Advanced physics simulation

Dear MuJoCo users,

We are excited to announce that DeepMind has acquired MuJoCo and is making it freely available to everyone via our Download page.

The DeepMind Robotics Simulation team is working hard to prepare the codebase for full open sourcing in 2022, and we look forward to developing it further together with the community.

When we open source MuJoCo, its GitHub repository will become its new home. We encourage everyone to follow the project there, and to submit any questions, bugs, feature requests, etc. using the Issues section of that repository. Until then, will remain the home for MuJoCo.

More on DeepMind's blog

Latest versions of MuJoCo can be downloaded from our GitHub Releases page. Legacy versions (i.e. versions 2.0 and earlier) are available from Roboti's download page.

Customers with existing paid licenses for MuJoCo are directed to for continued support.

MuJoCo is a physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed.

MuJoCo offers a unique combination of speed, accuracy and modeling power, yet it is not merely a better simulator. Instead it is the first full-featured simulator designed from the ground up for the purpose of model-based optimization, and in particular optimization through contacts.

MuJoCo makes it possible to scale up computationally-intensive techniques such optimal control, physically-consistent state estimation, system identification and automated mechanism design, and apply them to complex dynamical systems in contact-rich behaviors. It also has more traditional applications such as testing and validation of control schemes before deployment on physical robots, interactive scientific visualization, virtual environments, animation and gaming.

Key features

  • Simulation in generalized coordinates, avoiding joint violations
  • Inverse dynamics that are well-defined even in the presence of contacts
  • Unified continuous-time formulation of constraints via convex optimization
  • Constraints include soft contacts, limits, dry friction, equality constraints
  • Simulation of particle systems, cloth, rope and soft objects
  • Actuators including motors, cylinders, muscles, tendons, slider-cranks
  • Choice of Newton, Conjugate Gradient, or Projected Gauss-Seidel solvers
  • Choice of pyramidal or elliptic friction cones, dense or sparse Jacobians
  • Choice of Euler or Runge-Kutta numerical integrators
  • Multi-threaded sampling and finite-difference approximations
  • Intuitive XML model format (called MJCF) and built-in model compiler
  • Cross-platform GUI with interactive 3D visualization in OpenGL
  • Run-time module written in ANSI C and hand-tuned for performance