mujoco-warp
MuJoCo Warp (MJWarp)
Decision gist · record as of 2026-08-14
Yes, if you have an NVIDIA GPU and need high-throughput physics simulation for robotics research. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers a GPU-accelerated alternative to standard MuJoCo with batch rendering. Start with CPU mode for development if you lack GPU access. Not suitable for CPU-only environments requiring production performance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- NVIDIA GPU strongly recommended for performance; CPU mode available for development but not production.
- Low friction install via pip; actively maintained with recent releases (17 days old).
- Requires NVIDIA GPU for production use but supports CPU for development.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-07-28 (17 days) · last repo commit 2026-08-14 · 1,400 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 346,204 downloads/mo, #7,358 on PyPI
Alternatives
Verify before relying
pip install mujoco-warp
import mujoco_warp as mjw
# Load and simulate a MuJoCo model on GPU- Whether IMPLICITFAST integrator, PGS/noslip solvers, and PLUGIN actuator types will be supported in future releases.
- Current state of differentiability support via Warp (issue #500 referenced in description).
- Performance characteristics and throughput numbers on specific NVIDIA GPU architectures.
- Compatibility with PyTorch and JAX ecosystems beyond the mentioned integration paths.
What it is and what it does
MuJoCo Warp is a GPU-accelerated physics simulator maintained by Google DeepMind and NVIDIA, built on top of the MuJoCo engine and NVIDIA's Warp compute framework. It brings high-throughput simulation to robotics research by offloading physics computation to NVIDIA GPUs, supporting rigid bodies, contacts, soft bodies, cloth, and signed distance fields. The simulator is designed as a near drop-in replacement for MuJoCo, with the same API surface minus a few unsupported features (IMPLICITFAST integrator, PGS solver, PLUGIN actuators, and experimental Flex support).
The package includes a high-throughput GPU batch renderer capable of rendering millions of frames per second across many parallel simulation worlds, with support for meshes, textures, heightfields, deformable bodies, heterogeneous multi-camera setups, and lighting. It integrates with JAX via MJX and with PyTorch through Isaac Lab and mjlab, making it suitable for robotics machine learning workflows. Installation is straightforward via pip, though an NVIDIA GPU is essential for production use; CPU mode is available for development and debugging.
Use it for
- Train reinforcement learning policies for robotic control by running many parallel simulations on GPU.
- Render training datasets for vision-based robotics by batch-rendering multiple camera views across simulation worlds.
- Prototype robot behaviors and physics interactions during development using CPU mode before scaling to GPU.
- Integrate differentiable physics into JAX-based machine learning pipelines via MJX.
- Benchmark and profile physics simulation performance using the included mjwarp-testspeed tool with event tracing.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have an NVIDIA GPU and need high-throughput physics simulation for robotics research.
The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers a GPU-accelerated alternative to standard MuJoCo with batch rendering. Start with CPU mode for development if you lack GPU access. Not suitable for CPU-only environments requiring production performance.
Install
mujoco-warp on PyPI
Before you install
Low friction install via pip; actively maintained with recent releases (17 days old). Requires NVIDIA GPU for production use but supports CPU for development.
NVIDIA GPU strongly recommended for performance; CPU mode available for development but not production.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install mujoco-warp
import mujoco_warp as mjw
# Load and simulate a MuJoCo model on GPUVerify before relying
- Whether IMPLICITFAST integrator, PGS/noslip solvers, and PLUGIN actuator types will be supported in future releases.
- Current state of differentiability support via Warp (issue #500 referenced in description).
- Performance characteristics and throughput numbers on specific NVIDIA GPU architectures.
- Compatibility with PyTorch and JAX ecosystems beyond the mentioned integration paths.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesabsl-pyetilsmujoconumpywarp-lang |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 346,204 / month, #7,358 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: mujoco_warp-3.11.0-py3-none-any.whl
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See also mjlab · mujoco-mjx · newton · warp-lang · mjviser · dm-control · robosuite · mujoco · sapien · newton-actuators