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Real-World Robot Learning with Masked Visual Pre-training
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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.
Forward citations
Cited by 3 Pith papers
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Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation
Slot-based object-centric visual representations, especially with robot-video pretraining, improve out-of-distribution generalization of robotic manipulation policies compared to global and dense pre-trained features.
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Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
MSDP pre-trains a transformer encoder with masked multisensory autoencoding, then uses an asymmetric actor-critic bridge (cross-attention for critic, pooling for actor) to accelerate and robustify contact-rich RL acro...
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Is an object-centric representation beneficial for robotic manipulation ?
Evaluating the object-centric SAVi encoder against the global DINO and R3M representations on three simulated manipulation tasks, the authors find SAVi is the only model to solve the pick task and is more robust to un...
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