Robot Learning

Data Augmentation

Data Augmentation is the practice of expanding a training dataset by applying label-preserving transformations to existing samples. In robot learning this spans image-level perturbations such as color jitter, random cropping, and noise injection, as well as robotics-specific strategies like mirroring bimanual trajectories, replaying demonstrations from novel simulated viewpoints, and randomizing distractor objects. Random crop augmentation was a key ingredient in sample-efficient visual RL methods such as DrQ.

Why it matters for physical AI

Real robot data is expensive to collect, so squeezing more generalization out of every demonstration is critical. Well-chosen augmentations improve robustness to lighting, camera pose, and scene variation without additional hardware time.