Automated Planning for Peer-to-peer Teaming and its Evaluation in Remote Human-Robot Interaction

Abstract

Human factor studies on remote human-robot interaction are often restricted to various forms of supervision, in which the robot is essentially being used as a smart mobile manipulation platform with sensing capabilities. In this study, we investigate the incorporation of a general planning capability into the robot to facilitate peer-to-peer human-robot teaming, in which the human and robot are viewed as teammates that are physically separated. One intriguing question is to what extent humans may feel uncomfortable at such robot autonomy and lose situation awareness, which can potentially reduce teaming performance. Our results suggest that peer-to-peer teaming is preferred by humans and leads to better performance. Furthermore, our results show that peer-to-peer teaming reduces cognitive loads from objective measures (even though subjects did not report this in their subjective evaluations), and it does not reduce situation awareness for short-term tasks.