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.

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In: Advances in Neural Information Processing Systems,

Publisher: MIT Press

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