Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion

Abstract-- Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and highdimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning can address this issue by analyzing key problems and providing solutions using both current literature and novel techniques. I. INTRODUCTION Locomotion on simulated musculoskeletal humanoids requires precise muscle activation patterns. Humanoid model with 16 DOFs actuated by 92 Muscle-Tendon Units during running (left) and walking (right).