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

TL;DR

This paper introduces a nonparametric Bayesian method for segmenting trajectories in robotic movement data by modeling them with Hierarchical Dirichlet Process Switching Linear Dynamical Systems, enabling automatic discovery of meaningful segments.

Contribution

It proposes a novel nonparametric Bayesian approach using Hierarchical Dirichlet Process switching linear dynamical systems for trajectory segmentation.

Findings

Effective segmentation of demonstration trajectories

Automatic inference of transition points in movement data

Improved modeling of complex robotic movements

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.