# RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

arXiv: https://arxiv.org/abs/2602.14032 · HTML: https://mlsanity.com/paper/2602.14032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [CRAFT: Video Diffusion for Bimanual Robot Data Generation](https://mlsanity.com/paper/2604.03552/) (2026)
- [InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies](https://mlsanity.com/paper/2609.27734/) (2026)

## Abstract (arXiv)

Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on large-scale pretraining, which is labor-intensive and time-consuming, or semantic data augmentation methods that assume flawless upstream object detection in real-world scenarios. In this work, we propose RoboAug, a novel generative data augmentation framework that reduces reliance on large-scale pretraining and perfect visual recognition by requiring only a single image with bounding box annotations for dataset construction. Leveraging this minimal supervision, RoboAug employs pretrained generative models for precise semantic augmentation and introduces a plug-and-play region-contrastive loss to guide attention toward task-relevant regions, thereby enhancing generalization and task success rates. Extensive real-world experiments on UR-5e, AgileX, and Tian Gong 2.0 demonstrate that RoboAug consistently outperforms state-of-the-art augmentation baselines under background, distractor, and lighting shifts. Our project is available at https://x-roboaug.github.io/.