Balancing Shared Autonomy with Human-Robot Communication
This work addresses the problem of optimizing human-robot interaction for users in shared autonomy settings, but it appears incremental as it builds on existing concepts without introducing a new method or paradigm.
The paper tackles the trade-off between efficiency and cognitive load in human-robot communication within shared autonomy systems, showing how this perspective explains human decisions on instruction cooperativeness and explicitness.
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.