Probabilistic modeling of human movements for intention inference

Zhikun Wang, Marc Peter Deisenroth, Hani Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Inference of human intention may be an essential step towards understanding human actions 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 movements/actions. We introduce an efficient approximate inference algorithm to infer the human's intention from an ongoing movement. 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.

Original languageEnglish (US)
Title of host publicationRobotics: Science and Systems
PublisherMIT Press Journals
Pages433-440
Number of pages8
Volume8
ISBN (Print)9780262519687
StatePublished - 2013
Externally publishedYes
EventInternational Conference on Robotics Science and Systems, RSS 2012 - Sydney, Australia
Duration: Jul 9 2012Jul 13 2012

Other

OtherInternational Conference on Robotics Science and Systems, RSS 2012
CountryAustralia
CitySydney
Period7/9/127/13/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Wang, Z., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2013). Probabilistic modeling of human movements for intention inference. In Robotics: Science and Systems (Vol. 8, pp. 433-440). MIT Press Journals.