  The increasing presence of robots alongside humans, such as in human-robot
teams in manufacturing, gives rise to research questions about the kind of
behaviors people prefer in their robot counterparts. We term actions that
support interaction by reducing future interference with others as supportive
robot actions and investigate their utility in a co-located manipulation
scenario. We compare two robot modes in a shared table pick-and-place task: (1)
Task-oriented: the robot only takes actions to further its own task objective
and (2) Supportive: the robot sometimes prefers supportive actions to
task-oriented ones when they reduce future goal-conflicts. Our experiments in
simulation, using a simplified human model, reveal that supportive actions
reduce the interference between agents, especially in more difficult tasks, but
also cause the robot to take longer to complete the task. We implemented these
modes on a physical robot in a user study where a human and a robot perform
object placement on a shared table. Our results show that a supportive robot
was perceived as a more favorable coworker by the human and also reduced
interference with the human in the more difficult of two scenarios. However, it
also took longer to complete the task highlighting an interesting trade-off
between task-efficiency and human-preference that needs to be considered before
designing robot behavior for close-proximity manipulation scenarios.
