Transfer Learning

Transfer learning in robotics involves taking a model pretrained on one domain (e.g., internet vision-language data, simulation, or a different robot) and adapting it to a target task or robot with limited additional data. Fine-tuning the final layers of a pretrained backbone on robot demonstration data is the most common approach; full fine-tuning all weights is used when sufficient robot data is available. Transfer learning is the mechanism that makes foundation models practical for robotics — the alternative of training from scratch on robot data alone would require millions of demonstrations. See also pre-training, sim-to-real transfer.

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