713 citations · 2.1k across the 11 of their papers we have counts for
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cs.LG2017★ 1 cited
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart +9
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative…
cs.RO2017★ 38 cited
End-to-End Learning of Semantic Grasping
Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor +2
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis…