MuJoCo MPC

DeepMind's interactive real-time model predictive control framework for MuJoCo

License

Apache-2.0

Overview

MuJoCo MPC (MJPC) is an interactive application and software framework from Google DeepMind for real-time predictive control using the MuJoCo physics engine. It supports multiple planning algorithms including iLQG, Gradient Descent, and derivative-free Predictive Sampling, all accessible through an interactive GUI for rapid task authoring and parameter tuning. Validated on complex tasks including quadruped locomotion, bimanual manipulation, and dexterous hand control.

MuJoCo MPC is Google DeepMind's interactive framework that enables real-time predictive control for complex robotic tasks using the MuJoCo physics engine. It provides both derivative-based (iLQG, gradient descent) and derivative-free (predictive sampling) planners, all accessible through an interactive GUI for rapid experimentation.

Why it matters

MJPC makes state-of-the-art model predictive control accessible and interactive. Coming from the team that built MuJoCo itself, it represents the reference implementation for MPC-based robot control and bridges the gap between offline trajectory optimization and real-time behavior synthesis.

Problem it solves

Designing controllers for complex robotic tasks traditionally requires extensive manual tuning. MuJoCo MPC automates behavior synthesis through online optimization, computing optimal actions in real time by predicting future outcomes in the physics simulator.

Key takeaways

• Multiple MPC algorithms: iLQG, gradient descent, predictive sampling
• Interactive GUI for real-time task authoring and tuning
• Validated on locomotion, manipulation, and dexterous hand tasks
• Python bindings for integration with ML pipelines

Who it is for

• Robotics researchers working on control and motion planning
• Engineers experimenting with MPC approaches for locomotion
• MuJoCo users who want real-time predictive control

Limitations

• Requires MuJoCo as the physics backend
• Real-time performance depends on model complexity and horizon length
• C++ build process can be challenging on some platforms