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id	title	status	source_sections	related_topics	key_equations	key_terms	images	examples	open_questions
dev-environment	Development Environment Setup	established	reference/sources/github-unitree-sdk2.md, reference/sources/github-unitree-sdk2-python.md, reference/sources/github-unitree-mujoco.md, reference/sources/github-unitree-rl-gym.md, reference/sources/github-groot-wbc.md	[getting-started sdk-programming simulation learning-and-ai whole-body-control motion-retargeting]	[]	[unitree_sdk2 cyclone_dds sim_to_real domain_id]	[]	[]	[Isaac Gym Preview 4 compatibility with RTX 5090 / Blackwell WSL2 GPU passthrough latency impact on RL training throughput GR00T-WBC Docker vs native install trade-offs]

Development Environment Setup

Full software stack for G1 development on Windows 10 with WSL2. Install in layers — you don't need everything on day one.

Hardware assumed: Windows 10 PC with NVIDIA RTX 5090 (32GB VRAM), WSL2, developing for a G1 EDU with Jetson Orin NX.

Layer 1: WSL2 + Ubuntu (Install First)

Enable WSL2

Open PowerShell as Administrator on Windows:

wsl --install -d Ubuntu-22.04

Reboot when prompted. On first launch, create a username and password.

Verify WSL2

# Inside WSL2 Ubuntu
wsl --version          # From PowerShell — should show WSL2
uname -r               # Inside Ubuntu — should show a Linux kernel

NVIDIA CUDA for WSL2

Install the NVIDIA GPU driver on Windows (not inside WSL) — download from nvidia.com
Inside WSL2, install the CUDA toolkit:

# Add NVIDIA package repo
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update
sudo apt install cuda-toolkit-12-6

Verify:

nvidia-smi              # Should show your RTX 5090
nvcc --version          # Should show CUDA version

Note: Do NOT install NVIDIA drivers inside WSL2. The Windows driver handles GPU passthrough automatically. [T1]

Essential System Packages

sudo apt update && sudo apt install -y \
    build-essential \
    cmake \
    git \
    python3.10 \
    python3.10-venv \
    python3-pip \
    libyaml-cpp-dev \
    libeigen3-dev \
    libboost-all-dev \
    libspdlog-dev \
    libfmt-dev \
    libglfw3-dev \
    wget \
    curl \
    unzip

Create a Project Directory

mkdir -p ~/unitree && cd ~/unitree

All repos will be cloned here.

Layer 2: CycloneDDS 0.10.2 (CRITICAL)

This MUST be version 0.10.2 exactly. Any other version causes silent DDS communication failures with the G1. [T1]

cd ~/unitree
git clone -b 0.10.2 https://github.com/eclipse-cyclonedds/cyclonedds.git
cd cyclonedds
mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/unitree/cyclonedds/install
make -j$(nproc)
make install

Add to ~/.bashrc:

echo 'export CYCLONEDDS_HOME="$HOME/unitree/cyclonedds/install"' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH="$HOME/unitree/cyclonedds/install/lib:$LD_LIBRARY_PATH"' >> ~/.bashrc
echo 'export PATH="$HOME/unitree/cyclonedds/install/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

Verify:

echo $CYCLONEDDS_HOME    # Should print the install path

Layer 3: Unitree SDKs

Python SDK (Start Here)

cd ~/unitree
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip3 install -e .

Verify:

python3 -c "import unitree_sdk2py; print('SDK OK')"

If it fails, it's almost always a CycloneDDS issue. Verify CYCLONEDDS_HOME is set.

C++ SDK (For Production / Real-Time)

cd ~/unitree
git clone https://github.com/unitreerobotics/unitree_sdk2.git
cd unitree_sdk2
mkdir build && cd build
cmake ..
make -j$(nproc)
sudo make install

Layer 4: MuJoCo Simulation

MuJoCo Python

pip3 install mujoco
pip3 install pygame    # For joystick control

Unitree MuJoCo Simulator

cd ~/unitree
git clone https://github.com/unitreerobotics/unitree_mujoco.git

Run the Simulator

cd ~/unitree/unitree_mujoco/simulate_python
python3 unitree_mujoco.py

This opens a MuJoCo window with the G1. If you're in WSL2, you need an X server or WSLg (Windows 11 has it built in; Windows 10 needs VcXsrv or similar).

WSL2 GUI on Windows 10

If MuJoCo can't open a window, install VcXsrv on Windows:

Download VcXsrv from sourceforge
Launch with "Disable access control" checked
In WSL2:

echo 'export DISPLAY=$(cat /etc/resolv.conf | grep nameserver | awk "{print \$2}"):0' >> ~/.bashrc
source ~/.bashrc

Windows 11 alternative: WSLg handles this automatically — no extra setup needed.

Connecting SDK to Simulator

The simulator uses DDS just like the real robot. In your Python code:

# For simulation (localhost, domain ID 1)
ChannelFactoryInitialize(1, "lo")

# For real robot (your network interface)
ChannelFactoryInitialize(0, "enp2s0")  # Change to your interface name

Same code, one line changes. [T0]

Layer 5: RL Training (Install When Starting Phase C)

Python Virtual Environment (Recommended)

Keep RL dependencies separate to avoid conflicts:

cd ~/unitree
python3 -m venv rl_env
source rl_env/bin/activate

PyTorch with CUDA

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu124

Verify GPU access:

python3 -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
# Should print: True NVIDIA GeForce RTX 5090

Isaac Gym (Preview 4)

Isaac Gym requires downloading from NVIDIA (free account required):

Go to https://developer.nvidia.com/isaac-gym
Download Isaac Gym Preview 4 (.tar.gz)
Extract and install:

cd ~/unitree
tar -xzf IsaacGym_Preview_4.tar.gz
cd isaacgym/python
pip3 install -e .

Verify:

cd ~/unitree/isaacgym/python/examples
python3 joint_monkey.py    # Should open a sim window with a robot

Note: Isaac Gym Preview 4 uses an older gym API. If you see gym version warnings, install: pip3 install gym==0.23.1 [T2]

RTX 5090 note: Isaac Gym Preview 4 was released before Blackwell GPUs. It should work via CUDA compatibility, but if you hit issues, Isaac Lab (see Layer 6) is the actively maintained alternative. [T3]

unitree_rl_gym (G1 RL Training)

cd ~/unitree
git clone https://github.com/unitreerobotics/unitree_rl_gym.git
cd unitree_rl_gym
pip3 install -e .

Train Your First Policy

cd ~/unitree/unitree_rl_gym
python3 legged_gym/scripts/train.py --task=g1

This trains a G1 locomotion policy using PPO. On an RTX 5090 with 32GB VRAM, you can run thousands of parallel environments. Training a basic walking policy takes 1-4 hours depending on settings. [T2]

Validate in MuJoCo (Sim2Sim)

python3 legged_gym/scripts/play.py --task=g1    # Replay in Isaac Gym
# Then Sim2Sim transfer to MuJoCo for cross-validation

Deploy to Real Robot (Sim2Real)

The deploy/ directory in unitree_rl_gym contains C++ deployment code. This runs the trained policy and sends commands via rt/lowcmd. See getting-started §8 and locomotion-control §9.

Layer 6: Whole-Body Control (Install When Starting Phase F)

GR00T-WBC (NVIDIA)

cd ~/unitree
git clone https://github.com/NVlabs/GR00T-WholeBodyControl.git
cd GR00T-WholeBodyControl

GR00T-WBC provides Docker-based setup:

# Docker approach (recommended — handles all dependencies)
docker build -t groot-wbc .
docker run --gpus all -it groot-wbc

Or native install (follow the repo's README for detailed dependency list).

Key files:

deploy_g1.py — orchestration script for real G1 deployment
Pre-trained locomotion models included
LeRobot integration for data collection + behavior cloning

Pinocchio (Rigid Body Dynamics / IK)

# Via conda (recommended)
conda install -c conda-forge pinocchio

# Or via pip
pip3 install pin

Verify:

python3 -c "import pinocchio; print('Pinocchio', pinocchio.__version__)"

Isaac Lab (Optional — Alternative to Isaac Gym)

Isaac Lab is NVIDIA's actively maintained replacement for Isaac Gym. If Isaac Gym has issues with your RTX 5090:

Install Isaac Sim 4.5.0 or 5.0.0 from NVIDIA Omniverse
Clone unitreerobotics/unitree_sim_isaaclab
Follow the repo's setup instructions

Isaac Lab provides better integration with GR00T-WBC and newer GPU support. [T0]

Layer 7: Motion Data (Install When Starting Phase G)

SMPL Body Model

pip3 install smplx
pip3 install trimesh    # Mesh utilities

AMASS Dataset (Pre-Retargeted for G1)

pip3 install huggingface_hub

# Download the retargeted dataset
python3 -c "
from huggingface_hub import snapshot_download
snapshot_download(repo_id='ember-lab-berkeley/AMASS_Retargeted_for_G1', local_dir='~/unitree/data/amass_g1')
"

This provides thousands of human motions already mapped to the G1's 29-DOF joint structure. Format: numpy arrays of shape [-1, 36] (29 joint positions + 7 base state). [T1]

CMU Motion Capture Database (Optional — Raw Source Data)

Available at mocap.cs.cmu.edu in BVH/C3D/ASF+AMC formats. Use AMASS instead for G1 — it includes CMU data already retargeted.

Jetson Orin NX Setup (On the Robot)

The Jetson comes with most dependencies pre-installed. Key things to verify/add:

# SSH in
ssh unitree@192.168.123.164    # password: 123

# Verify CycloneDDS
echo $CYCLONEDDS_HOME          # Should be /home/unitree/cyclonedds/install

# Verify Python SDK
python3 -c "import unitree_sdk2py; print('OK')"

# If SDK not installed:
cd ~
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip3 install -e .

For production deployment, you'll copy your trained policy weights to the Jetson and run inference there. The Jetson's 100 TOPS handles RL policy inference easily (< 1ms per step). [T1]

Quick Reference: What Runs Where

Machine	What Runs	Why
Your PC (WSL2)	RL training, simulation, policy development	GPU power (RTX 5090), fast iteration
Jetson Orin NX	Policy inference, real-time control, deployment	On the robot, low DDS latency
Locomotion Computer (RK3588)	Stock controller OR passthrough in debug mode	Not user-programmable

Your dev workflow: train on PC → validate in sim on PC → copy weights to Jetson → deploy on real robot.

Install Order Summary

Day 1:     Layer 1 (WSL2 + Ubuntu + CUDA)
           Layer 2 (CycloneDDS 0.10.2)
           Layer 3 (Python SDK)
           Layer 4 (MuJoCo + unitree_mujoco)

Week 3+:   Layer 5 (PyTorch, Isaac Gym, unitree_rl_gym)

Month 2+:  Layer 6 (GR00T-WBC, Pinocchio)
           Layer 7 (AMASS dataset, SMPL)

Key Relationships

Prereq for: getting-started (need SDK installed to talk to robot)
Uses: sdk-programming (SDK installation details)
Enables: simulation (MuJoCo + Isaac Gym environments)
Enables: learning-and-ai (RL training pipeline)
Enables: whole-body-control (GR00T-WBC framework)
Enables: motion-retargeting (AMASS dataset, Pinocchio IK)

11 KiB Raw Blame History

Development Environment Setup

Layer 1: WSL2 + Ubuntu (Install First)

Enable WSL2

Verify WSL2

NVIDIA CUDA for WSL2

Essential System Packages

Create a Project Directory

Layer 2: CycloneDDS 0.10.2 (CRITICAL)

Layer 3: Unitree SDKs

Python SDK (Start Here)

C++ SDK (For Production / Real-Time)

Layer 4: MuJoCo Simulation

MuJoCo Python

Unitree MuJoCo Simulator

Run the Simulator

WSL2 GUI on Windows 10

Connecting SDK to Simulator

Layer 5: RL Training (Install When Starting Phase C)

Python Virtual Environment (Recommended)

PyTorch with CUDA

Isaac Gym (Preview 4)

unitree_rl_gym (G1 RL Training)

Train Your First Policy

Validate in MuJoCo (Sim2Sim)

Deploy to Real Robot (Sim2Real)

Layer 6: Whole-Body Control (Install When Starting Phase F)

GR00T-WBC (NVIDIA)

Pinocchio (Rigid Body Dynamics / IK)

Isaac Lab (Optional — Alternative to Isaac Gym)

Layer 7: Motion Data (Install When Starting Phase G)

SMPL Body Model

AMASS Dataset (Pre-Retargeted for G1)

CMU Motion Capture Database (Optional — Raw Source Data)

Jetson Orin NX Setup (On the Robot)

Quick Reference: What Runs Where

Install Order Summary

Key Relationships

11 KiB

Raw Blame History