---
id: simulation
title: "Simulation Environments"
status: established
source_sections: "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"
related_topics: [ros2-integration, learning-and-ai, locomotion-control]
key_equations: []
key_terms: [sim_to_real, domain_id]
images: []
examples: []
open_questions:
  - "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
```bash
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]])

### Known Sim-to-Real Gaps [T1 — Verified on real G1, 2026-02-15]

| Gap | Impact | Fix |
|-----|--------|-----|
| **IMU mounting offset** | Persistent backward lean (~6° pitch offset between sim and real IMU) | Quaternion pitch rotation before gravity computation. See [[sensors-perception]] §4. |
| **PD gain calibration** | Policy trained with specific gains; changing gains degrades balance even if tracking improves | Use sim-trained gains OR recalibrate after gain change. Policy corrections are calibrated for training gains. |
| **Action clipping** | Not present in sim (RSL-RL default); adding clip in deployment causes saturation | Do NOT clip ONNX outputs — policy needs full range for push recovery |
| **Surface compliance** | Carpet adds ~1° effective ankle sag vs rigid sim floors | Minor — within policy tolerance |

**Key insight:** When a sim-trained policy produces poor real-world behavior, check sensor calibration BEFORE tuning gains or modifying the policy. An incorrect IMU reference frame cannot be fixed by any amount of gain tuning — the policy's balance loop actively fights the correction.

### 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)