  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.
