脚式・車輪脚式ロボットのための強化学習によるブラインド階段昇降
Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots
強化学習を用いて、脚式・車輪脚式ロボットが外部センサに頼らず階段を昇降できる汎用コントローラを開発し、実機で15cmの階段昇降を実現した。
著者: Simon Chamorro, Victor Klemm, Miguel de la Iglesia Valls, Christopher Pal, Roland Siegwart
分類: cs.RO
原文アブストラクト
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 velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. 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. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot.
