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MuJoCo

Classic physics engine widely used in robot RL and control research — lightweight and accurate.

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SimulationPhysics engineRL

Why we recommend it

MuJoCo is the default physics stack for a huge share of robot-learning papers and courses. Install is relatively light, Menagerie gives ready robot models, and tutorials are everywhere. Best if you want fast iteration on locomotion or manipulation before committing to a heavy GPU sim. Intermediate once you care about contacts and actuators; beginner-friendly for reading code and running demos.11.0 — https://github.com/google-deepmind/mujoco/releases/tag/3.11.0.11.0 — https://github.com/google-deepmind/mujoco/releases/tag/3.11.0.11.0 — https://github.com/google-deepmind/mujoco/releases/tag/3.11.0. Latest GitHub release: 3.11.0 — https://github.com/google-deepmind/mujoco/releases/tag/3.11.0.

Overview

MuJoCo (Multi-Joint dynamics with Contact) is the default physics engine for much of robot learning research. It is fast, accurate for contact-rich manipulation, and has mature Python bindings plus a large ecosystem of environments (Meta-World, robosuite, many custom envs).

DeepMind open-sourced MuJoCo, which accelerated adoption. Researchers use it when they need reproducible rigid-body dynamics without the weight of a full game-engine stack. It is not trying to be a photoreal renderer; pair it with simple cameras or external renderers when vision matters.

If you are implementing a new RL algorithm or a small manipulation study, MuJoCo is often the lowest-friction physics core.

Who it is for

RL / control researchers; anyone building custom sim envs; students learning robot dynamics.

Key highlights

  • Industry-standard contact dynamics for research
  • Lightweight compared to full digital-twin stacks
  • Huge ecosystem of envs and teaching materials
  • Official Python bindings (mujoco) actively maintained
  • Great baseline before scaling to GPU-native simulators

When to use

  • Algorithm research and teaching
  • Contact-rich tabletop tasks with modest visual needs
  • You want fast iteration on a laptop or single GPU workstation

When not to use

  • You need photoreal indoor scenes at scale (Isaac / Habitat)
  • Soft-body / fluid-dominant phenomena are core to the task
  • You require certified digital-twin pipelines for industry deployment

Getting started

  1. 1Install the official `mujoco` Python package and open a sample MJCF model.
  2. 2Learn MJCF structure: bodies, joints, actuators, sensors.
  3. 3Wrap a Gymnasium-compatible env for your task.
  4. 4Log contacts and joint limits — most bugs hide there.
  5. 5Only then plug in RL or imitation learners.

Install / setup

pip

pip install mujoco

Papers & reading

How it compares

Default research physics engine: lighter than Isaac, more standard than PyBullet for modern IL papers.

Caveats & pitfalls

  • MJCF authoring has a learning curve; start from existing models.
  • Visual appearance is basic unless you invest in assets/rendering.
  • Different forks/bindings historically caused confusion — prefer official package.

Content reviewed 2026-07-23