Learning and Blending Robot Hugging Behaviors in Time and Space
This work addresses the challenge of responsive physical human-robot interaction in social robotics, though it is incremental as it generalizes prior methods.
The paper tackles the problem of enabling robots to respond appropriately in complex hugging interactions by predicting and blending multiple interaction primitives, achieving significantly better prediction error and more favorable user responses compared to existing methods.
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.