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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
| Date | Stars |
|---|---|
| 2026-07-24 | 14313 |
| 2026-07-25 | 14320 |
| 2026-07-28 | 14358 |
| 2026-07-30 | 14358 |
| 2026-08-06 | 14358 |
Today
— stars today
This week
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This month
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Momentum
35.0
growth rate 0.00%/day
<h1>
<a href="#"><img alt="MuJoCo" src="banner.png" width="100%"/></a>
</h1>
<p>
<a href="https://github.com/google-deepmind/mujoco/actions/workflows/build.yml?query=branch%3Amain" alt="GitHub Actions">
<img src="https://img.shields.io/github/actions/workflow/status/google-deepmind/mujoco/build.yml?branch=main">
</a>
<a href="https://mujoco.readthedocs.io/" alt="Documentation">
<img src="https://readthedocs.org/projects/mujoco/badge/?version=latest">
</a>
<a href="https://github.com/google-deepmind/mujoco/blob/main/LICENSE" alt="License">
<img src="https://img.shields.io/github/license/google-deepmind/mujoco">
</a>
</p>
**MuJoCo** stands for **Mu**lti-**Jo**int dynamics with **Co**ntact. It is a
general purpose physics engine that aims to facilitate research and development
in robotics, biomechanics, graphics and animation, machine learning, and other
areas which demand fast and accurate simulation of articulated structures
interacting with their environment.
This repository is maintained by [Google DeepMind](https://www.deepmind.com/).
MuJoCo has a C API and is intended for researchers and developers. The runtime
simulation module is tuned to maximize performance and operates on low-level
data structures that are preallocated by the built-in XML compiler. The library
includes interactive visualization with a native GUI, rendered in OpenGL. MuJoCo
further exposes a large number of utility functions for computing
physics-related quantities.
We also provide [Python bindings] and a plug-in for the [Unity] game engine.
## Documentation
MuJoCo's documentation can be found at [mujoco.readthedocs.io]. Upcoming
features due for the next release can be found in the [changelog] in the
"latest" branch.
## Getting Started
There are two easy ways to get started with MuJoCo:
1. **Run `simulate` on your machine.**
[This video](https://www.youtube.com/watch?v=P83tKA1iz2Y) shows a screen capture
of `simulate`, MuJoCo's native interactive viewer. Follow the steps described in
the [Getting Started] section of the documentation to get `simulate` running on
your machine.
2. **Explore our online IPython notebooks.**
If you are a Python user, you might want to start with our tutorial notebooks
running on Google Colab:
- The **introductory** tutorial teaches MuJoCo basics:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)
- The **Model Editing** tutorial shows how to create and edit models procedurally:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mjspec.ipynb)
- The **rollout** tutorial shows how to use the multithreaded `rollout` module:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb)
- The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a
humanoid on one leg:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb)
- The **least-squares** tutorial explains how to use the Python-based nonlinear
least-squares solver:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb)
- The **MJX** tutorial provides usage examples of
[MuJoCo XLA](https://mujoco.readthedocs.io/en/stable/mjx.html), a branch of MuJoCo written in JAX:
[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb)
- The **differentiable physics** tutorial trains locomotion policies with
analyticalExcerpt of 9,724 characters
Read on GitHub
Yuval Tassa · DeepMind · United Kingdom
1.2k
Alessio Quaglino · DeepMind
705
447
Saran Tunyasuvunakool · @google-deepmind · United Kingdom
292
Kyle Bayes · @DeepMind · United Kingdom
284
Taylor Howell · Google DeepMind · United Kingdom
260
Copybara Service · @google
227
Sam Haves · Google Deepmind
175
Google Deepmind · United States
169
Nimrod Gileadi · @Genesis-Embodied-AI
137
Kevin Zakka
123
114
Abhishek Joshi
94
80
Balint Hodossy · Imperial College London · United Kingdom
64
Erik Frey · United States
61
51
Tom Erez · DeepMind · United Kingdom
51
38
Andrew Luo · Canada
32
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f65c7895e5c76f18, topic:robotics, readme:robotics, readme:humanoid