Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
John Zhang, Taylor Howell, Zeji Yi, Chaoyi Pan, Guanya Shi, Guannan Qu, Tom Erez, Yuval Tassa
Abstract
We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model- predictive control (MPC) of quadruped and humanoid robots: the iterative linear-quadratic regulator (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time MPC while leveraging whole-body dynamics collision detection on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. Additionally, our GUI system enables users to interactively update robot behavior in real-time on the robot hardware, making task-specific objective parameter tuning easy and intuitive. Our code is available at:https://johnzhang3.github.io/mujoco_ilqr