接触モデルarXiv:1710.05947
データ効率の良い剛体接触モデルの学習：平面衝突のケーススタディ
Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
平面衝突の実験データを用いて、剛体接触モデルをデータ駆動型・データ補強型モデルと比較し、少ない学習データで学習モデルが解析モデルを上回ることを示した。
著者: Nima Fazeli, Samuel Zapolsky, Evan Drumwright, Alberto Rodriguez
分類: cs.RO
原文アブストラクト
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.
