mujoco_warp

# MuJoCo Warp (MJWarp) MJWarp is a GPU-accelerated version of the [MuJoCo](https://github.com/google-deepmind/mujoco) physics simulator, designed for NVIDIA hardware. MJWarp delivers high-throughput, accurate simulation for robotics research. MJWarp is maintained by [Google DeepMind](https://deepmind.google/) and [NVIDIA](https://www.nvidia.com/) as part of the [Newton](https://github.com/newton-physics/newton) project. # Getting started MuJoCo Warp requires an NVIDIA GPU for fast simulation but supports CPU for development and debugging. **Try it now:** view a simulation of a dancing humanoid robot locally on your machine: ```bash git clone https://github.com/google-deepmind/mujoco_warp.git && cd mujoco_warp python benchmarks/run.py -f unitree_g1_flat --view ``` Or try out [a tutorial in your browser](https://colab.research.google.com/github/google-deepmind/mujoco_warp/blob/main/notebooks/tutorial.ipynb) (no local setup required). MJWarp is also available via PyPI: ```bash pip install mujoco-warp ``` # Examples MuJoCo Warp simulates many kinds of physical systems, from rigid bodies with contacts to soft bodies, cloth, signed distance fields, and more. Here are a few examples of what it can do:
python benchmarks/run.py -f unitree_g1_flat --view
python benchmarks/run.py -f unitree_g1_hfield --view
python benchmarks/run.py -f myoarm --view
python benchmarks/run.py -f aloha_clutter --view
python benchmarks/run.py -f aloha_pot --view
python benchmarks/run.py -f aloha_sdf --view
python benchmarks/run.py -f aloha_cloth --view
python benchmarks/run.py -f three_humanoids --view
python benchmarks/run.py -f cloth --view Each of these scenes is benchmarked nightly and the [results are published nightly](https://google-deepmind.github.io/mujoco_warp/nightly/). # Tips for developers To set up MJWarp for development: ```bash git clone https://github.com/google-deepmind/mujoco_warp.git && cd mujoco_warp uv sync --all-extras # install all optional dependencies for development uv run pre-commit install # enables ruff, uv-lock, and kernel-analyzer checks on commit uv run pytest -n 8 # run all tests, verify everything works ``` If you plan to write Warp kernels for MJWarp, please use the `kernel_analyzer` vscode plugin located in [`contrib/kernel_analyzer`](https://github.com/google-deepmind/mujoco_warp/tree/main/contrib/kernel_analyzer). See the [README](https://github.com/google-deepmind/mujoco_warp/blob/main/contrib/kernel_analyzer/README.md) there for details on how to install it and use it. The same kernel analyzer will run on any PR you open, so it's important to fix any issues it reports. For performance profiling MJWarp, use the `--event_trace` flag on `mjwarp-testspeed` to get a full trace on a test scene of your choice: ```bash mjwarp-testspeed benchmarks/humanoid/humanoid.xml --event_trace ``` `mjwarp-testspeed` has many configuration options, see ```mjwarp-testspeed --help``` for details. For more details and advanced topics on using MJWarp, see the [MuJoCo Warp documentation](https://mujoco.readthedocs.io/en/latest/mjwarp/index.html). # Integrating MuJoCo Warp There are many ways to use MuJoCo Warp in your projects. In many cases, you can directly install and use MJWarp as a drop-in replacement for MuJoCo. If you prefer the [JAX](https://github.com/jax-ml/jax) ecosystem, you can use MJWarp via [MJX](https://mujoco.readthedocs.io/en/stable/mjx.html). See [MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground) for robotics machine learning recipes that use [JAX](https://github.com/jax-ml/jax) and MJWarp. If you prefer [PyTorch](https://pytorch.org/) for research, consider one of these two great options: * [Isaac Lab](https://github.com/isaac-sim/IsaacLab/tree/feature/newton) integrates MJWarp via [Newton](https://github.com/newton-physics/newton). This setup enables powerful, highly extensible multi-physics simulation with deep NVIDIA ecosystem integration. * [mjlab](https://github.com/mujocolab/mjlab) exposes the [Isaac Lab](https://github.com/isaac-sim/IsaacLab) manager-based API directly on top of MJWarp, providing a focused framework for robotics research with minimal dependencies and direct access to native MuJoCo data structures. # MuJoCo API Compatibility MuJoCo Warp supports the same features as MuJoCo with the following exceptions: - **Integrator**: `IMPLICITFAST` midpoint integrator feature is not supported - **Solver**: `PGS` and `noslip` not yet supported - **Actuator / Sensors**: `PLUGIN` types not yet supported - **Flex**: experimental — not all features are implemented or optimized yet [Differentiability via Warp](https://nvidia.github.io/warp/user_guide/differentiability.html) is not yet available. See [#500](https://github.com/google-deepmind/mujoco_warp/issues/500) for progress. # Batch Rendering MJWarp includes a **high-throughput** GPU batch renderer designed for simultaneous rendering of cameras across many parallel simulation worlds. The renderer uses ray-tracing to render MuJoCo scenes at millions of frames per second on NVIDIA GPUs. Key capabilities: - Mesh rendering - Texture support - Heightfield rendering - Flex deformable rendering - Gaussian splat rendering - Heterogeneous multi-camera support (different resolutions/FOV/intrinsics for each camera) - Lighting and shadow support See the [announcement PR](https://github.com/google-deepmind/mujoco_warp/pull/1113) for more details. # License MJWarp is released under the Apache 2.0 license. See [LICENSE](LICENSE) for details.