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arXiv · 2008.11466

Self-Supervised Goal-Conditioned Pick and Place

Coline Devin·Payam Rowghanian·Chris Vigorito·Will Richards·Khashayar Rohanimanesh
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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.

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Coline Devin, Payam Rowghanian, Chris Vigorito, Will Richards, Khashayar Rohanimanesh. 2020-08-26. Self-Supervised Goal-Conditioned Pick and Place. https://arxiv.org/abs/2008.11466

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