google-deepmind/mujoco_mpc

Health v2 · maintenance only

Adoption not part of the score

1713 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MuJoCo MPC (MJPC) is an interactive application and C++ software framework from Google DeepMind for real-time predictive control with the MuJoCo physics simulator. It supports multiple shooting-based planners including derivative-based methods (iLQG, Gradient Descent) and a derivative-free Predictive Sampling planner, with a GUI for authoring and solving robotics tasks.

Use cases

• solve quadruped locomotion tasks with model-predictive control
• control bimanual robotic manipulation in simulation
• track humanoid motion capture in real time
• author and solve complex robotics tasks in MuJoCo
• compare shooting-based MPC planners like iLQG and Predictive Sampling
• experiment with real-time behaviour synthesis for robots

When to choose

• you need real-time model-predictive control with MuJoCo
• you want a GUI to interactively author and solve robotics control tasks
• you need shooting-based planners such as iLQG, Gradient Descent, or Predictive Sampling
• you are doing robotics research on locomotion, manipulation, or motion tracking

When to avoid

• you need a lightweight headless control library without a GUI
• your project does not use MuJoCo as its physics engine
• you need sampling-based motion planning rather than predictive control
• you require Windows support, which is not a tested platform

Facets

application · maturity active

simulation robotics gui developer-tools robotics simulation machine-learning cpp python model-predictive-control mujoco ilqg predictive-sampling robotics-tasks real-time-control linux macos

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markdown · JSON · MCP: product_card(name="google-deepmind/mujoco_mpc")