Unitree G1#

← Tutorial Library · Other Hardware and teleoperation

This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

<img src=”https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg” alt=”Unitree G1 locomanipulation demo” style={{ width: “100%” }} />

The Unitree G1 humanoid is now supported in LeRobot! You can teleoperate, train locomanipulation policies, test in sim, and more. Both 29 and 23 DoF variants are supported.

Part 1: Getting Started#

Install the Unitree SDK#

Follow the unitree_sdk2_python installation guide. Tested with unitree_sdk2py==1.0.1 and cyclonedds==0.10.2:

conda create -y -n lerobot python=3.12
conda activate lerobot
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip install -e .
cd ..

Install LeRobot#

conda install ffmpeg -c conda-forge
conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e '.[unitree_g1]'

Tips

For now, pinocchio must be installed from conda-forge (not pip) to include the CasADi bindings needed for arm IK.

Test the Installation (Simulation)#

The simulation environment has its own dependencies. Check the Simulation environment dependencies: Unitree G1 Mujoco EnvHub.

pip install mujoco loguru msgpack msgpack-numpy

lerobot-teleoperate \
  --robot.type=unitree_g1 \
  --robot.is_simulation=true \
  --teleop.type=unitree_g1 \
  --teleop.id=wbc_unitree \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30, "warmup_s": 5}}' \
  --display_data=true \
  --robot.controller=GrootLocomotionController

This will launch a MuJoCo sim instance for the G1. You can connect a gamepad to your machine before launching in order to control the robot’s locomotion in sim. We support both HolosomaLocomotionController and GrootLocomotionController via --robot.controller.

  • Press 9 to release the robot

  • Press 7 / 8 to increase / decrease waist height

Connect to the Physical Robot#

The G1’s Ethernet IP is fixed at 192.168.123.164. Your machine must have a static IP on the same subnet: 192.168.123.x where x ≠ 164.

# Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
sudo ip addr flush dev enp131s0
sudo ip addr add 192.168.123.200/24 dev enp131s0
sudo ip link set enp131s0 up

SSH into the Robot#

ssh unitree@192.168.123.164
# Password: 123

Share Internet via Ethernet#

The G1 needs internet access to clone repos and install packages. Share your laptop’s connection over Ethernet:

On your laptop:

sudo sysctl -w net.ipv4.ip_forward=1

# Replace wlp132s0f0 with your WiFi interface name
sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT

On the G1:

sudo ip route del default 2>/dev/null || true
sudo ip route add default via 192.168.123.200 dev eth0
echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf

# Verify
ping -c 3 8.8.8.8

Install the Unitree SDK on the G1#

Follow the unitree_sdk2_python installation guide:

conda create -y -n lerobot python=3.12
conda activate lerobot
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
python -m pip install -e .
cd ..

Install LeRobot on the G1#

git clone https://github.com/huggingface/lerobot.git
cd lerobot
conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
python -m pip install -e '.[unitree_g1]'

Tips

For now, pinocchio must be installed from conda-forge (not pip) to include the CasADi bindings needed for arm IK.

(Optional) Enable WiFi on the Robot#

For wireless SSH access, you can enable WiFi on the G1 (it’s blocked by default):

sudo rfkill unblock all
sudo ip link set wlan0 up
sudo nmcli radio wifi on
sudo nmcli device set wlan0 managed yes
sudo systemctl restart NetworkManager

Connect to a WiFi network:

nmcli device wifi list

sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
sudo nmcli connection up "YourNetwork"

ip a show wlan0

You can then SSH over WiFi instead of Ethernet:

ssh unitree@<ROBOT_WIFI_IP>
# Password: 123

Part 2: Teleoperation & Locomotion#

Run the Robot Server#

On the robot (from ~/lerobot):

cd ~/lerobot
python src/lerobot/robots/unitree_g1/run_g1_server.py --camera

Run the Locomotion Policy#

You can run the teleoperation client from your laptop over Ethernet, over WiFi (experimental), or directly on the robot itself. Mind potential latency introduced by your network.

From your laptop:

lerobot-teleoperate \
  --robot.type=unitree_g1 \
  --robot.is_simulation=false \
  --robot.robot_ip=<ROBOT_IP> \
  --teleop.type=unitree_g1 \
  --teleop.id=wbc_unitree \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
  --display_data=true \
  --robot.controller=HolosomaLocomotionController

We support both GrootLocomotionController and HolosomaLocomotionController via --robot.controller.

Part 3: Loco-Manipulation with the Homunculus Exoskeleton#

We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Check it out here.

Calibrate#

lerobot-calibrate \
  --teleop.type=unitree_g1 \
  --teleop.left_arm_config.port=/dev/ttyACM1 \
  --teleop.right_arm_config.port=/dev/ttyACM0 \
  --teleop.id=exo

During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press n to advance.

Record a Dataset#

lerobot-record \
  --robot.type=unitree_g1 \
  --robot.is_simulation=true \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
  --teleop.type=unitree_g1 \
  --teleop.left_arm_config.port=/dev/ttyACM1 \
  --teleop.right_arm_config.port=/dev/ttyACM0 \
  --teleop.id=exo \
  --dataset.repo_id=your-username/dataset-name \
  --dataset.single_task="Test" \
  --dataset.num_episodes=2 \
  --dataset.episode_time_s=5 \
  --dataset.reset_time_s=5 \
  --dataset.push_to_hub=true \
  --dataset.streaming_encoding=true \
  --dataset.encoder_threads=2

Note: Omit --teleop.left_arm_config.port and --teleop.right_arm_config.port if you’re only using the joystick.

Example dataset: nepyope/unitree_box_move_blue_full

Part 4: Training & Inference#

Train#

python src/lerobot/scripts/lerobot_train.py \
  --dataset.repo_id=your-username/dataset-name  \
  --policy.type=pi05 \
  --output_dir=./outputs/pi05_training \
  --job_name=pi05_training \
  --policy.repo_id=your-username/your-repo-id \
  --policy.pretrained_path=lerobot/pi05_base \
  --policy.compile_model=true \
  --policy.gradient_checkpointing=true \
  --wandb.enable=true \
  --policy.dtype=bfloat16 \
  --policy.freeze_vision_encoder=false \
  --policy.train_expert_only=false \
  --steps=3000 \
  --policy.device=cuda \
  --batch_size=32

Inference with RTC#

Once trained, we recommend deploying policies using inference-time RTC:

lerobot-rollout \
  --strategy.type=base \
  --policy.path=your-username/your-repo-id \
  --policy.device=cuda \
  --robot.type=unitree_g1 \
  --robot.is_simulation=false \
  --robot.controller=HolosomaLocomotionController \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
  --task="task_description" \
  --duration=1000 \
  --fps=30 \
  --inference.type=rtc

Additional Resources#

Last updated: March 2026