MuJoCo

DeepMind physics engine for robotics and ML research

License

Apache-2.0

Overview

DeepMind's physics engine for research in robotics, biomechanics, and machine learning. Known for fast, accurate contact dynamics simulation with automatic differentiation support for gradient-based optimization.

MuJoCo (Multi-Joint dynamics with Contact) is DeepMind's advanced physics engine optimized for robotics simulation, biomechanics research, and machine learning applications requiring accurate contact dynamics. The engine implements fast and stable contact modeling that handles complex multi-body interactions including friction, joint limits, and constraints with high numerical accuracy. A key differentiating feature is native support for automatic differentiation, enabling gradient-based optimization directly through the physics simulation for applications like trajectory optimization and system identification. MuJoCo balances computational speed with physical accuracy, making it suitable both for rapid reinforcement learning training with thousands of parallel simulations and for detailed biomechanics modeling requiring precise dynamics. The combination of performance, accuracy, and differentiability has made MuJoCo a foundational tool in the robotics research community. The current release, MuJoCo 4.0.0 (6 November 2026), adds speculative contacts, which keep fast-moving convex shapes from passing through thin obstacles within a timestep, and elasto-plasticity, which lets joint and tendon springs and 2D and 3D flexes deform permanently. It removes the cable plugin and the composite element in favour of one-dimensional flexcomp with elasticity, which changes the qpos layout of elastic rods, and it splits the Python package into mujoco-core and mujoco-studio, with mujoco installing both. MuJoCo Warp, the GPU-accelerated version of MuJoCo for NVIDIA hardware, was released as v4.0.0 alongside it.

Why it matters

MuJoCo is the de facto standard physics engine for robot learning research. Acquired by DeepMind and made open-source, it combines the speed needed for RL training with the accuracy needed for contact-rich manipulation, and its C API is the foundation for dozens of simulation frameworks.

Problem it solves

Robot learning requires a physics engine that balances speed (for RL training) with accuracy (for realistic contact). MuJoCo provides both, with stable contact dynamics that other engines struggle to match, especially for dexterous manipulation.

Key takeaways

• Industry-standard physics engine used in thousands of research papers
• Fast, stable contact dynamics ideal for reinforcement learning
• First-class support for differentiable simulation via MJX (JAX backend)
• Open-sourced by DeepMind with active development and community
• Native support for tendons, actuators, and complex kinematic trees

Who it is for

• Robot learning researchers at all levels
• Teams building manipulation and locomotion policies
• Developers of simulation benchmarks and environments
• Companies prototyping control algorithms before hardware deployment

Limitations

• Rendering is functional rather than photorealistic
• Deformable object simulation is limited compared to specialized engines
• XML model format (MJCF) has a learning curve
• GPU parallelization runs through the separate MJX and MuJoCo Warp backends; batched multi-threaded CPU stepping is built in since 3.17