effector type: records actions performed by gripper and dexterous hand separately
• Language description: e.g., "Pick up the medicine box from the conveyor belt and place it on the designated tray", supporting natural language and action alignment modeling

Data Statistics

LET dataset statistics are as follows:

Data type & Scene distribution

Data type distribution	Scene distribution

Task distribution

Task duration distribution

Distribution of atomic skills

📦 Dataset

---

Dataset Directory Structure

.
├── hdf5
│   ├── real
│   │   ├── Labelled
│   │   │   ├── customer_check_in-P4-dex_hand
│   │   │   ├── deliver_room_card-P4-dex_hand
│   │   │   ├── deliver_water_bottle-P4-dex_hand
│   │   │   ├── loading_of_large_tooling-P4-dex_hand
│   │   │   ├── loading_of_small_tooling-P4-dex_hand
│   │   │   ├── more_coil_sorting-P4-dex_hand
│   │   │   ├── more_FMCG_loading-P4-dex_hand
│   │   │   ├── more_goods_orders-P4-dex_hand
│   │   │   ├── more_scan_code_for_weighing-P4-dex_hand
│   │   │   ├── parts_offline-P4-dex_hand
│   │   │   ├── quick_sort-P4-leju_claw
│   │   │   ├── rubbish_sorting-P4-leju_claw
│   │   │   ├── shop_oversale-P4-leju_claw
│   │   │   ├── single_coil_sorting-P4-dex_hand
│   │   │   ├── single_FMCG_loading-P4-dex_hand
│   │   │   ├── single_goods_orders-P4-dex_hand
│   │   │   ├── single_scan_code_for_weighing-P4-dex_hand
│   │   │   ├── SPS_parts_grab-P4-leju_claw
│   │   │   ├── SPS_parts_sorting-P4-dex_hand
│   │   │   └── task_mass_check-P4-leju_claw
│   │   └── Unlabelled
│   │       ├── assembly_line_sorting-P4-leju_claw
│   │       ├── clothing_storage-P4-leju_claw
│   │       ├── countertop_cleaning-P4-leju_claw
│   │       ├── deliver_room_card-P4-dex_hand
│   │       ├── desktop_decluttering-P4-leju_claw
│   │       ├── drug_finishing-P4-leju_claw
│   │       ├── express_delivery_sorting-P4-leju_claw
│   │       ├── express_logistics_scenario-P4-leju_claw
│   │       ├── loading_of_large_tooling-P4-dex_hand
│   │       ├── loading_of_small_tooling-P4-dex_hand
│   │       ├── loading_of_small_tooling-P4-leju_claw
│   │       ├── more_coil_sorting-P4-dex_hand
│   │       ├── more_FMCG_loading-P4-dex_hand
│   │       ├── more_goods_orders-P4-dex_hand
│   │       ├── more_goods_orders-P4-leju_claw
│   │       ├── more_scan_code_for_weighing-P4-dex_hand
│   │       ├── parts_offline-P4-dex_hand
│   │       ├── parts_off_line-P4-leju_claw
│   │       ├── quick_sort-P4-leju_claw
│   │       ├── rubbish_sorting-P4-leju_claw
│   │       ├── shop_oversale-P4-leju_claw
│   │       ├── single_coil_sorting-P4-dex_hand
│   │       ├── single_FMCG_loading-P4-leju_claw
│   │       ├── single_goods_orders-P4-dex_hand
│   │       ├── SMT_tray_rack_blanking-P4-leju_claw
│   │       ├── SPS_parts_grab-P4-leju_claw
│   │       ├── SPS_parts_sorting-P4-dex_hand
│   │       ├── SPS_parts_sorting-P4-leju_claw
│   │       ├── standardized_feeding_for_FMCG-P4-dex_hand
│   │       └── task_mass_check-P4-leju_claw
│   └── sim
│       └── Unlabelled
│           ├── bottle_flip-P4-claw(Rq2f85)
│           ├── package_weighing-P4-claw(Rq2f85)
│           ├── SPS_parts_sorting-P4-claw(Rq2f85)
│           └── target_placement-P4-claw(Rq2f85)
└── rosbag
    ├── real
    │   ├── Labelled  // Same task structure as HDF5.
    │   └── Unlabelled // Same task structure as HDF5.
    └── sim
        └── Unlabelled // Same task structure as HDF5.

Data Format

ROSbag Data Format

Topic Type	Topic Name	Message Type	Main Fields / Description
Camera RGB Image	/cam_x/color/image_raw/compressed	sensor_msgs/CompressedImage	x is h/l/r, for head/left wrist/right wrist camera respectively; header (message header with timestamp, sequence, frame, etc.), format (image encoding format), data (image data)
Camera Depth Image	/cam_x/depth/image_rect_raw/compressed	sensor_msgs/CompressedImage	x is h/l/r, for head/left wrist/right wrist camera respectively; header (message header), format (encoding format), data (image data)
Arm Trajectory Control	/kuavo_arm_traj	sensor_msgs/JointState	header (message header), name (joint name list, 14 joints, arm_joint_1~arm_joint_14), position (desired joint position, structure same as raw sensor data items 12-25)
Raw Sensor Data	/sensors_data_raw	kuavo_msgs/sensorsData	sensor_time (timestamp), joint_data (joint data: position, velocity, acceleration, current), imu_data (IMU data: gyroscope, accelerometer, quaternion), end_effector_data (end effector data, currently unused)
Dexterous Hand Position (Real Robot)	/control_robot_hand_position	kuavo_msgs/robotHandPosition	left_hand_position (left hand 6D, 0 open, 100 closed), right_hand_position (right hand 6D, 0 open, 100 closed)
Dexterous Hand State (Real Robot)	/dexhand/state	sensor_msgs/JointState	name (12 joint names), position (12 joint positions, first 6 for left hand, last 6 for right hand), velocity (12 joint velocities), effort (12 joint currents)
Gripper Control (Real Robot)	/leju_claw_command	kuavo_msgs/leju_claw_command	name (length 2, left_claw/right_claw), position (length 2, 0 open, 100 closed), velocity (length 2, target velocity, default 50), effort (length 2, target current in A, default 1)
Gripper State (Real Robot)	/leju_claw_state	kuavo_msgs/lejuClawState	state (int8[2], left/right gripper state, see details below), data (kuavo_msgs/endEffectorData, contains gripper position, velocity, current)
Simulation Gripper Control	/gripper/command	sensor_msgs/JointState	header (message header), position (length 2, 0 open, 255 closed)
Simulation Gripper State	/gripper/state	sensor_msgs/JointState	header (message header), position (length 2, 0 open, 0.8 closed)
Robot Position Command	/cmd_pose_world	geometry_msgs/Twist	linear.x/y/z (translation in world frame in m), angular.x/y/z (rotation in world frame in radians)

Detailed Field Descriptions

• /cam_x/color/image_raw/compressed、/cam_x/depth/image_rect_raw/compressed：

  • header（std_msgs/Header）：Message header with timestamp, sequence number, frame information
  • format（string）：Image encoding format
  • data（uint8[]）：Image data

• /kuavo_arm_traj：

  • header：Message header
  • name：Joint name list, 14 joints named arm_joint_1~arm_joint_14
  • position：Desired joint position, structure same as raw sensor data items 12-25

• /sensors_data_raw：

  • sensor_time（time）：Timestamp
  • joint_data（kuavo_msgs/jointData）：Joint data including position, velocity, acceleration, current
    • Data order：

      • First 12 items are lower limb motor data:

        • Indices 0–5: left leg
          (l_leg_roll, l_leg_yaw, l_leg_pitch, l_knee, l_foot_pitch, l_foot_roll)
        • Indices 6–11: right leg
          (r_leg_roll, r_leg_yaw, r_leg_pitch, r_knee, r_foot_pitch, r_foot_roll)

      • Next 14 items are arm motor data:

        • Indices 12–18: left arm
          (l_arm_pitch, l_arm_roll, l_arm_yaw, l_forearm_pitch, l_hand_yaw, l_hand_pitch, l_hand_roll)
        • Indices 19–25: right arm
          (r_arm_pitch, r_arm_roll, r_arm_yaw, r_forearm_pitch, r_hand_yaw, r_hand_pitch, r_hand_roll)

      • Last 2 items are head motor data: head_yaw, head_pitch

    • Units: position in radians, velocity in radian/s, acceleration in radian/s², current in Amperes (A)
  • imu_data（kuavo_msgs/imuData）：IMU data including gyroscope (gyro, unit rad/s), accelerometer (acc, unit m/s²), quat (IMU orientation)
  • end_effector_data（kuavo_msgs/endEffectorData）：End effector data, currently unused

• /control_robot_hand_position：

  • left_hand_position（float[6]）：Left hand 6D, each element [0,100], 0 fully open, 100 fully closed
  • right_hand_position（float[6]）：Right hand 6D, same meaning as above

• /dexhand/state：

  • name（string[12]）：12 joint names
  • position（float[12]）：12 joint positions, first 6 for left hand, last 6 for right hand
  • velocity（float[12]）：12 joint velocities, first 6 for left hand, last 6 for right hand
  • effort（float[12]）：12 joint currents, first 6 for left hand, last 6 for right hand

• /leju_claw_command：

  • name（string[2]）：left_claw, right_claw
  • position（float[2]）：Left/right gripper target position, [0,100], 0 open, 100 closed
  • velocity（float[2]）：Target velocity, [0,100], default 50
  • effort（float[2]）：Target current in A, default 1

• /leju_claw_state：

  • state（int8[2]）：Left/right gripper state, meanings as follows:
    • -1：Error (execution anomaly)
    • 0：Unknown (default initialization state)
    • 1：Moving
    • 2：Reached target position
    • 3：Object grasped
  • data（kuavo_msgs/endEffectorData）：Contains gripper position, velocity, current, structure same as /leju_claw_command

• /gripper/command（Simulation）：

  • header：Message header
  • position（float[2]）：Left/right gripper target position, [0,255], 0 open, 255 closed

• /gripper/state（Simulation）：

  • header：Message header
  • position（float[2]）：Left/right gripper current position, [0,0.8], 0 open, 0.8 closed

• /cmd_pose_world（Simulation Task 4 only）：

  • linear.x/y/z（float）：Translation in world frame in meters
  • angular.x/y/z（float）：Rotation in world frame in radians

HDF5 Data Format

<task_root>
├── cameras
│   ├── hand_left  // Left hand camera
│   │   ├── color     // RGB image info
│   │   │   └── data   // RGB image data (by timestamp)
│   │   └── depth/     // Depth image info
│   │       └── data   // Depth data
│   ├── hand_right // Right hand camera
│   │   ├── color // RGB image info
│   │   │   └── data // RGB data
│   │   └── depth  // Depth image info
│   │       └── data // Depth data
│   └── head // Head camera
│       ├── color // RGB image info
│       │   └── data // RGB image data
│       └── depth // Depth image info
│           └── data // Depth data
├── joints  // Joint data
│   ├── action  // Desired joint values
│   │   ├── arm // Arm
│   │   │   ├── position   // N(rows)*14(cols); N=frames, 14=DoF for both arms (7 per arm)
│   │   │   └── velocity   // Desired joint velocity
│   │   ├── effector // End effector
│   │   │   └── position   //  N(rows)*2(cols); N=frames, 2=left/right gripper open/close
│   │   ├── head // Head
│   │   │   ├── position   //  N(rows)*2(cols); N=frames, 2=2 DoF (pitch/yaw)
│   │   │   └── velocity  // Joint velocity
│   │   └── leg // Leg 
│   │       ├── position   //  N(rows)*12(cols)
│   │       └── velocity  // Joint velocity 
│   └── state  // Actual joint values
│       ├── arm // Arm
│       │   ├── position // N(rows)*14(cols); N=frames, 14=DoF for both arms (7 per arm)
│       │   └── velocity // Joint velocity
│       ├── effector // End effector
│       │   └── position //  N(rows)*2(cols); N=frames, 2=left/right gripper open/close
│       ├── head // Head
│       │   ├── position  // N(rows)*2(cols); N=frames, 2=2 DoF (pitch/yaw)
│       │   └── velocity   // Joint velocity
│       └── leg // Leg
│           ├── position //  N(rows)*12(cols)
│           └── velocity // Joint velocity
├── parameters  // Sensor extrinsics
│   └── camera
│       ├── hand_left.json   # Left hand camera intrinsics/extrinsics
│       ├── hand_right.json  # Right hand camera intrinsics/extrinsics
│       └── head.json        # Head camera intrinsics/extrinsics
└── metadata.json            # Collection metadata: device, end effector type, camera frame rate, joint info, etc.

Label Format

Label information is stored in a JSON file with the same name as the data file. Example:

{
  "loaction": "Yangtze River Delta Integrated Demonstration Zone Intelligent Robot Training Center",
  "primaryScene": "Default primary scene",
  "primarySceneCode": "default_level_one_scene",
  "secondaryScene": "3C factory scene",
  "secondarySceneCode": "3C factory manufacturing",
  "tertiaryScene": "Coil sorting",
  "tertiarySceneCode": "Coil sorting",
  "initSceneText": "Coils of various colors are placed in the middle of the table, material boxes are placed on both sides of the table, and the robot is located at the back of the table",
  "englishInitSceneText": "Coils of various colors are placed in the middle of the table, material boxes are placed on both sides of the table, and the robot is located at the back of the table",
  "taskGroupName": "Single coil sorting",
  "taskGroupCode": "single_coil_sorting",
  "taskName": "7-22-Coil classification",
  "taskCode": "XQFL_11",
  "deviceSn": "P4-209",
  "taskPrompt": "",
  "marks": [
    {
      "taskId": "1947326026455584768",
      "markStart": "2025-07-22 9:18:39.640",
      "markEnd": "2025-07-22 9:18:39.814",
      "duration": 0.233,
      "startPosition": 0.7363737795977026,
      "endPosition": 0.769568869806783,
      "skillAtomic": "pick",
      "skillDetail": "Pick up the coil from the table",
      "enSkillDetail": "pick coil from table",
      "markType": "step"
    }
  ]
}

📥Data Access

• Official request: You can request access by contacting the official email wangsong@lejurobot.com.
• Public platforms: The LET dataset will be publicly released on major platforms such as Openloong, ModelScope, and Hugging Face to provide convenience for developers and researchers worldwide.

📋 Data Communication Group

• Data communication QQ group: 1043359345
  

📝 Citation

If you use this dataset in your research, please cite it according to the platform from which you accessed it:

Citation for Hugging Face

@misc{LET2025,
    title={LET:Full-Size Humanoid Robot Real-World Dataset},
    author={LejuRobotics},
    year={2025},
    howpublished={\url{https://huggingface.co/datasets/LejuRobotics/let_dataset}}
}

Citation for ModelScope

@misc{LET2025,
    title={LET:Full-Size Humanoid Robot Real-World Dataset},
    author={LejuRobotics},
    year={2025},
    howpublished={\url{https://www.modelscope.cn/datasets/LejuRobotics/let_dataset}}
}

📄 License

All the data and code within this repo are under CC BY-NC-SA-4.0.