Learning Multi-Class Dynamics

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Andrew Blake, Ben North, Michael Isard


Standard techniques (eg. Yule-Walker) are available for learning Auto-Regressive process models of simple, directly observable, dy(cid:173) namical processes. When sensor noise means that dynamics are observed only approximately, learning can still been achieved via Expectation-Maximisation (EM) together with Kalman Filtering. However, this does not handle more complex dynamics, involving multiple classes of motion. For that problem, we show here how EM can be combined with the CONDENSATION algorithm, which is based on propagation of random sample-sets. Experiments have been performed with visually observed juggling, and plausible dy(cid:173) namical models are found to emerge from the learning process.