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