Abstract

Quadrupedal robots have emerged as a cutting--edge platform for assisting humans, finding applications in tasks related to inspection and exploration in remote areas. Nevertheless, their floating base structure renders them susceptible to failure in cluttered environments, where manual recovery by a human operator may not always be feasible. Several recent studies have presented recovery controllers employing deep reinforcement learning algorithms. However, these controllers are not specifically designed to operate effectively in cluttered environments, such as stairs and slopes, which restricts their applicability. In this study, we propose a robust all-terrain recovery policy to facilitate rapid and secure recovery in cluttered environments. We substantiate the superiority of our proposed approach through simulations and real-world outdoor tests encompassing various terrain types.

Media Coverage

IEEE Spectrum Video Friday

Related Work

DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning

I Made Aswin Nahrendra, Byeongho Yu, and Hyun Myung

IEEE International Conference on Robotics and Automation (ICRA) 2023