C. Breazeal, M. Berlin

Abstract

Spatial scaffolding is a naturally occurring human teaching behavior, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner. Robotic systems can take advantage of simple, highly reliable spatial scaffolding cues to learn from human teachers. We present an integrated robotic architecture that combines social attention and machine learning components to learn tasks effectively from natural spatial scaffolding interactions with human teachers. We evaluate the performance of this architecture in comparison to human learning data drawn from a novel study of the use of embodied cues in human task learning and teaching behavior. This evaluation provides quantitative evidence for the utility of spatial scaffolding to learning systems. In addition, this evaluation supported the construction of a novel, interactive demonstration of a humanoid robot taking advantage of spatial scaffolding cues to learn from natural human teaching behavior.