google-deepmind/mujoco

★ 13,464C++Audience · researcherComplexity · 4/5Setup · moderate

Mindmap

mindmap
  root((mujoco))
    What it does
      Physics simulation
      Jointed body modeling
      Contact dynamics
    Tech stack
      C core library
      Python bindings
      JAX variant MJX
    Use cases
      Robot training
      Biomechanics research
      RL environments
    Setup paths
      pip install
      Precompiled binaries
      Build from source
    Audience
      ML researchers
      Robotics engineers
      Animation teams

mindmap root((mujoco)) What it does Physics simulation Jointed body modeling Contact dynamics Tech stack C core library Python bindings JAX variant MJX Use cases Robot training Biomechanics research RL environments Setup paths pip install Precompiled binaries Build from source Audience ML researchers Robotics engineers Animation teams

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Things people build with this

USE CASE 1

Simulate a robot arm picking up objects and train a machine learning agent to control it.

USE CASE 2

Model a human skeleton walking and analyze joint forces for biomechanics research.

USE CASE 3

Run MuJoCo physics simulations inside Python using pip and visualize them with the interactive viewer.

USE CASE 4

Train a reinforcement learning agent in a MuJoCo environment using JAX with the MJX variant.

Tech stack

C++PythonJAXUnityXML

Getting it running

Difficulty · moderate Time to first run · 30min

Easiest path is pip install for Python users, native viewer requires downloading precompiled binaries per OS.

In plain English

MuJoCo is a physics engine, meaning software that simulates how physical objects move, collide, and interact. The name stands for Multi-Joint dynamics with Contact. It is designed to model jointed structures, things like robot arms, animal skeletons, or animated characters, as they move through and interact with a simulated world. Google DeepMind maintains it, and it is aimed at researchers and developers working in robotics, biomechanics, graphics, animation, and machine learning. The core of MuJoCo is a C library tuned for speed. Models are written in XML files and compiled by a built-in tool into low-level data structures that the simulator runs directly. It includes an interactive visual viewer that shows the simulation on screen in real time, and a set of utility functions for computing physics quantities. Python bindings are available as a first-party package, and there is also a plug-in for the Unity game engine. For getting started, the README points to two paths. One is downloading the native viewer, called simulate, and running it on your machine by following the documentation. The other is a set of online Python tutorial notebooks on Google Colab that cover the basics, model editing, a JAX-based variant called MJX, and control examples including balancing a humanoid on one leg. Installation options include precompiled binaries for Linux, Windows, and macOS, which the README calls the recommended route. Python users can install it with a single pip command, since that package bundles a copy of MuJoCo. Building from source is also documented, though the README notes the tip of the main branch may be unstable. The project releases a new version around the first week of each month. It welcomes questions and bug reports through GitHub Discussions and Issues. The README also lists a range of community-maintained bindings for Swift, Java, Julia, and Rust, plus converters that translate models from other formats into MuJoCo's XML format.

Copy-paste prompts

Prompt 1

I want to simulate a simple robot arm in MuJoCo using Python. Show me how to define the XML model and load it with the Python API.

Prompt 2

How do I install MuJoCo's Python bindings with pip and run the interactive viewer on Linux?

Prompt 3

I'm training a reinforcement learning agent using MuJoCo and JAX. Show me how to set up MJX and run a basic control loop.

Prompt 4

Walk me through creating a MuJoCo XML model of a two-link pendulum with contact constraints.

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