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  Probabilistic Modeling of Human Movements for Intention Inference

Wang, Z., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2012). Probabilistic Modeling of Human Movements for Intention Inference. In N. Roy, P. Newman, & S. Srinivasa (Eds.), Robotics: Science and Systems VIII (pp. 433-440). Cambridge, MA, USA: MIT Press.

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 Creators:
Wang, Z1, Author              
Deisenroth, MP, Author              
Ben Amor, H, Author
Vogt, D, Author
Schölkopf, Bernhard1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: 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.

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 Dates: 2012-07
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: WangDBVSP2012
DOI: 10.15607/RSS.2012.VIII.055
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Title: 2012 Robotics: Science and Systems Conference
Place of Event: Sydney, Australia
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Title: Robotics: Science and Systems VIII
Source Genre: Proceedings
 Creator(s):
Roy, N, Editor
Newman, P, Editor
Srinivasa, S, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 433 - 440 Identifier: ISBN: 978-0-262-51968-7