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- W4383860659 abstract "The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that have broad generalization. Prior works have explored visual pre-training with different self-supervised objectives, but the generalization capabilities of the learned policies remain relatively unknown. In this work, we take the first step towards this challenge, focusing on how pre-trained representations can help the generalization of the learned policies. We first identify the key bottleneck in using a frozen pre-trained visual backbone for policy learning. We then propose SpawnNet, a novel two-stream architecture that learns to fuse pre-trained multi-layer representations into a separate network to learn a robust policy. Through extensive simulated and real experiments, we demonstrate significantly better categorical generalization compared to prior approaches in imitation learning settings." @default.
- W4383860659 created "2023-07-11" @default.
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- W4383860659 date "2023-07-07" @default.
- W4383860659 modified "2023-09-27" @default.
- W4383860659 title "SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks" @default.
- W4383860659 doi "https://doi.org/10.48550/arxiv.2307.03567" @default.
- W4383860659 hasPublicationYear "2023" @default.
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