Deep-LfD: Deep robot learning from demonstrations

Ghalamzan Esfahani, Amir, Nazari Sasikolomi, Kiyanoush, Hashempour, Hamidreza and Zhong, Fangxun (2021) Deep-LfD: Deep robot learning from demonstrations. Software Impacts, 9 . p. 100087. ISSN 2665-9638

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Deep-LfD: Deep robot learning from demonstrations
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Like other robot learning from demonstration (LfD) approaches, deep-LfD builds a task model from sample demonstrations. However, unlike conventional LfD, the deep-LfD model learns the relation between high dimensional visual sensory information and robot trajectory/path. This paper presents a dataset of successful needle insertion by da Vinci Research Kit into deformable objects based on which several deep-LfD models are built as a benchmark of models learning robot controller for the needle insertion task.

Keywords:Deep learning, Robot Learning from Demonstration, deformable object manipulation, surgical robots
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:45212
Deposited On:14 Jun 2021 11:26

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