Unpacking Human Teachers' Intentions For Natural Interactive Task Learning

TL;DR

This paper explores how human teachers naturally instruct robots in interactive task learning, analyzing teaching behaviors and proposing design requirements for more effective, personalized robot learning systems.

Contribution

It provides a qualitative analysis of human teaching actions and discusses personalized adaptation needs for ITL robot design based on collaborative interaction theories.

Findings

Human teachers exhibit diverse teaching actions and styles.

Personalized adaptation is crucial for effective human-robot teaching.

Design guidelines for ITL robots are derived from collaborative interaction analysis.

Abstract

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.