gr00t_wbc

Software stack for loco-manipulation experiments across multiple humanoid platforms, with primary support for the Unitree G1. This repository provides whole-body control policies, a teleoperation stack, and a data exporter.

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System Installation

Prerequisites

• Ubuntu 22.04
• NVIDIA GPU with a recent driver
• Docker and NVIDIA Container Toolkit (required for GPU access inside the container)

Repository Setup

Install Git and Git LFS:

sudo apt update
sudo apt install git git-lfs
git lfs install

Clone the repository:

mkdir -p ~/Projects
cd ~/Projects
git clone https://github.com/NVlabs/gr00t_wbc.git
cd gr00t_wbc

Docker Environment

We provide a Docker image with all dependencies pre-installed.

Install a fresh image and start a container:

./docker/run_docker.sh --install --root

This pulls the latest gr00t_wbc image from docker.io/nvgear.

Start or re-enter a container:

./docker/run_docker.sh --root

Use --root to run as the root user. To run as a normal user, build the image locally:

./docker/run_docker.sh --build

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Running the Control Stack

Once inside the container, the control policies can be launched directly.

• Simulation:

  python gr00t_wbc/control/main/teleop/run_g1_control_loop.py

• Real robot: Ensure the host machine network is configured per the G1 SDK Development Guide and set a static IP at 192.168.123.222, subnet mask 255.255.255.0:

  python gr00t_wbc/control/main/teleop/run_g1_control_loop.py --interface real

Keyboard shortcuts (terminal window):

• ]: Activate policy
• o: Deactivate policy
• 9: Release / Hold the robot
• w / s: Move forward / backward
• a / d: Strafe left / right
• q / e: Rotate left / right
• z: Zero navigation commands
• 1 / 2: Raise / lower the base height
• backspace (viewer): Reset the robot in the visualizer

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Running the Teleoperation Stack

The teleoperation policy primarily uses Pico controllers for coordinated hand and body control. It also supports other teleoperation devices, including LeapMotion and HTC Vive with Nintendo Switch Joy-Con controllers.

Keep run_g1_control_loop.py running, and in another terminal run:

python gr00t_wbc/control/main/teleop/run_teleop_policy_loop.py --hand_control_device=pico --body_control_device=pico

Pico Setup and Controls

Configure the teleop app on your Pico headset by following the XR Robotics guidelines.

The necessary PC software is pre-installed in the Docker container. Only the XRoboToolkit-PC-Service component is needed.

Prerequisites: Connect the Pico to the same network as the host computer.

Controller bindings:

• menu + left trigger: Toggle lower-body policy
• menu + right trigger: Toggle upper-body policy
• Left stick: X/Y translation
• Right stick: Yaw rotation
• L/R triggers: Control hand grippers

Pico unit test:

python gr00t_wbc/control/teleop/streamers/pico_streamer.py

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Running the Data Collection Stack

Run the full stack (control loop, teleop policy, and camera forwarder) via the deployment helper:

python scripts/deploy_g1.py \
    --interface sim \
    --camera_host localhost \
    --sim_in_single_process \
    --simulator robocasa \
    --image-publish \
    --enable-offscreen \
    --env_name PnPBottle \
    --hand_control_device=pico \
    --body_control_device=pico

The tmux session g1_deployment is created with panes for:

• control_data_teleop: Main control loop, data collection, and teleoperation policy
• camera: Camera forwarder
• camera_viewer: Optional live camera feed

Operations in the controller window (control_data_teleop pane, left):

• ]: Activate policy
• o: Deactivate policy
• k: Reset the simulation and policies
• `: Terminate the tmux session
• ctrl + d: Exit the shell in the pane

Operations in the data exporter window (control_data_teleop pane, right top):

• Enter the task prompt

Operations on Pico controllers:

• A: Start/Stop recording
• B: Discard trajectory