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MuJoCo-Tutorial

Tutorial on how to get started with MuJoCo Simulation Platform. MuJoCo stands for Multi-Joint dynamics with Contact. It was acquired and made freely available by DeepMind in October 2021, and open sourced in May 2022. Feel free to contribute. Show your support by ✨this repository.

#deepmind#mujoco#mujoco-environments#python#robotics#simulation

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README preview

MuJoCo is open-source (Apache 2.0) and maintained by Google DeepMind. Since v3.0, it includes GPU-accelerated simulation via MJX.

Installation 🚀

MuJoCo is a pure Python package — no license keys, no system dependencies:

pip install mujoco gymnasium[mujoco] mediapy

That's it. For interactive visualization you also need a display (or use offscreen rendering in notebooks).

Optional: Conda environment

conda create -n mujoco-tut python=3.11
conda activate mujoco-tut
pip install -r requirements.txt

Quickstart

import mujoco
import mujoco.viewer

model = mujoco.MjModel.from_xml_string("""
<mujoco>
  <worldbody>
    <light pos="0 0 3" dir="0 0 -1"/>
    <geom type="plane" size="5 5 0.1"/>
    <body pos="0 0 2">
      <joint type="free"/>
      <geom type="sphere" size="0.2" rgba="1 0 0 1"/>
    </body>
  </worldbody>
</mujoco>
""")
data = mujoco.MjData(model)

# Launch interactive viewer
mujoco.viewer.launch(model, data)

For headless/notebook rendering, see examples/offscreen_rendering.py.

Preview

Example	Description
`examples/projectile.py`	Projectile with drag force
`examples/pendulum_control.py`	PD control of a pendulum
`examples/double_pendulum.py`	Double pendulum swing-up control
`examples/hopper.py`	2D hopper with open-loop jumping
`examples/inverse_kinematics.py`

from mujoco_base import MuJoCoBase

class MySim(MuJoCoBase):
    def reset(self):
        pass  # set initial conditions

    def controller(self, model, data):
        data.ctrl[0] = ...  # your control logic

sim = MySim("my_model.xml")
sim.run()  # interactive viewer
# or
frames = sim.run_headless(duration=5.0)  # offscreen

Notebook	Topic
`tutorial/00_what_is_mujoco.ipynb`	Start here! What MuJoCo is, mental model, first simulation
`tutorial/quickstart.ipynb`	Zero-install quickstart (Colab-ready)
`tutorial/01_basics.ipynb`	MjModel, MjData, named access
`tutorial/02_rendering.ipynb`	Rendering, cameras, scenes
`tutorial/03_simulation.ipynb`	Time stepping, mj_step, mj_forward
`tutorial/04_control.ipynb`	Actuators, PD control, callbacks
`tutorial/05_contact.ipynb`	Collision, contacts, force sensing
`tutorial/06_xml_modeling.ipynb`	MJCF modeling guide
`tutorial/07_mjx.ipynb`	MJX: GPU-parallel simulation with JAX
`tutorial/08_gymnasium.ipynb`	MuJoCo + Gymnasium for RL
`tutorial/09_sensors_and_estimation.ipynb`	Sensors, IMU, noise, state estimation
`tutorial/10_troubleshooting.ipynb`	Common errors & how to fix them
`tutorial/interactive_control.ipynb`	🎮 Interactive PD tuning with sliders

Concept	Description
MJCF	MuJoCo's XML format for defining models (docs)
`MjModel`	Compiled model (static) — physics parameters, geometry
`MjData`	Simulation state (dynamic) — positions, velocities, forces
`mujoco.viewer`	Built-in interactive 3D viewer
`mujoco.Renderer`	Offscreen rendering for headless environments
MJX	JAX backend for GPU-parallel simulation (docs)

Resource	What you'll learn
MuJoCo Menagerie	Production-quality robot models (Franka, UR5e, Unitree, etc.)
dm_control	DeepMind's control suite — benchmarks for continuous control
Gymnasium Leaderboard	Standard RL benchmarks (HalfCheetah, Humanoid, Ant)
Brax	Differentiable physics in JAX (alternative to MJX)
MuJoCo XLA (MJX) Paper	Technical details of GPU-parallel MuJoCo
Robosuite	Manipulation benchmarks built on MuJoCo
IsaacLab	NVIDIA's sim framework (comparison point)

MuJoCo-Tutorial

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README preview

Installation 🚀

Optional: Conda environment

Quickstart

Preview

Contents

Base class

Tutorial Notebooks

Key Concepts

Resources

What's Next? 🚀

Contributing 🤝

License 📃