CoRL 20170 citations
Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
Nima Fazeli, Samuel Zapolsky, Evan Drumwright, Alberto Rodriguez
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.
BibTeX
@inproceedings{corl2017_learningdataeffi,
title = {Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact},
author = {Nima Fazeli and Samuel Zapolsky and Evan Drumwright and Alberto Rodriguez},
booktitle = {CoRL 2017},
year = {2017}
}