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Data Augmentation Robotics
Artificially expanding a robot dataset by applying transformations that preserve label validity: image augmentations (crop, color jitter, blur), proprioceptive noise injection, action perturbations, and viewpoint synthesis. Data augmentation is critical for sample-efficient robot learning — random crop augmentation alone can double the effective dataset size and significantly improve policy generalization.