  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.
