⬡PipelineRobotics & ControlFree
MuJoCo
Fast and accurate physics engine for robotics, biomechanics, and RL research, now open-sourced by DeepMind.
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MuJoCo
MuJoCo (Multi-Joint dynamics with Contact) is a fast, accurate physics engine originally developed by Emo Todorov and now maintained by Google DeepMind as free open-source software. It is the de-facto standard simulator for robotics and reinforcement learning research, used in thousands of published works.
Key Features
- Generalised-coordinate articulated body simulation with contacts and friction
- Native Python bindings (
mujocopackage on PyPI) with NumPy integration - Full access to forward/inverse dynamics, Jacobians, and analytical gradients
- Passive viewer, offscreen rendering (OpenGL), and MJX GPU-accelerated backend (JAX)
- XML-based MJCF model format with extensive robot library (humanoids, arms, hands, legged robots)
Quick Start
pip install mujoco
import mujoco
import numpy as np
model = mujoco.MjModel.from_xml_string('<mujoco><worldbody><body><joint/><geom size=".1"/></body></worldbody></mujoco>')
data = mujoco.MjData(model)
for _ in range(100):
mujoco.mj_step(model, data)
print(data.qpos)
npx ai-supply add mujoco-physics-engine
Curated mirror of the open-source MuJoCo (Apache-2.0). Get it from the source.