Teaching a humanoid robot to walk faster through Safe Reinforcement Learning

Teaching a humanoid robot to walk is an open and challenging problem. Classical walking behaviors usually require the tuning of many control parameters (e.g., step size, speed). To find an initial or basic configuration of such parameters could not be so hard, but optimizing them for some goal (for instance, to walk faster) is not easy because, when defined incorrectly, may produce the fall of the humanoid, and the consequent damages. In this paper we propose the use of Safe Reinforcement Learning for improving the walking behavior of a humanoid that permits the robot to walk faster than with a pre-defined configuration. Safe Reinforcement Learning assumes the existence of a safe baseline policy that permits the humanoid to walk, and probabilistically reuse such a policy to learn a better one, which is represented following a case based approach. The proposed algorithm has been evaluated in a real humanoid robot proving that it drastically increases the learning speed while reduces the number of falls during learning when compared with state-of-the-art algorithms.