Human robot motion: A shared effort approach

This paper is about Human Robot Motion (HRM), i.e. the study of how a robot should move among humans. This problem has often been solved by considering persons as moving obstacles, predicting their future trajectories and avoiding these trajectories. In contrast with such an approach, recent works have showed benefits of robots that can move and avoid collisions in a manner similar to persons, what we call human-like motion. One such benefit is that human-like motion was shown to reduce the planning effort for all persons in the environment, given that they tend to solve collision avoidance problems in similar ways. The effort required for avoiding a collision, however, is not shared equally between agents as it varies depending on factors such as visibility and crossing order. Thus, this work tackles HRM using the notion of motion effort and how it should be shared between the robot and the person in order to avoid collisions. To that end our approach learns a robot behavior using Reinforcement Learning that enables it to mutually solve the collision avoidance problem during our simulated trials.