Unpacking Human Teachers' Intentions For Natural Interactive Task Learning
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Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. Various research communities are contributing methods for ITL and a large subset of this research is robot-centered with a focus on developing algorithms that can learn online, quickly. This paper studies the ITL problem from a human-centered perspective to provide guidance for robot design so that human teachers can interact with ITL robots naturally. In this paper, we present 1) a cognitive task analysis of an interactive teaching study (N=10) that extracts and classify various actions intended and executed by human teachers when teaching a robot; 2) in-depth discussion of the teaching approach employed by two participants to understand the need for personal adaptation to individual styles; and 3) requirements for ITL robot design based on our analyses informed by plan-based theories of dialogue, specifically SharedPlans.
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