Nonverbal behavior modeling for socially assistive robots

Abstract

The field of socially assistive robotics (SAR) aims to build robots that help people through social interaction. Human social interaction involves complex systems of behavior, and modeling these systems is one goal of SAR. Nonverbal behaviors, such as eye gaze and gesture, are particularly amenable to modeling through machine learning because the effects of the system — the nonverbal behaviors themselves — are inherently observable. Uncovering the underlying model that defines those behaviors would allow socially assistive robots to become better interaction partners. Our research investigates how people use nonverbal behaviors in tutoring applications. We use data from human-human interactions to build a model of nonverbal behaviors using supervised machine learning. This model can both predict the context of observed behaviors and generate appropriate nonverbal behaviors