SimRig

Turn MuJoCo robots into trained policies with agent-guided task design, PPO training, evaluation, and interactive previews.

SimRig combines:

• a Python CLI that inspects models, runs MuJoCo Playground environments, trains Brax PPO policies, evaluates checkpoints, and serves previews;
• an agent skill that teaches Codex, Claude Code, and Cursor how to use that pipeline safely.

Raw robot XML is not automatically a training task. SimRig helps the agent move from a model and a requested behavior to explicit observations, actions, rewards, resets, termination conditions, validation, training, and evaluation.

What SimRig can do

SimRig v0 uses MuJoCo and MuJoCo Playground. Isaac Lab is not currently a supported backend.

Installation

SimRig requires Python 3.10 or newer. Install the Playground training stack from PyPI:

python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "simrig[playground]"

simrig --version

For an editable source installation, clone the repository and install from its root instead:

git clone https://github.com/Su1eym4n/simrig.git
cd simrig
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[playground]"

Development dependencies, tests, and contribution checks are documented in CONTRIBUTING.md.

Install the agent skill

Inside this repository, Codex discovers the SimRig skill automatically through .agents/skills/simrig.

To make the skill available from any project, install it globally with the Skills CLI:

npx skills add Su1eym4n/simrig --skill simrig --global

The installer supports Codex, Claude Code, Cursor, and other agents. Restart the agent after installation. See Agent skill installation for provider-specific commands, manual installation, and troubleshooting.

Use SimRig

Open a project containing a MuJoCo robot or scene and ask the agent naturally:

Train this MuJoCo robot to walk forward.

Create a crouching task for this robot, smoke-test it, and start a small training run.

Evaluate this checkpoint across five seeds and preview the policy.

You can invoke the workflow explicitly with $simrig, but the skill can also activate automatically when the request matches its description.

Existing Playground environment

simrig list-envs --backend mujoco-playground
simrig inspect-env Go1JoystickFlatTerrain
simrig smoke Go1JoystickFlatTerrain --steps 10
simrig train Go1JoystickFlatTerrain --preset smoke

Use the smoke preset before a longer local or cloud configuration.

Custom robot or scene

Inspect the model before designing a task:

simrig inspect-model path/to/robot.xml --save-report
simrig view-model path/to/robot.xml --port 8766

Open http://127.0.0.1:8766/ to inspect the compiled model, orbit/zoom/pan the camera, and adjust named joints. The default threejs renderer sends MuJoCo's visual meshes and primitives to a GPU-accelerated WebGL scene, so camera motion stays smooth without streaming image frames from Python. If the MJCF defines a keyframe, the viewer starts from its first authored pose and Reset Joints restores it.

The Three.js modules are pinned and loaded from jsDelivr, so the default viewer needs an internet connection when the page first loads. For an entirely local MuJoCo-rendered image stream, use:

simrig view-model path/to/robot.xml --render-mode mujoco --port 8766

Use --render-mode topdown only for the schematic debugging fallback.

View a running MuJoCo script

Standalone controllers can publish the MjModel and MjData they already own to the same Three.js viewer. SimRig does not step, pause, or replay the script:

from simrig import LiveWebViewer

with LiveWebViewer(
    model,
    data,
    name="my controller",
    tracking_body="end_effector",
) as web:
    while running:
        with web.lock:
            data.ctrl[:] = controller(data)
            mujoco.mj_step(model, data)
            web.sync(phase="moving")

Open the printed http://127.0.0.1:8767/ URL. The page receives lightweight geom transforms while the Python script retains full control of simulation timing and state. A named tracking_body also draws its live path. Use wait_for_client() when motion should begin only after the page is ready.

After defining the task, scaffold and validate an editable environment:

simrig new-env my_task --model path/to/scene.xml --template mjx
simrig validate-env envs/my_task.py
simrig validate-env envs/my_task.py --runtime
simrig smoke envs/my_task.py --steps 10
simrig train envs/my_task.py --preset smoke

The generated environment is a starter, not an invented task definition. The reward, observations, actions, resets, and termination logic remain explicit and editable in Python.

Evaluate and preview

simrig eval runs/<run-dir>/policy.params \
  --env Go1JoystickFlatTerrain \
  --steps 500 \
  --seed 0 \
  --command 0.5 0.0 0.0

simrig preview runs/<run-dir>/policy.params \
  --env Go1JoystickFlatTerrain \
  --command 0.5 0.0 0.0 \
  --port 8765

Open http://127.0.0.1:8765/ to orbit, zoom, pan, change commands, pause, and inspect the live rollout. Preview uses the Three.js renderer by default: the policy advances on a server-side rollout clock while lightweight MuJoCo geom transforms update the browser scene. The camera follows the robot without streaming rendered image frames. Use --render-mode mujoco for the older local image-stream preview or --render-mode topdown for the schematic fallback.

Documentation

• Examples
• Agent workflow
• Agent skill installation
• Contributing
• SimRig skill source

Outputs

SimRig writes project-local artifacts:

• reports/ — model, environment, and evaluation reports
• runs/ — training configuration, metrics, checkpoints, and policy parameters
• envs/ — editable custom environment modules
• artifacts/ and configs/ — user-managed outputs and configuration

License

MIT