Minimizing user cost for shared autonomy

Abstract

In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. One often used strategy considers the user and autonomy as independent decision makers, with the system blending these decisions. However, this independence leads to suboptimal, and often frustrating, behavior. Instead, we propose a system that explicitly models the interplay between the user and assistance. Our approach centers around the idea of learning how users respond to assistance. We then propose a cost minimization framework for assisting while utilizing this learned model.