Learning data-efficient rigid-body contact models: Case study of planar impact
Conference on Robot Learning, 2017•proceedings.mlr.press
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.
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.
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.
proceedings.mlr.press
以上显示的是最相近的搜索结果。 查看全部搜索结果