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Using movement primitive libraries is an effective means to enable robots tosolve more complex tasks. In order to build these movement libraries, currentalgorithms require a prior segmentation of the demonstration trajectories. Apromising approach is to model the trajectory as being generated by a set ofSwitching Linear Dynamical Systems and inferring a meaningful segmentation byinspecting the transition points characterized by the switching dynamics. Withrespect to the learning, a nonparametric Bayesian approach is employedutilizing a Gibbs sampler.

Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems