Real-World Robot Learning with Masked Visual Pre-trainingDownload PDF

16 Jun 2022, 10:45 (modified: 16 Nov 2022, 03:30)CoRL 2022 OralReaders: Everyone
Student First Author: yes
Keywords: Self-Supervised Learning, Visual Representations, Robot Learning
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.
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