Inverse Optimal Control for Humanoid Locomotion

Abstract—In this paper, we present a method for learning the reward function for humanoid locomotion from motion-captured demonstrations of human running. We show how an approximate, local inverse optimal control algorithm can be used to learn the reward function for this high dimensional domain, and demonstrate how trajectory optimization can then be used to recreate dynamic, naturalistic running behaviors in new environments. Results are presented in simulation on a 29-DoF humanoid model, and include running on flat ground, rough terrain, and under strong lateral perturbation. I