  This paper presents a learning-from-demonstration (LfD) framework for
teaching human-robot social interactions that involve whole-body haptic
interaction, i.e. direct human-robot contact over the full robot body. The
performance of existing LfD frameworks suffers in such interactions due to the
high dimensionality and spatiotemporal sparsity of the demonstration data. We
show that by leveraging this sparsity, we can reduce the data dimensionality
without incurring a significant accuracy penalty, and introduce three
strategies for doing so. By combining these techniques with an LfD framework
for learning multimodal human-robot interactions, we can model the
spatiotemporal relationship between the tactile and kinesthetic information
during whole-body haptic interactions. Using a teleoperated bimanual robot
equipped with 61 force sensors, we experimentally demonstrate that a model
trained with 121 sample hugs from 4 participants generalizes well to unseen
inputs and human partners.
