Data Augmentation (for robotics)

Data augmentation in robot learning applies random transformations to training observations to improve policy robustness without collecting additional demonstrations. Common image augmentations include random cropping, color jitter, Gaussian blur, and cutout. More sophisticated augmentations overlay distracting backgrounds, change lighting conditions, or inject sensor noise to prevent overfitting to specific visual features in the training environment. Some approaches augment actions as well — for example, adding noise to joint trajectories to teach the policy to recover from perturbations. Augmentation is especially important when training data is expensive (each demonstration requires human operator time).