Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning

Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus; it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper; we explore the limits of such an approach by investigating the problem of traversing stair-like terrain without any external perception or terrain models on a bipedal robot. For such blind bipedal platforms; the problem appears difficult (even for humans) due to the surprise elevation changes. Our main contribution is to show that sim-to-real reinforcement learning (RL) can achieve robust locomotion over stair-like terrain on the bipedal robot Cassie using only proprioceptive feedback. Importantly; this only requires modifying an existing flat-terrain training RL framework to include stair-like terrain randomization; without any changes in reward function. To our knowledge; this is the first controller for a bipedal; human-scale robot capable of reliably traversing a variety of real-world stairs and other stair-like disturbances using only proprioception.