Variable Stiffness for Robust Locomotion through Reinforcement Learning

Reinforcement-learned locomotion enables legged robots to perform highly dynamic motions but often accompanies time-consuming manual tuning of joint stiffness. This paper introduces a novel control paradigm that integrates variable stiffness into the action space alongside joint positions, enabling grouped stiffness control such as per-joint stiffness (PJS), per-leg stiffness (PLS) and hybrid joint-leg stiffness (HJLS). We show that variable stiffness policies, with grouping in per-leg stiffness (PLS), outperform position-based control in velocity tracking and push recovery. In contrast, HJLS excels in energy efficiency. Furthermore, our method showcases robust walking behaviour on diverse outdoor terrains by sim-to-real transfer, although the policy is sorely trained on a flat floor. Our approach simplifies design by eliminating per-joint stiffness tuning while keeping competitive results with various metrics.