unitree-g1/context/simulation.md

id	title	status	source_sections	related_topics	key_equations	key_terms	images	examples	open_questions
simulation	Simulation Environments	established	reference/sources/github-unitree-mujoco.md, reference/sources/github-unitree-rl-gym.md, reference/sources/github-unitree-rl-lab.md, reference/sources/github-unitree-sim-isaaclab.md, reference/sources/github-mujoco-menagerie-g1.md	[ros2-integration learning-and-ai locomotion-control]	[]	[sim_to_real domain_id]	[]	[]	[Optimal domain randomization parameters for G1 sim-to-real MJX vs standard MuJoCo performance for G1 training PyBullet G1 model quality and community support]

Simulation Environments

Simulation tools, robot models, and sim-to-real transfer for the G1.

1. Available Simulators

Simulator	Repository / Source	Status	Model Format	G1 Configs	Tier
MuJoCo (Unitree)	unitreerobotics/unitree_mujoco	Official	MJCF	G1 (all variants)	T0
MuJoCo Menagerie	google-deepmind/mujoco_menagerie/unitree_g1	Official	MJCF	G1 29-DOF, G1 with hands	T0
Isaac Lab	unitreerobotics/unitree_sim_isaaclab	Official	USD/URDF	G1-29dof-gripper, G1-29dof-dex3, G1-23dof	T0
Isaac Gym (legged)	unitreerobotics/unitree_rl_gym	Official	URDF	G1	T0
Isaac Lab (RL)	unitreerobotics/unitree_rl_lab	Official	USD/URDF	G1-29dof	T0
Gazebo	unitreerobotics/unitree_ros	Limited	URDF	G1 (no high-level walking)	T1
PyBullet	Community	Community	URDF	G1 (community models)	T2
Unity ML-Agents	RizziGama/unitree-g1-rl-unity	Community	URDF	G1 (PPO/SAC training)	T2

2. Robot Models

MuJoCo Menagerie (Recommended for MuJoCo)

• Repository: https://github.com/google-deepmind/mujoco_menagerie/tree/main/unitree_g1
• Key files:
  • g1.xml — Main 29-DOF model
  • g1_with_hands.xml — Variant with hand models
  • scene.xml — Standard environment
  • scene_mjx.xml — MJX-optimized environment
  • assets/ — Mesh and texture files
• Requirements: MuJoCo 2.3.4+
• MJX variant: Manually designed collision geometries, tuned solver parameters, realistic PD gains validated on real hardware (MuJoCo Playground, arXiv:2502.08844)
• License: BSD-3-Clause [T0]

Unitree ROS URDF

• Repository: https://github.com/unitreerobotics/unitree_ros
• Location: robots/g1_description/
• Contents: URDF with mass, inertia, moment, and joint limits
• Note: Designed for MuJoCo, NOT Gazebo-compatible for walking [T0]

USD Models (Isaac Sim)

• Repository: https://github.com/unitreerobotics/unitree_model (DEPRECATED)
• Current source: HuggingFace (newer models)
• Format: USD, converted from URDF via Isaac Sim [T0]

3. unitree_mujoco — Primary Simulator

Official MuJoCo simulator with seamless sim-to-real transition: [T0]

Feature	Details
Languages	C++ (recommended) and Python
C++ location	simulate/ directory
Python location	simulate_python/ directory
Terrain generation	Stairs, rough ground, heightmaps
Humanoid init	Virtual elastic band for stable initialization
Joystick support	pygame-based joystick control (Python)

C++ Dependencies

unitree_sdk2, MuJoCo, libyaml-cpp-dev, libspdlog-dev, libboost-all-dev, libglfw3-dev

Python Dependencies

unitree_sdk2_python, mujoco (pip), pygame (for joystick)

Supported Communication

• LowCmd/LowState (motor control)
• SportModeState (robot position/velocity)
• IMUState (for G1 robots)

4. unitree_sim_isaaclab — Isaac Lab Integration

High-fidelity simulation for data collection and model validation: [T0]

Feature	Details
Isaac Sim versions	4.5.0 or 5.0.0
G1 configurations	G1-29dof-gripper, G1-29dof-dex3, G1-23dof
Tasks	Pick-and-place, stacking, mobile manipulation
DDS communication	Same protocol as physical robot
Visual data	Camera capture in simulation
Performance	1s sim = 27 min real-world (RTX 4090, rough terrain)

Example Command

python sim_main.py --task Isaac-PickPlace-Cylinder-G129-Dex1-Joint \
  --enable_dex1_dds --robot_type g129

5. Sim-to-Real Transfer

The G1 ecosystem is designed for straightforward sim-to-real transfer: [T0]

MuJoCo → Real

Switch from simulation to real robot by changing only the network configuration:

• Simulation: Use domain ID for DDS routing
• Real hardware: Specify network interface (e.g., enp2s0)

No code changes required — the same DDS API works in both environments. [T0]

Training → Deployment Pipeline

1. Train (Isaac Gym / MuJoCo) → policy checkpoint
2. Play (MuJoCo sim) → validate behavior
3. Sim2Sim (MuJoCo ↔ Isaac Lab) → cross-sim validation
4. Sim2Real (unitree_sdk2) → deploy to physical robot

Domain Randomization

Standard RL training uses domain randomization for robustness:

• Physics parameters (friction, mass, restitution)
• Sensor noise (IMU bias, encoder noise)
• Terrain variation (procedural heightmaps)
• Latency simulation
• External force perturbations — random pushes applied to torso during training for push recovery robustness (see push-recovery-balance)

Perturbation Testing in Simulation

Both MuJoCo and Isaac Gym/Lab support applying external forces to the robot body during simulation. This is essential for:

• Training push-robust locomotion policies (push-recovery-balance)
• Validating balance during mocap playback (motion-retargeting)
• Testing whole-body controller robustness (whole-body-control)

In MuJoCo: use mj_applyFT() to apply forces/torques to bodies. In Isaac Gym: use gym.apply_body_force_at_pos() for the same.

Motion Retargeting Validation

The AMASS motion dataset (retargeted for G1, available on HuggingFace) provides reference trajectories that can be played back in simulation before real-robot execution. This enables safe validation of the full mocap → retarget → WBC → execute pipeline. See motion-retargeting.

Key Relationships

• Models from: ros2-integration (URDF files)
• Trains policies for: learning-and-ai (RL training environments)
• Validates: locomotion-control (test gaits in sim before real hardware)
• Same API as: sdk-programming (DDS interface identical in sim and real)