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• W2808158640 abstract "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." @default.
• W2808158640 created "2018-06-21" @default.
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• W2808158640 date "2018-05-29" @default.
• W2808158640 modified "2023-09-27" @default.
• W2808158640 title "Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems" @default.
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