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We introduce an imitation learning-based physical human-robot interactionalgorithm capable of predicting appropriate robot responses in complexinteractions involving a superposition of multiple interactions. Our proposedalgorithm, Blending Bayesian Interaction Primitives (B-BIP) allows us toachieve responsive interactions in complex hugging scenarios, capable ofreciprocating and adapting to a hugs motion and timing. We show that thisalgorithm is a generalization of prior work, for which the original formulationreduces to the particular case of a single interaction, and evaluate our methodthrough both an extensive user study and empirical experiments. Our algorithmyields significantly better quantitative prediction error and more-favorableparticipant responses with respect to accuracy, responsiveness, and timing,when compared to existing state-of-the-art methods.

Learning and Blending Robot Hugging Behaviors in Time and Space