Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems
Machine Learning
2020-03-03 v3 Machine Learning
Robotics
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.
Cite
@article{arxiv.1806.06063,
title = {Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems},
author = {Maximilian Sieb and Matthias Schultheis and Sebastian Szelag and Rudolf Lioutikov and Jan Peters},
journal= {arXiv preprint arXiv:1806.06063},
year = {2020}
}