Robotics: Science and Systems VIII

Probabilistic Modeling of Human Movements for Intention Inference

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


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.



    AUTHOR    = {Zhikun Wang AND Marc Deisenroth AND Heni Ben Amor AND David Vogt AND Bernhard Scholkopf AND Jan Peters}, 
    TITLE     = {Probabilistic Modeling of Human Movements for Intention Inference}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2012}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2012.VIII.055}