Understanding Intentions in Human Teaching to Design Interactive Task Learning Robots

The goal of Interactive Task Learning (ITL) is to build robots that can be trained in new tasks by human instructors. In this paper, we approach the ITL research problem from a human instructor perspective. The research question that we address here is how do we understand and leverage the intentionality of the instructors to enable natural and flexible ITL. We propose a taxonomy based on Collaborative Discourse Theory that organizes human teaching intentions in a human robot teaching interaction. This taxonomy will provide guidance for ITL robot design that leverages a human’s natural teaching skills, and reduces the cognitive burden of non-expert instructors. We propose human participant studies to validate this taxonomy and gain a comprehensive understanding of teaching interactions in ITL.