Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
复制标题
学习数据高效的刚体接触模型：平面冲击案例研究
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alberto Rodriguez
中科院分区：
文献类型：
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作者：
Nima Fazeli;Samuel Zapolsky;Evan Drumwright;Alberto Rodriguez
在本文中，我们展示了在机器人社区中使用的常见刚体接触模型的局限性，通过将它们与一系列数据驱动和数据增强的模型进行比较，这些模型利用了刚性接触范式启发的底层结构。我们评估和比较的分析和数据驱动的接触模型的经验平面的影响数据集，并表明，学习的模型是能够超越他们的分析对手与一个小的训练集。
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.