  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 \emph{robot-centered} with a
focus on developing algorithms that can learn online, quickly. This paper
studies the ITL problem from a \emph{human-centered} perspective to provide
guidance for robot design so that human teachers can naturally teach ITL
robots. In this paper, we present 1) a qualitative bidirectional analysis of an
interactive teaching study (N=10) through which we characterize various aspects
of actions intended and executed by human teachers when teaching a robot; 2) an
in-depth discussion of the teaching approach employed by two participants to
understand the need for personal adaptation to individual teaching styles; and
3) requirements for ITL robot design based on our analyses and informed by a
computational theory of collaborative interactions, SharedPlans.
