Abstract
<h2>Abstract</h2> 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.
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