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Probabilistic Modeling of Human Movements for Intention Inference

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84300

Wang,  Z
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84381

Deisenroth,  MP
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Ben Amor H, Vogt D, Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Wang, Z., Deisenroth, M., Ben Amor H, Vogt D, Schölkopf, B., & Peters, J. (2012). Probabilistic Modeling of Human Movements for Intention Inference. In Robotics: Science and Systems VIII (pp. 1-8).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B6C8-5
Zusammenfassung
Inference of human intention may be an essential step towards understanding human actions [21] and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human behaviors/actions and we introduce an approximate inference algorithm to efficiently infer the human’s intention from an ongoing action. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification.