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Self-Supervised Goal-Conditioned Pick and Place

2020-08-26 09:51:44
Coline Devin, Payam Rowghanian, Chris Vigorito, Will Richards, Khashayar Rohanimanesh

Abstract

Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from autonomously collected data without human-labeled supervision. In this work we learn pixel-wise object representations from unsupervised pick and place data that generalize to new objects. We introduce a novel framework for using these representations in order to predict where to pick and where to place in order to match a goal image. Finally, we demonstrate the utility of our approach in a simulated grasping environment.

Abstract (translated)

URL

https://arxiv.org/abs/2008.11466

PDF

https://arxiv.org/pdf/2008.11466.pdf


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