Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning

arXiv:2309.14246v2 Announce Type: replace-cross
Abstract: Deployment in hazardous environments requires robots to understand the risks associated with their actions and movements to prevent accidents. Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. In this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Instead of relying on a value expectation, we estimate the complete value distribution to account for uncertainty in the robot's …

arxiv cs.lg cs.ro reinforcement reinforcement learning risk type