Learning multi-class dynamics

A. Blake, B. North, and M. Isard

Abstract

Standard techniques (Yule-Walker) are available for learning Auto-Regressive process models of dynamical processes. When sensor noise means that dynamics are observed only approximately, learning has still been achieved via Expectation-Maximisation (EM) together with Kalman Filtering. This cannot handle more complex dynamics, involving multiple classes of motion. For that case, we show here how EM can be combined with the sc Condensation algorithm, which is based on propagation of random sample-sets. Experiments have been performed with visually observed juggling, and plausible dynamical models are found to emerge from the learning process.

Details

Publication typeInproceedings
Published inAdvances in Neural Information Processing Systems,
Pages389–395
PublisherMIT Press
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