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.
@article{arxiv.1805.07719,
title = {Balancing Shared Autonomy with Human-Robot Communication},
author = {Rosario Scalise and Yonatan Bisk and Maxwell Forbes and Daqing Yi and Yejin Choi and Siddhartha Srinivasa},
journal= {arXiv preprint arXiv:1805.07719},
year = {2018}
}