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Probabilistic models for time-series with different underlying dynamics regimes with application to robot imitation learning

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In this talk I will describe several probabilistic models for analysing time-series containing different underlying dynamics regimes, and discuss approximation schemes for dealing with intractable inference. I will first present a Bayesian approach to switching and mixtures of linear dynamical systems (LDS) for incorporating prior domain knowledge and enforcing a sparse parametrisation. I will then introduce an extension of the switching LDS with regime-duration-distribution and time-warping modeling for extracting repeated occurrences of basic shapes. I will show how these models can be applied to robot imitation learning.

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