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- W4303648978 abstract "In this work, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our visual representations are pre-trained via a masked autoencoder (MAE), frozen, and then passed into a learnable control module. Unlike prior work, we show that the pre-trained representations are effective across a range of real-world robotic tasks and embodiments. We find that our encoder consistently outperforms CLIP (up to 75%), supervised ImageNet pre-training (up to 81%), and training from scratch (up to 81%). Finally, we train a 307M parameter vision transformer on a massive collection of 4.5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning." @default.
- W4303648978 created "2022-10-08" @default.
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- W4303648978 date "2022-10-06" @default.
- W4303648978 modified "2023-09-26" @default.
- W4303648978 title "Real-World Robot Learning with Masked Visual Pre-training" @default.
- W4303648978 doi "https://doi.org/10.48550/arxiv.2210.03109" @default.
- W4303648978 hasPublicationYear "2022" @default.
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