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Conference Paper

Probabilistic Modeling of Human Movements for Intention Inference

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84300

Wang,  Z
Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

Deisenroth,  MP
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

Ben Amor H, Vogt D, Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

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

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Citation

Wang, Z., Deisenroth, M., Ben Amor H, Vogt D, Schölkopf, B., & Peters, J. (2013). Probabilistic Modeling of Human Movements for Intention Inference. In Roy, N., P. Newman, & S. Srinivasa (Eds.), Robotics: Science and Systems VIII (pp. 433-440). Cambridge, MA, USA: MIT Press.


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.