Yes, MuJoCo is entirely free and open-source. Google DeepMind acquired MuJoCo in October 2021 and subsequently open-sourced the entire codebase in May 2022 under the Apache 2.0 license, making all its features available without cost.

What are the main features of MuJoCo?

MuJoCo offers simulation in generalized coordinates, well-defined inverse dynamics with contacts, a unified continuous-time constraint formulation, and support for various constraints like soft contacts and friction. It also includes diverse actuators, multiple solver choices, and recent additions like `mju_threadpool` for parallelization and GPU acceleration via MuJoCo Warp.

How does MuJoCo compare to alternatives?

MuJoCo is known for its high simulation fidelity and speed in contact-rich scenarios, often preferred over PyBullet for complex dynamics. Unlike Gazebo, which is a broader ROS 2 simulator, MuJoCo focuses on core physics. While NVIDIA Isaac Sim offers massive GPU-parallelism and photorealistic rendering, MuJoCo provides faster single-environment simulation and superior physics accuracy for articulated systems, with its own GPU acceleration via MuJoCo Warp. Brax, also from DeepMind, is a differentiable engine optimized for even larger-scale parallel RL simulations.

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MuJoCo Review

MuJoCo (Multi-Joint dynamics with Contact) is a free, open-source physics engine for fast, accurate simulation of articulated structures, excelling in contact-rich manipulation and reinforcement learning research.

shipped Jul 9, 2026aifree

Why it matters

1Free and open-source under the Apache 2.0 license since May 2022.

2Optimized for speed, capable of running simulations hundreds of times faster than real-time.

3Offers monthly releases, with Version 3.10.0 released on June 22, 2026, introducing `mju_threadpool`.

4Supports GPU acceleration through MuJoCo Warp and Python 3.14+.

About MuJoCo

Business Model

Open Source

Platforms

Web, Desktop

Target Audience

Researchers and developers in robotics, biomechanics, graphics, and animation

GitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is MuJoCo?

MuJoCo is a physics engine tool developed by Google DeepMind that enables researchers and developers to simulate articulated structures interacting with their environment. It is particularly strong for contact-rich manipulation and locomotion research, and widely used in robotics and reinforcement learning. MuJoCo (Multi-Joint dynamics with Contact) is a high-performance, open-source physics engine designed for fast and accurate simulation of articulated structures interacting with their environment. It serves as a general-purpose physics engine primarily facilitating research and development in robotics, biomechanics, graphics and animation, and machine learning. Its core strength lies in simulating complex dynamical systems, particularly those involving contact-rich interactions, making it a standard for continuous control and model-based computations.

features

Key Features of MuJoCo

MuJoCo provides a comprehensive set of features for high-fidelity physics simulation, catering to complex dynamic systems and advanced research needs.

Simulation in generalized coordinates, effectively avoiding joint violations.
Well-defined inverse dynamics, even in the presence of contacts.
Unified continuous-time formulation of constraints via convex optimization.
Comprehensive constraint types, including soft contacts, limits, dry friction, and equality constraints.
Capability to simulate particle systems, cloth, rope, and soft objects.
Diverse actuators, such as motors, cylinders, muscles, tendons, and slider-cranks.
Choice of Newton, Conjugate Gradient, or Projected Gauss-Seidel solvers for dynamics.
Options for pyramidal or elliptic friction cones, and dense or sparse Jacobians.
mju_threadpool for parallelizing collision detection and constraint solving across islands (introduced in v3.10.0).
Unified logging API for errors, warnings, and informational messages.

use cases

Who Should Use MuJoCo?

MuJoCo is designed for a range of technical users requiring precise and efficient simulation capabilities for articulated structures and contact dynamics.

Robotics Researchers and Developers: For simulating robotic hands, manipulation tasks, and legged locomotion, and for testing and validating control schemes before physical deployment.
Reinforcement Learning (RL) Practitioners: For training RL policies efficiently due to its speed (hundreds of times faster than real-time) and accurate contact model, ensuring reliable sim-to-real transfer.
Biomechanics Researchers: For conducting research and development in human and animal movement, analyzing complex biological systems.
Machine Learning Engineers: For model-based computations including control synthesis, state estimation, system identification, and mechanism design.
Graphics and Animation Professionals: For creating interactive virtual environments and animations requiring realistic physical interactions.

how to use

How to Use MuJoCo

MuJoCo is primarily used by researchers and developers who integrate its physics engine into their computational workflows. Getting started involves setting up the environment and defining simulation models.

1Download the MuJoCo software package from mujoco.org, which includes the C++ library and Python bindings.
2Install necessary dependencies, ensuring compatibility with Python 3.14+ for the latest features.
3Define simulation models using the MuJoCo XML format (MJCF) or programmatically through its C/C++ or Python API.
4Utilize the Python-based System Identification toolbox for analyzing and refining model parameters.
5Integrate with frameworks like MuJoCo Playground (released February 2025) for streamlined robot learning, training, and sim-to-real transfer.
6Leverage MuJoCo Warp for GPU-accelerated simulations to enhance performance for large-scale experiments.

pricing

MuJoCo Pricing & Plans

MuJoCo is available as a free, open-source physics engine. Following its acquisition by Google DeepMind in October 2021, the entire codebase was open-sourced in May 2022 under the Apache 2.0 license, making all its features accessible without cost.

Free: Provides the full, open-source physics engine for fast, accurate simulation of articulated structures, optimized for contact-rich manipulation and reinforcement learning research.

Pros

+High simulation fidelity and stability, particularly for continuous control and contact-rich scenarios.
+Exceptional computational speed, running hundreds of times faster than real-time, crucial for reinforcement learning.
+Free and open-source under the Apache 2.0 license, actively maintained and developed by Google DeepMind.
+Provides well-defined inverse dynamics, even in the presence of complex contacts.
+Supports GPU acceleration via MuJoCo Warp and parallelization with mju_threadpool for enhanced performance.
+Guarantees no training on user data, ensuring privacy and data security.

Cons

−The native graphical user interface (GUI) is minimal, often requiring programmatic model definition or XML for complex setups.
−Can present a steeper learning curve for users without a strong computational or physics background.
−Requires integration into custom projects or scripts for full utilization, rather than offering a standalone, user-friendly application.
−While it supports vision-based environments through integrations like Madrona, it does not natively provide photorealistic rendering capabilities like some competitors.

Policies

Free Tier

Vendor website advertises a free tier.

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MuJoCo vs Competitors

MuJoCo operates in a competitive landscape of physics engines and robotics simulators, each with distinct strengths. Its primary competitors include PyBullet, Gazebo, NVIDIA Isaac Sim, and Brax.

PyBullet↗

Provides a lightweight, open-source Python interface to the Bullet physics engine, enabling dynamic creation and simulation of physics-based environments for robotics and machine learning.

Similar to MuJoCo, PyBullet is popular for reinforcement learning research and supports loading URDFs and MJCFs. It is often considered more accessible and lightweight, though some sources suggest MuJoCo offers higher simulation fidelity for certain complex contact scenarios.

GazeboOn Stork Compare

An open-source robot simulation software that offers robust capabilities for simulating and testing robotic systems in complex indoor and outdoor environments.

Gazebo provides a wider range of pre-built models and environments and supports multiple underlying physics engines (including ODE, Bullet, Simbody, and DART), making it highly versatile for general robotics simulation, whereas MuJoCo is particularly optimized for contact-rich manipulation and locomotion research.

NVIDIA Isaac Sim↗

A GPU-accelerated robotics simulation platform built on NVIDIA Omniverse, offering photorealistic RTX ray-traced rendering and advanced AI capabilities for synthetic data generation and robot learning.

While MuJoCo excels in accurate CPU-based dynamics (with a GPU-optimized MuJoCo-Warp now available), Isaac Sim leverages GPUs for massive parallel simulations, providing high fidelity and scalability for AI-enabled robotics, albeit with potentially higher hardware requirements and a steeper learning curve.

BraxOn Stork Compare

An open-source, differentiable physics engine written in JAX, specifically designed for reinforcement learning and optimized for efficiently running thousands of parallel physics simulations on a single accelerator.

Developed by Google DeepMind, like MuJoCo, Brax shares a similar focus on RL but is built from the ground up for differentiability and GPU/TPU acceleration, enabling significantly larger-scale parallel simulations for training RL agents.

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GitHubgithub.com/google-deepmind/mujoco/releases

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