Trajectory representation by nonlinear scaling of dynamic movement primitives
Aleš Ude, Rok Vuga, Bojan Nemec, Jun Morimoto
Abstract
An effective robot trajectory representation should encode all relevant aspects of the desired motion. For kinematic representations, this means that both the spatial course of the trajectory and its speed profile must be specified. The concept of dynamic movement primitives (DMP) provides a kinematic representation that fully specifies these two aspects of motion. They are, however, not separated from each other within the DMP representation. This can be problematic when movements with significant speed variations are compared within movement recognition and skill learning algorithms. In such comparisons it is often important to distinguish between the spatial and temporal aspects of motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects are well separated. We demonstrate the effectiveness of the proposed representation for statistical learning of robot skills and movement recognition and compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.
BibTeX
@inproceedings{iros2016_trajectoryrepres,
title = {Trajectory representation by nonlinear scaling of dynamic movement primitives},
author = {Aleš Ude and Rok Vuga and Bojan Nemec and Jun Morimoto},
booktitle = {IROS 2016},
year = {2016}
}