  Robotic agents that share autonomy with a human should leverage human domain
knowledge and account for their preferences when completing a task. This extra
knowledge can dramatically improve plan efficiency and user-satisfaction, but
these gains are lost if communicating with a robot is taxing and unnatural. In
this paper, we show how viewing humanrobot language through the lens of shared
autonomy explains the efficiency versus cognitive load trade-offs humans make
when deciding how cooperative and explicit to make their instructions.
