Whole-Body Model Predictive Control for Biped Locomotion on a Torque-Controlled Humanoid Robot

Abstract

Locomotion of biped robots requires predictive controllers due to its unstable dynamics and physical limitations of contact forces. A real-time controller designed to perform complex motions while maintaining balance over feet must generate whole-body trajectories, predicting a few seconds in the future with a high enough updating rate to reduce model errors. Due to the huge computational power demanded by such solvers, future trajectories are usually generated using a reduced order model that contains the unstable dynamics. However, this simplification introduces feasibility problems on many edge cases. Considering the permanent improvement of computers and algorithms, whole-body locomotion in real-time is becoming a viable option for humanoids, and this article aims at illustrating this point. We propose a whole-body model predictive control scheme based on differential dynamic programming that takes into account the full dynamics of the system and decides the optimal actuation for the robot's lower body (20 degrees of freedom) along a preview horizon of 1.5 s. Our experimental validation on the torque-controlled robot Talos shows good and promising results for dynamic locomotion at different gaits as well as 10 cm height stairstep crossing.