Comparing Two Automatically Generated Explanations on the Perception of a Robot Teammate

Trust is critical to the success of human-robot interaction (HRI). Research has shown that people will more accurately trust a robot if they have a more accurate understanding of its decisionmaking process. Recent research has shown promise in calibrating human-agent trust by automatically generating explanations of decision-making process such as POMDP-based ones. In this paper, we compare two automatically generated explanations, one with quantitative information on uncertainty and one based on sensor observations, and study the impact of such explanations on perception of a robot in human-robot team.