Plan or not: Remote Human-robot Teaming with Incomplete Task Information

TL;DR

This paper explores how autonomous planning enables remote human-robot teams to collaborate effectively despite incomplete task information, balancing autonomy and human oversight to optimize performance.

Contribution

It introduces a framework for peer-to-peer remote human-robot teaming with autonomous planning under incomplete information, highlighting trade-offs in autonomy and team dynamics.

Findings

Autonomous planning improves team coordination in remote settings.

Incomplete information affects robot decision-making and team performance.

Trade-offs exist between robot autonomy and human workload.

Abstract

Human-robot interaction can be divided into two categories based on the physical distance between the human and robot: remote and proximal. In proximal interaction, the human and robot often engage in close coordination; in remote interaction, the human and robot are less coupled due to communication constraints. As a result, providing automation for the robot in remote interaction becomes more important. Thus far, human factor studies on automation in remote human-robot interaction have been 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 paper, we investigate the incorporation of 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. The human and robot share the same global goal and collaborate to achieve it. Note that humans may feel uncomfortable at such robot autonomy, which can potentially reduce teaming performance. One important difference between peer-to-peer teaming and supervised teaming is that an autonomous robot in peer-to-peer teaming can achieve the goal alone when the task information is completely specified. However, incompleteness often exists, which implies information asymmetry. While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance. We aim to investigate the various trade-offs, e.g., mental workload and situation awareness, between these two types of remote human-robot teaming.