# Unitree G1

<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](https://github.com/unitreerobotics/unitree_sdk2_python#installation). Tested with `unitree_sdk2py==1.0.1` and `cyclonedds==0.10.2`:

```bash
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

```bash
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]'
```

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

### Test the Installation (Simulation)

The simulation environment has its own dependencies. Check the Simulation environment dependencies: [Unitree G1 Mujoco EnvHub](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main).

```bash
pip install mujoco loguru msgpack msgpack-numpy
```

```bash
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](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) 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](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl) 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`.

```bash
# 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

```bash
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:**

```bash
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:**

```bash
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](https://github.com/unitreerobotics/unitree_sdk2_python#installation):

```bash
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

```bash
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]'
```

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

### (Optional) Enable WiFi on the Robot

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

```bash
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:**

```bash
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:

```bash
ssh unitree@<ROBOT_WIFI_IP>
# Password: 123
```

---

## Part 2: Teleoperation & Locomotion

### Run the Robot Server

On the robot (from `~/lerobot`):

```bash
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:**

```bash
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](https://github.com/NVlabs/GR00T-WholeBodyControl) and [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) 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](https://github.com/nepyope/hmc_exo).

### Calibrate

```bash
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

```bash
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](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)

---

## Part 4: Training & Inference

### Train

```bash
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:

```bash
python examples/rtc/eval_with_real_robot.py \
  --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

- [Unitree SDK Documentation](https://github.com/unitreerobotics/unitree_sdk2_python)
- [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
- [Holosoma](https://github.com/amazon-far/holosoma)
- [LeRobot Documentation](https://github.com/huggingface/lerobot)
- [Unitree IL LeRobot](https://github.com/unitreerobotics/unitree_IL_lerobot)

---

_Last updated: March 2026_
