M. Berlin, C. Breazeal, C. Chao

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 via a human subjects experiment which examines our humanoid robot’s ability to learn from live interactions with human teachers in a secret-constraint task domain. This evaluation provides quantitative evidence for the utility of spatial scaffolding cues to systems that learn from natural human teaching behavior.