This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Google Scholar Articles by Kulic, D. Articles by Nakamura, Y. Social Bookmarking                   What's this?

Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

Dana Kuli

Department of Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, dana{at}ynl.t.u-tokyo.ac.jp

Wataru Takano

Department of Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, takano{at}ynl.t.u-tokyo.ac.jp

Yoshihiko Nakamura

Department of Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, nakamura{at}ynl.t.u-tokyo.ac.jp

This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.

Key Words: learning and adaptive systems • cognitive robotics • gesture • posture • social spaces and facial expressions • human-centred and life-like robotics • humanoid robots • recognition • sensing and perception • computer vision

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?