Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

Abstract-- In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocitybased controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. Siekmann et al. have also shown that it is possible to I. INTRODUCTION Additionally, Mobile ground robots have been widely studied and used we investigate the concept of a boolean mode switch for for various tasks, such as delivery, inspection, and security, stair-climbing, thereby allowing a good performance on both [1]. While wheeled robots are efficient at traveling regular terrain and stair ascent with the same control policy.