MuJoCo overview and key facts (October 2026)

MuJoCo (Multi-Joint dynamics with Contact) is a general-purpose physics engine maintained by Google DeepMind. Robotics, biomechanics and machine learning teams use it to simulate robots and their contacts quickly and accurately, often as the environment for training control policies.

MuJoCo has a C API tuned for performance, a built-in XML model compiler, an interactive viewer called simulate, Python bindings and a Unity plugin. Tutorials cover model editing, a multithreaded rollout module, LQR control, a least-squares solver and MJX, which runs simulations with JAX.

Documentation is at mujoco.readthedocs.io. The repository is under Apache 2.0, had 15,366 GitHub stars and was updated on September 28, 2026. Release 3.14.0 appeared on September 22, 2026.

• Serves as a training environmentReinforcement learning agents interact with MuJoCo models to learn walking, grasping or balancing. Research and robotics teams can train policies in simulation without risking hardware.
• Runs simulations on acceleratorsMJX executes MuJoCo physics in JAX so that many environments can run in parallel on accelerator hardware. Training runs that need millions of steps finish faster.
• Evaluates controllers in parallelThe multithreaded rollout module simulates many trajectories at once from Python. Engineers can compare control parameters or policies across scenarios in one batch.

• Apache 2.0 license with a stable C API and Python bindings.
• MJX variant for JAX-based parallel simulation.
• Maintained by Google DeepMind with frequent releases.

MuJoCo is an open source project under the Apache 2.0 license and runs on your own hardware in any country.

MuJoCo is a physics library for developers. It has no graphical scene editor for plant layouts and no vendor support contract. Your team builds models in XML or code and maintains the integration. Check whether its rigid-body focus fits your use case or whether you also need photorealistic rendering from another tool.

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