Install the data converter

February 17, 2026 · View on GitHub

OpenWBC

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An XR-based Robot Teleoperation and Data Collection System

This project implements whole-body control for the Unitree G1 robot: using Apple Vision Pro with avp_teleoperate to control the robot's upper body and the OpenHomie algorithm to control lower body movement. It also supports whole-body data collection functionality.

🎥 Demo Video

Demo

🔥🔥 News

  • (🔥 New) [2026/02/01] TrajBooster is accepted by ICRA-2026. 🎉🎉🎉

🚀 Don’t miss our related project TrajBooster — it’s built upon OpenWBC and boosts VLA on humanoid whole-body manipulation via trajectory-centric learning!
If you like this repo, please consider citing or starring it too 🙌

🚀 Key Features

  • Dual-mode Control: Upper body teleoperation + Lower body autonomous locomotion
  • Real-time Control: Low-latency control based on Apple Vision Pro
  • Whole-body Data Collection: Complete robot motion data collection support
  • Modular Design: Independent deployment of upper or lower body control
  • Cross-platform Communication: TCP/IP network communication architecture

📋 TODO List

We plan to support the following features in future versions:

  • Data Format Conversion: Convert collected data to LeRobot format (Available in OpenWBC_to_Lerobot submodule)
  • VLA Training Integration: Support training NVIDIA GR00T N1.5 (Available in gr00t_modified_for_OpenWBC submodule)

🤖 Future AI Training Pipeline

We plan to implement a complete data collection to AI training pipeline:

  1. Data Collection: Use this system to collect whole-body motion data ✅ Implemented
  2. Format Conversion: Use OpenWBC_to_Lerobot to convert data to LeRobot format ✅ Implemented
  3. Model Training: Use NVIDIA Isaac GR00T to train full-body mobile manipulation models ✅ Implemented
  • 🛠️ any4lerobot: Collection of utilities for LeRobot, supporting multiple data format conversions
  • 🧠 NVIDIA Isaac GR00T: World's first open foundation model for generalized humanoid robot reasoning and skills

📋 System Requirements

Hardware Requirements

  • Unitree G1 Robot
  • Dex-3 Dexterous Hand (optional)
  • Apple Vision Pro
  • Development Host (Linux recommended, CUDA support)

Software Requirements

  • Python 3.8+
  • CMake 3.16+
  • GCC/G++ with C++14 support
  • Unitree SDK2
  • LeRobot (for data conversion and training)

🏗️ Installation Steps

1. Compile Unitree SDK2

For robot control, you need to compile g1_control.cpp (Unitree G1) and hand_control.cpp (Dex-3):

cd unitree_sdk2
rm -rf build
mkdir build && cd build
cmake ..
make

After compilation, executable files will be located in unitree_sdk2/build/bin.

2. Install g1_gym_deploy

cd g1_gym_deploy && pip install -e .

3. Install LeRobot (optional, for data conversion and training)

pip install lerobot

4. Initialize Data Conversion Submodule

For data format conversion functionality:

# Initialize and update submodule
git submodule update --init --recursive

# Install the data converter
cd OpenWBC_to_Lerobot
pip install -e .

⚙️ Network Configuration

Determine IP Addresses

Run the following command on both robot and PC to get IP addresses:

ifconfig | grep inet

Configure Network Addresses

Please set the IP addresses in the code to the correct values to ensure proper communication between robot and PC.

🎮 Deployment Process

Preparation Steps

⚠️ Important: Before deployment, please execute the following operations in sequence to close G1's initial control process:

  1. L1 + A
  2. L2 + R2
  3. L2 + A (Robot will raise its arms upon success)
  4. L2 + B (Robot will lose force control upon success)

Robot-side Operations

Terminal 1: Start Robot Control Program

cd unitree_sdk2/build/bin && ./g1_control eth0
# If eth0 doesn't work, try eth1

Terminal 2: Start Policy Inference Thread

python g1_gym_deploy/scripts/deploy_policy.py

Terminal 3: Start Image Server (AVP Mode)

cd avp_teleoperate/teleop/image_server
python image_server.py

Robot Operations

  1. Place the robot on the ground
  2. Press the R2 button on the controller to make the robot stand
  3. Press R2 again to start control

📱 Apple Vision Pro Control & Data Collection

PC-side Operations

# Start G1 (29DoF) Robot + Dex3-1 Dexterous Hand control
cd avp_teleoperate/teleop
python teleop_data_collecting.py --arm=G1_29 --hand=dex3 --record

Parameter Description:

  • --arm=G1_29: Robot arm type (default value, can be omitted)
  • --hand=dex3: Dexterous hand type
  • --record: Enable data recording functionality

Data Collection Description

This system has modified AVP to support complete whole-body data collection:

  • 📹 Visual Data: Multi-angle camera feed collection
  • 🎯 Action Data: Complete joint angles and end-effector positions
  • 🤖 State Data: Robot pose, velocity, torque, etc.
  • 🕐 Temporal Synchronization: Precise time synchronization across all data streams

🔄 Data Format Conversion

Convert collected OpenWBC data to LeRobot format using the included converter:

cd OpenWBC_to_Lerobot

# Basic conversion
python convert_to_lerobot.py \
    --input_dir /path/to/openwbc/dataset \
    --output_dir ./lerobot_dataset \
    --dataset_name "pick_cola" \
    --robot_type "g1" \
    --fps 30

# Or use the installed command
wbc-convert --input_dir /path/to/dataset --output_dir ./output

For detailed usage instructions, see the OpenWBC_to_Lerobot README.

⚠️ Safety Precautions

  • 🔴 Warning: Please deploy the system only after fully understanding all file functions
  • First deployment should be tested in a safe, open environment
  • Ensure sufficient safety space around
  • Recommend experienced personnel supervision
  • Keep emergency stop button ready

📁 Project Structure

WBC_Deploy/
├── avp_teleoperate/          # Apple Vision Pro teleoperation 
├── OpenHomie/                # Lower body control algorithm
│   └── HomieDeploy/          # Deployment package
│       ├── unitree_sdk2/     # Unitree SDK2
│       └── g1_gym_deploy/    # Deployment scripts
├── OpenWBC_to_Lerobot/       # Data format conversion tools (submodule)
│   ├── convert_to_lerobot.py # Main conversion script
│   ├── modality.json         # Robot modality configuration
│   ├── requirements.txt      # Python dependencies
│   └── README.md             # Conversion tool documentation
├── demos_all.gif            # Demo animation
└── README.md                 # This document

👏 Acknowledgements

📜 License

Please refer to the license terms of the related sub-projects.

🤝 Contributing

Welcome to submit Issues and Pull Requests to improve this project.

📖 Citation

If you find our work helpful, please consider citing:

@article{liu2025trajbooster,
  title={TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning},
  author={Liu, Jiacheng and Ding, Pengxiang and Zhou, Qihang and Wu, Yuxuan and Huang, Da and Peng, Zimian and Xiao, Wei and Zhang, Weinan and Yang, Lixin and Lu, Cewu and Wang, Donglin},
  journal={arXiv preprint arXiv:2509.11839},
  year={2025}
}