  Human-robot interaction benefits greatly from multimodal sensor inputs as
they enable increased robustness and generalization accuracy. Despite this
observation, few HRI methods are capable of efficiently performing inference
for multimodal systems. In this work, we introduce a reformulation of
Interaction Primitives which allows for learning from demonstration of
interaction tasks, while also gracefully handling nonlinearities inherent to
multimodal inference in such scenarios. We also empirically show that our
method results in more accurate, more robust, and faster inference than
standard Interaction Primitives and other common methods in challenging HRI
scenarios.
