Learning Forward and Inverse Kinematics Maps Efficiently
Daniel Kubus, Rania Rayyes, Jochen J. Steil
Abstract
When learning forward and inverse kinematics maps of manipulators, usually little attention is paid to data-efficiency, i.e., the accuracy gained per action-outcome sample. This paper examines properties of popular (online) learning techniques and demonstrates that - regardless of the employed exploration strategy - the structure of kinematics mappings does not allow for a practically viable trade-off between the number of samples and the resulting approximation error for manipulators with more than a few DoFs - unless tailored parametric models are employed. We discuss suitable choices for these parametric models for both rigid and elastic discretely-actuated robots and compare their data -efficiency to that of popular exploratory learning approaches relying on non-parametric models. Our theoretical considerations are confirmed by various experimental results for inverse kinematics mappings of rigid and omnielastic manipulators.
BibTeX
@inproceedings{iros2018_learningforwarda,
title = {Learning Forward and Inverse Kinematics Maps Efficiently},
author = {Daniel Kubus and Rania Rayyes and Jochen J. Steil},
booktitle = {IROS 2018},
year = {2018}
}