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Abstract

Socially assistive robots are designed to help people in social ways, for example through coaching or teaching. Because they operate in social environments, these robots must be able to understand and communicate with social cues that people use. Non-verbal behavior, such as eye gaze and gesture, can provide significant communication in social contexts. Our research investigates how people use non-verbal behaviors when teaching. We use data from human-human interactions to computationally model peoples' non-verbal behaviors, with the goal of constructing a generative robot behavior model based on this computational model of people. In this paper, we describe our work in progress and discuss challenges of real-time human-robot interaction.