Unitree Simulation Stack¶

Unitree repos and tools for simulating the G1 in Isaac Sim.

Guides: TensorBoard — Training Monitoring · Sim-to-Real Deploy

Repo Overview¶

How they relate¶

Isaac Sim 5.1 (the rendering/physics engine)
  └── Isaac Lab 2.3.2 (the robotics RL framework)
       ├── unitree_rl_lab (RL training — locomotion, velocity control)
       └── unitree_sim_isaaclab (full sim — manipulation, teleoperation, DDS)

unitree_ros (robot URDF files — used by both)

Both Unitree repos are applications built on Isaac Lab. They share the same conda environment (isaaclab) and the same Isaac Sim installation.

unitree_rl_lab¶

Purpose: Train and evaluate RL locomotion policies for Unitree robots.

Supported robots: Go2, H1, G1-29DOF

Key commands:

# Train a G1 locomotion policy (headless, ~30 min on RTX 3090)
cd ~/GIT/unitree_rl_lab
python scripts/rsl_rl/train.py --headless --task Unitree-G1-29dof-Velocity

# Play back trained policy with GUI
python scripts/rsl_rl/play.py --task Unitree-G1-29dof-Velocity

# Monitor training (TensorBoard)
tensorboard --logdir logs/rsl_rl/ --host 0.0.0.0
# Then open http://workstation:6006

Training details:

• Runs 4096 parallel G1 robots on the GPU
• Uses PPO (Proximal Policy Optimization) via RSL-RL
• Checkpoints saved every 100 iterations to logs/rsl_rl/unitree_g1_29dof_velocity/<timestamp>/
• Produces both .pt (PyTorch) and .onnx (portable) policy files
• The .onnx gets deployed on the real G1's Jetson Orin

What the locomotion policy does:

Velocity command (vx, vy, yaw_rate)  →  Locomotion Policy (neural network)  →  29 joint actions

The policy converts a velocity command ("walk forward at 0.5 m/s") into the 29 joint positions/torques needed to walk without falling. Input source doesn't matter — keyboard, joystick, VR headset, or autonomous planner.

unitree_sim_isaaclab¶

Purpose: Full simulation environment for sim-to-real transfer. Uses the same DDS communication protocol as the physical G1.

Key features:

• Manipulation tasks (pick-and-place, stacking) with G1 + dexterous hands
• DDS communication (same as real robot — code transfers directly)
• Pre-trained ONNX locomotion policy for walking (lower body)
• Teleoperation pipeline (VR headset, keyboard via DDS)
• Data collection and replay for imitation learning
• Camera streaming (for vision-based policies)

Pre-trained models: Downloaded via fetch_assets.sh from HuggingFace (~1.2 GB). Located at assets/model/policy.onnx.

When to use: Full sim-to-real pipeline with DDS, manipulation, or teleoperation. For locomotion training only, use unitree_rl_lab.

unitree_ros¶

Purpose: URDF robot description files for all Unitree robots.

A URDF describes a robot's physical structure — links, joints, meshes, and physical properties (mass, inertia). Isaac Sim reads the URDF to build the robot in simulation.

G1 URDF variants available:

Installation¶

Prerequisites¶

• Isaac Sim 5.1 (pip install in isaaclab conda env)
• Isaac Lab 2.3.2 (pip install in same env)

Step 1: Clone repos¶

cd ~/GIT
git clone https://github.com/unitreerobotics/unitree_rl_lab.git
git clone https://github.com/unitreerobotics/unitree_ros.git
git clone https://github.com/unitreerobotics/unitree_sim_isaaclab.git  # optional

Step 2: Configure URDF path¶

Edit unitree_rl_lab/source/unitree_rl_lab/unitree_rl_lab/assets/robots/unitree.py:

UNITREE_ROS_DIR = "/home/zeul/GIT/unitree_ros"

Switch the G1 29DOF config from USD to URDF spawner — uncomment UnitreeUrdfFileCfg, comment out UnitreeUsdFileCfg.

Step 3: Install unitree_rl_lab¶

conda activate isaaclab
cd ~/GIT/unitree_rl_lab
pip install -e "source/unitree_rl_lab"

Step 4 (optional): Install unitree_sim_isaaclab dependencies¶

conda activate isaaclab
pip install -r ~/GIT/unitree_sim_isaaclab/requirements.txt
# Fix cryptography version conflict:
pip install cryptography==44.0.0

Download assets (USD models + pre-trained ONNX policy):

cd ~/GIT/unitree_sim_isaaclab
sudo apt install -y git-lfs  # if not already installed
bash fetch_assets.sh  # downloads ~1.2 GB from HuggingFace

Key Concepts¶

Policy formats¶

Both contain the same neural network. ONNX is a portable export format (like PDF for documents). PyTorch is the native training format.

DDS (Data Distribution Service)¶

The networking layer under ROS 2. Normally invisible. Matters here because:

• Isaac Sim host uses FastDDS (default in ROS 2 Jazzy)
• G1 robot uses CycloneDDS
• unitree_sim_isaaclab uses DDS directly (bypasses ROS 2 for lower latency)
• Host-to-container communication requires FastDDS shared memory disabled (UDP-only)

URDF vs USD¶

Isaac Sim supports both. We use URDF because Unitree provides their robots as URDF.