  Using movement primitive libraries is an effective means to enable robots to
solve more complex tasks. In order to build these movement libraries, current
algorithms require a prior segmentation of the demonstration trajectories. A
promising approach is to model the trajectory as being generated by a set of
Switching Linear Dynamical Systems and inferring a meaningful segmentation by
inspecting the transition points characterized by the switching dynamics. With
respect to the learning, a nonparametric Bayesian approach is employed
utilizing a Gibbs sampler.
