MuJoCo — The Complete Guide: Fast, Free Physics Simulation for Robotics and Reinforcement Learning
MuJoCo (Multi-Joint dynamics with Contact) began at the University of Washington, was acquired by DeepMind and released as open source under Apache 2.0. It specialises in what other simulators find hard: fast, stable contacts between many bodies.
MuJoCo
Google DeepMind
A fast physics engine for robotics, biomechanics and machine learning.
Get it from the vendorWhat you get
- Accurate physics — joints, friction, soft contacts, tendons and actuators.
- Speed — thousands of simulation steps per second, suitable for training controllers.
- XML model file — a readable description of the robot and its environment.
- Python — the mujoco package from pip and an interactive viewer.
- MJX — parallel runs on a GPU through JAX.
Who it suits
Control and reinforcement-learning researchers, biomechanics (skeleton and muscle models) and engineers testing grippers and mechanisms with many contact points. If you need camera sensors and a full world with ROS, Gazebo or Webots are a better fit.
Related tools
For another open Python-based physics simulator — PyBullet. For a scientific Python environment — Python. For the biomechanics of movement — OpenSim.
PyBullet
Bullet Physics
A physics engine for robotics and reinforcement learning, driven from Python.
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Python
Python Software Foundation
The language behind most modern engineering computation, and its full ecosystem.
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OpenSim
Stanford University
Musculoskeletal modelling and movement simulation, from Stanford University.
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Gazebo
Open Robotics
3D robotics simulator with physics and simulated sensors, integrated with ROS.
Get it from the vendorThe bottom line
MuJoCo has become a standard in robotics and reinforcement-learning research — and it is now completely free, including for commercial use.