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MuJoCo MPC (MJPC) is an advanced interactive framework for real-time model predictive control (MPC) built on top of the MuJoCo physics engine, developed by Google DeepMind. It allows researchers and roboticists to design, visualize, and execute complex control tasks for simulated or real robotic systems. MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that achieves robust real-time control. The system supports multi-shooting optimization, enabling precise motion planning across diverse domains like quadruped locomotion, humanoid tracking, and dexterous manipulation. In addition to its C++ core, MJPC includes an experimental Python API, enabling integration with custom models and MuJoCo tasks for flexible scripting and experimentation.
Features
- Real-time model predictive control with multiple planners (iLQG, Gradient Descent, Predictive Sampling)
- Interactive graphical user interface for visualizing tasks and trajectories
- Multi-shooting optimization framework for robust control over long horizons
- Experimental Python API for task scripting and MuJoCo integration
- Support for complex control tasks (quadruped, humanoid, manipulation, motion tracking)
- Fully open-source and extensible research prototype for robotics control
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