RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation
RoboAug is a data augmentation framework that helps robots perform better in real-world tasks by requiring only bounding box annotations from a single image during training. It works in three steps: extracting task-relevant regions, augmenting data with realistic backgrounds, and learning a contrastive policy. This approach significantly improves generalization across diverse and unseen environments.