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This paper presents a learning-from-demonstration (LfD) framework forteaching human-robot social interactions that involve whole-body hapticinteraction, i.e. direct human-robot contact over the full robot body. Theperformance of existing LfD frameworks suffers in such interactions due to thehigh dimensionality and spatiotemporal sparsity of the demonstration data. Weshow that by leveraging this sparsity, we can reduce the data dimensionalitywithout incurring a significant accuracy penalty, and introduce threestrategies for doing so. By combining these techniques with an LfD frameworkfor learning multimodal human-robot interactions, we can model thespatiotemporal relationship between the tactile and kinesthetic informationduring whole-body haptic interactions. Using a teleoperated bimanual robotequipped with 61 force sensors, we experimentally demonstrate that a modeltrained with 121 sample hugs from 4 participants generalizes well to unseeninputs and human partners.

Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts