Robots that Help Humans Build Better Mental Models of Robots

Abstract

Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot's knowledge, capabilities, shortcomings, goals, and intentions. The research question that we investigate is "How can the robot help the human build a better mental model of the robot?" We study human-robot interaction failures to understand the role of mental models in resolving them. We also discuss a human-centred interaction model design that is informed by human subject studies and plan-based theories of dialogue, specifically Collaborative Discourse Theory.