Learning and Blending Robot Hugging Behaviors in Time and Space

• URL: http://arxiv.org/abs/2212.01507v2
• Date: Sat, 24 Aug 2024 18:29:13 GMT
• Title: Learning and Blending Robot Hugging Behaviors in Time and Space
• Authors: Michael Drolet, Joseph Campbell, Heni Ben Amor,
• Abstract summary: 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. 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. 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.
• Score: 10.014074169023058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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