  We introduce an imitation learning-based physical human-robot interaction
algorithm capable of predicting appropriate robot responses in complex
interactions involving a superposition of multiple interactions. Our proposed
algorithm, Blending Bayesian Interaction Primitives (B-BIP) allows us to
achieve responsive interactions in complex hugging scenarios, capable of
reciprocating and adapting to a hugs motion and timing. We show that this
algorithm is a generalization of prior work, for which the original formulation
reduces to the particular case of a single interaction, and evaluate our method
through both an extensive user study and empirical experiments. Our algorithm
yields significantly better quantitative prediction error and more-favorable
participant responses with respect to accuracy, responsiveness, and timing,
when compared to existing state-of-the-art methods.
