  Shared dynamics models are important for capturing the complexity and
variability inherent in Human-Robot Interaction (HRI). Therefore, learning such
shared dynamics models can enhance coordination and adaptability to enable
successful reactive interactions with a human partner. In this work, we propose
a novel approach for learning a shared latent space representation for HRIs
from demonstrations in a Mixture of Experts fashion for reactively generating
robot actions from human observations. We train a Variational Autoencoder (VAE)
to learn robot motions regularized using an informative latent space prior that
captures the multimodality of the human observations via a Mixture Density
Network (MDN). We show how our formulation derives from a Gaussian Mixture
Regression formulation that is typically used approaches for learning HRI from
demonstrations such as using an HMM/GMM for learning a joint distribution over
the actions of the human and the robot. We further incorporate an additional
regularization to prevent "mode collapse", a common phenomenon when using
latent space mixture models with VAEs. We find that our approach of using an
informative MDN prior from human observations for a VAE generates more accurate
robot motions compared to previous HMM-based or recurrent approaches of
learning shared latent representations, which we validate on various HRI
datasets involving interactions such as handshakes, fistbumps, waving, and
handovers. Further experiments in a real-world human-to-robot handover scenario
show the efficacy of our approach for generating successful interactions with
four different human interaction partners.
