Perspective Taking: An Organizing Principle for Learning in Human-Robot Interaction

Abstract

The ability to interpret demonstrations from the perspective of the teacher plays a critical role in human learning. Robotic sys-tems that aim to learn effectively from human teachers must similarly be able to engage in perspective taking. We present an integrated architecture wherein the robot’s cognitive function-ality is organized around the ability to understand the environ-ment from the perspective of a social partner as well as its own. The performance of this architecture on a set of learning tasks is evaluated against human data derived from a novel study exam-ining the importance of perspective taking in human learning. Perspective taking, both in humans and in our architecture, fo-cuses the agent’s attention on the subset of the problem space that is important to the teacher. This constrained attention al-lows the agent to overcome ambiguity and incompleteness that can often be present in human demonstrations and thus learn what the teacher intends to teach.