Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
Abstract
In this paper we demonstrate the limitations of common rigid-body contact models used in the robotics community by comparing them to a collection of data-driven and data-reinforced models that exploit underlying structure inspired by the rigid contact paradigm. We evaluate and compare the analytical and data-driven contact models on an empirical planar impact data-set, and show that the learned models are able to outperform their analytical counterparts with a small training set.
Cite
Text
Fazeli et al. "Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact." Conference on Robot Learning, 2017.Markdown
[Fazeli et al. "Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact." Conference on Robot Learning, 2017.](https://mlanthology.org/corl/2017/fazeli2017corl-learning/)BibTeX
@inproceedings{fazeli2017corl-learning,
title = {{Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact}},
author = {Fazeli, Nima and Zapolsky, Samuel and Drumwright, Evan M. and Rodriguez, Alberto},
booktitle = {Conference on Robot Learning},
year = {2017},
pages = {388-397},
url = {https://mlanthology.org/corl/2017/fazeli2017corl-learning/}
}