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Tutorial on how to get started with MuJoCo Simulation Platform. MuJoCo stands for Multi-Joint dynamics with Contact.
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<p align="center">
<img src="./img/Logo.svg" alt="MuJoCo Tutorial Logo" width="125"/>
</p>
<h1 align="center">MuJoCo Tutorial 🤖</h1>
<p align="center">
A modern tutorial for getting started with <a href="https://mujoco.org/">MuJoCo</a> — a fast, accurate physics engine for robotics, biomechanics, and reinforcement learning.
</p>
> MuJoCo is open-source (Apache 2.0) and maintained by Google DeepMind. Since v3.0, it includes GPU-accelerated simulation via MJX.
[](https://colab.research.google.com/github/tayalmanan28/MuJoCo-Tutorial/blob/main/tutorial/quickstart.ipynb)
---
## Installation 🚀
MuJoCo is a pure Python package — no license keys, no system dependencies:
```bash
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
```bash
conda create -n mujoco-tut python=3.11
conda activate mujoco-tut
pip install -r requirements.txt
```
---
## Quickstart
```python
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`](examples/offscreen_rendering.py).
---
## Preview
<p align="center">
<img src="img/previews/trajectory_tracking.gif" width="45%" alt="Trajectory Tracking"/>
<img src="img/previews/lqr_control.gif" width="45%" alt="LQR Cart-Pole"/>
</p>
<p align="center">
<img src="img/previews/impedance_control.gif" width="45%" alt="Impedance Control"/>
<img src="img/previews/pick_and_place.gif" width="45%" alt="Pick and Place"/>
</p>
<p align="center"><em>Left to right: trajectory tracking, LQR balancing, impedance control, pick-and-place</em></p>
---
## Contents
| Example | Description |
|---------|-------------|
| [`examples/projectile.py`](examples/projectile.py) | Projectile with drag force |
| [`examples/pendulum_control.py`](examples/pendulum_control.py) | PD control of a pendulum |
| [`examples/double_pendulum.py`](examples/double_pendulum.py) | Double pendulum swing-up control |
| [`examples/hopper.py`](examples/hopper.py) | 2D hopper with open-loop jumping |
| [`examples/inverse_kinematics.py`](examples/inverse_kinematics.py) | Damped least-squares IK with Jacobians |
| [`examples/contact_forces.py`](examples/contact_forces.py) | Reading contact info & forces |
| [`examples/domain_randomization.py`](examples/domain_randomization.py) | Varying physics params across episodes |
| [`examples/menagerie_robot.py`](examples/menagerie_robot.py) | Loading robots from MuJoCo Menagerie |
| [`examples/mjx_parallel.py`](examples/mjx_parallel.py) | GPU-parallel sim with MJX (JAX) |
| [`examples/gymnasium_env.py`](examples/gymnasium_env.py) | Using Gymnasium's MuJoCo envs |
| [`examples/offscreen_rendering.py`](examples/offscreen_rendering.py) | Headless rendering & video export |
| [`examples/trajectory_tracking.py`](examples/trajectory_tracking.py) | Arm follows circular path (task-space PD) |
| [`examples/operational_space_control.py`](examples/operational_space_control.py) | Full dynamics compensation in task space |
| [`examples/lqr_control.py`](examples/lqr_control.py) | LQR cart-pole balancing (linearization + Riccati) |
| [`examples/impedance_control.py`](examples/impedance_control.py) | Compliant end-effector (spring-damper behavior) |
| [`examples/locomotion_controller.py`](examples/locomotion_controller.py) | Hopper FSM locomotion (finite state machine) |
| [`examples/pick_and_place.py`](examples/pick_and_place.py) | Gripper pick-and-place with waypoint sequencing |
| [Excerpt of 8,271 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:48957d8b8a2c580a, topic:robotics, topic:simulation, readme:robotics
matched fp:48957d8b8a2c580a, name:tutorial, desc:tutorial, readme:tutorial