Self-Supervised Goal-Conditioned Pick and Place
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Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious 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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