MuJoCo (Multi-Joint Dynamics with Coherence) is an open-source physics engine developed and maintained by Google DeepMind. It is designed to simulate composite structures and object interactions, making it highly useful for robotics, biomechanics, computer graphics, animation, and machine learning.
Unlike general-purpose physics engines used in games, MuJoCo focuses on the accurate and practical simulation of complex mechanical systems. It offers a native C API, visualization tools, Python bindings, and integration with various other development environments.
The combination of its simulation capabilities, computational tools, and compatibility with machine learning makes it a popular choice for researchers and developers working in the fields of robotics and reinforcement learning.
Features
High-Performance Physics Simulation
MuJoCo provides a physics engine optimized for simulating articulated systems interacting with their environments.
It handles rigid bodies, joints, constraints, collisions, friction, and contact forces. Its simulation architecture uses preallocated data structures to reduce runtime overhead and improve computational efficiency.
This makes it suitable for applications involving robotic arms, humanoid robots, legged locomotion, mechanical systems, and other complex physical models.
MJCF Model Description
MuJoCo uses its own XML-based modeling format called MJCF (MuJoCo XML). It allows developers to describe physical environments, including bodies, joints, geometries, actuators, sensors, materials, and simulation parameters.
The built-in XML compiler converts these descriptions into optimized simulation structures.
MJCF also supports reusable components, procedural model construction, and external assets, making it easier to manage complex simulation environments.
MuJoCo additionally supports URDF model import, allowing developers to work with robot descriptions from other robotics tools.
Contact and Collision Simulation
One of MuJoCo's defining capabilities is its handling of contact dynamics.
It provides collision detection and constraint-based contact simulation for objects interacting with each other or their surroundings.
The engine supports different collision geometries, friction models, and contact parameters. These features are particularly important for robotics research, where realistic interaction between robots and objects is essential.
Its contact solver is designed to provide stable and efficient simulation rather than relying exclusively on conventional rigid-body collision handling.
Robotics and Actuator Simulation
MuJoCo provides extensive support for modeling robotic mechanisms.
Developers can define joints, actuators, transmissions, sensors, and control inputs to simulate robotic movement and behavior.
It supports different joint configurations, including hinge, slide, ball, and free joints. Actuator models can represent motors and other mechanisms used to control simulated robots.
This makes it useful for testing robotic control algorithms before deploying them on physical hardware.
Python Integration
MuJoCo provides official Python bindings through the mujoco package.
Developers can load models, control simulation steps, access physical state information, modify model parameters, and interact with the simulation directly from Python.
Installation is straightforward:
pip install mujocoThe Python package includes the native MuJoCo library, so a separate installation of the C/C++ runtime is generally unnecessary.
The Python API is particularly useful for machine learning experiments, robotics research, simulation automation, and reinforcement learning environments.
Reinforcement Learning and MJX
MuJoCo is widely used in reinforcement learning research because it provides efficient and repeatable physics simulations.
Its ecosystem includes MJX, an implementation built using JAX that enables physics simulation on supported accelerators.
MJX makes it possible to explore batched simulation, accelerator-based computation, and differentiable physics workflows.
These capabilities are valuable when training robotic control policies that require large numbers of simulation steps.
Interactive Visualization
MuJoCo includes a native visualization application called simulate.
It allows users to load models, inspect simulated environments, interact with objects, and observe physical behavior in real time.
The visualization system uses OpenGL and provides tools for examining simulation states, contacts, and other physical properties.
MuJoCo also offers MuJoCo Studio and browser-based visualization through its WebAssembly implementation.
Plugin Architecture
MuJoCo supports plugins that extend its simulation capabilities.
Plugins can introduce custom sensors, actuators, modeling components, and other functionality without requiring developers to modify the core engine.
This makes the architecture suitable for specialized robotics applications and experimental simulation features.
Unity Integration
MuJoCo provides a Unity plugin for developers who want to combine its physics simulation capabilities with the Unity development environment.
This is useful for robotics visualization, interactive simulation environments, and applications that benefit from Unity's rendering and user interface tools.
Additional language integrations are available through community projects, including Java, Rust, Julia, and Swift bindings.
Download MuJoCo 4.0.0 - Physics Engine - Software Mirrors |
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MuJoCo 4.0.0 - Physics Engine for Windows |
MuJoCo 4.0.0 - Physics Engine for macOS |
MuJoCo 4.0.0 - Physics Engine for Linux |
MuJoCo 4.0.0 - Physics Engine Source Code |
MuJoCo 4.0.0 - Physics Engine Release Notes:Version 4.0.0 (November 6, 2026)
 Significant new features
timestep. On the right, one step of conservative advancement between two rotating boxes. See model/speculative/ for examples.
-- (spatial, fixed) springs (left).
-- (yield) for 2D and 3D flexes, with separate stretching and -- bending yield strains for 2D shells, volumetric-deviatoric (von Mises $J_2$) stretching and 2D shell bending -- return mapping (right).
 
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orientations of ball and free joints. Previously, the angular velocities of the Runge-Kutta stages were combined without the Runge-Kutta-Munthe-Kaas correction, so orientations converged only at second order. This changes the simulation of models with ball or free joints that use
surface velocity (
quaternion orientations of body inertias, geoms, and sites alongside
fixed and spatial tendons. Previously, the type was implied by whether the first path element was a joint. Deformables
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Performance and Compatibility
MuJoCo is designed around computational efficiency and accurate physics simulation.
Its C API provides direct access to simulation structures, while preallocated memory and optimized numerical algorithms help reduce runtime overhead.
The engine supports Linux, Windows, and macOS, with prebuilt binaries available for x86-64 and ARM64 platforms.
Developers can use MuJoCo through C/C++, Python, Unity, and other language integrations.
For machine learning workloads, MJX provides an alternative implementation using JAX, allowing compatible simulation operations to run on accelerators.
Actual performance depends on model complexity, collision geometry, solver settings, simulation frequency, and hardware capabilities. Large environments and complex contact interactions can still require substantial computational resources.
System Requirements
MuJoCo has relatively modest requirements for basic simulation, although more complex robotics and machine learning workloads benefit from stronger hardware.
Typical requirements include:
Linux, Windows, or macOS.
A compatible 64-bit processor.
OpenGL-compatible graphics hardware for native visualization.
Sufficient RAM for simulation models and application data.
CMake and a C++17 compiler when building from source.
Python 3.11 or newer for the current Python package, according to the repository's installation documentation.
Additional requirements depend on the selected integrations. MJX requires its Python and JAX dependencies, while Unity development requires a compatible Unity environment.
Pros and Cons
Pros
High-performance physics simulation.
Accurate articulated-body and contact dynamics.
Designed for robotics and biomechanics research.
Supports MJCF and URDF-based workflows.
Official Python bindings.
Integration with JAX through MJX.
Includes interactive visualization tools.
Supports custom simulation plugins.
Unity integration.
Available for Linux, Windows, and macOS.
Open-source under Apache-2.0.
Cons
Primarily designed for technical users and researchers.
Requires understanding of physics modeling and simulation concepts.
MJCF has a learning curve for beginners.
Complex environments can require substantial computational resources.
Some integrations require additional dependencies.
Not intended to replace general-purpose game engines.
How to Install
MuJoCo provides precompiled libraries for Linux, Windows, and macOS. These are the recommended option for developers who simply want to use the simulation engine.
For Python users, installation through PyPI is straightforward:
pip install mujocoThe package includes the native simulation library and Python bindings.
Developers who want to build the C/C++ engine from source need CMake and a compatible C++17 compiler.
Clone the repository and create a separate build directory:
git clone https://github.com/google-deepmind/mujoco.git
cd mujoco
mkdir build
cd buildConfigure and compile the project in Release mode:
cmake ../ -DCMAKE_BUILD_TYPE=Release
cmake --build . --parallelTo install the compiled files into a custom directory:
cmake ../ -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=/opt/mujoco
cmake --build . --parallel
cmake --install .The build system automatically downloads required dependencies through CMake FetchContent. Python bindings require a separate build process if they also need to be compiled from source.
Final Verdict
MuJoCo is an excellent physics engine for developers and researchers working with robotics, biomechanics, and machine learning. Its focus on articulated-body dynamics, contact simulation, and computational efficiency makes it particularly valuable for applications where accurate physical behavior matters.
Its official Python bindings and MJX implementation also make it an attractive platform for reinforcement learning experiments. Meanwhile, its C API, plugin architecture, and Unity integration provide flexibility for more specialized development projects.
The main limitation is its learning curve. MuJoCo is a technical simulation framework rather than a beginner-friendly visual application. Understanding MJCF, physical parameters, actuators, and contact dynamics requires some knowledge of physics and robotics.
Overall, MuJoCo is a powerful open-source choice for anyone developing robotic systems, training control algorithms, or researching physically accurate simulations.
Sources
Developer:
Google DeepMind
Operating System:
Windows / macOS / Linux
Date Added:
2026-11-09T16:50:14.000Z
