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

• URL: http://arxiv.org/abs/2602.14032v1
• Date: Sun, 15 Feb 2026 07:40:00 GMT
• Title: RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation
• Abstract summary: RoboAug is a novel generative data augmentation framework that minimizes the reliance on large-scale pretraining.<n>We conduct extensive real-world experiments on three robots, namely UR-5e, AgileX, and Tien Kung 2.0, spanning over 35k rollouts.<n> Empirical results demonstrate that RoboAug significantly outperforms state-of-the-art data augmentation baselines.
• Score: 34.2367474351408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enhancing the generalization capability of robotic learning to enable robots to operate effectively in diverse, unseen scenes is a fundamental and challenging problem. Existing approaches often depend on pretraining with large-scale data collection, which is labor-intensive and time-consuming, or on semantic data augmentation techniques that necessitate an impractical assumption of flawless upstream object detection in real-world scenarios. In this work, we propose RoboAug, a novel generative data augmentation framework that significantly minimizes the reliance on large-scale pretraining and the perfect visual recognition assumption by requiring only the bounding box annotation of a single image during training. Leveraging this minimal information, RoboAug employs pre-trained generative models for precise semantic data augmentation and integrates a plug-and-play region-contrastive loss to help models focus on task-relevant regions, thereby improving generalization and boosting task success rates. We conduct extensive real-world experiments on three robots, namely UR-5e, AgileX, and Tien Kung 2.0, spanning over 35k rollouts. Empirical results demonstrate that RoboAug significantly outperforms state-of-the-art data augmentation baselines. Specifically, when evaluating generalization capabilities in unseen scenes featuring diverse combinations of backgrounds, distractors, and lighting conditions, our method achieves substantial gains over the baseline without augmentation. The success rates increase from 0.09 to 0.47 on UR-5e, from 0.16 to 0.60 on AgileX, and from 0.19 to 0.67 on Tien Kung 2.0. These results highlight the superior generalization and effectiveness of RoboAug in real-world manipulation tasks. Our project is available at https://x-roboaug.github.io/.

Related papers

• Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data [60.94418315274988]
  Ego2Robot supports both curated and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies.<n>Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple types.
  arXiv  Detail & Related papers  (2026-08-03T17:52:26Z)
• RoboReward: General-Purpose Vision-Language Reward Models for Robotics [124.34685604054312]
  Vision-language models (VLMs) have shown promise as automatic reward models, yet their effectiveness on real robot tasks is poorly understood.<n>We aim to close this gap by introducing RoboReward, a robotics reward dataset and benchmark built on large-scale real-robot corpora.
  arXiv  Detail & Related papers  (2026-01-02T12:47:34Z)
• Invariance Co-training for Robot Visual Generalization [72.88252238231269]
  Reasoning from diverse observations is a fundamental capability for generalist robot policies to operate in a wide range of environments.<n>We show that co-training on diverse data improves performance by 18 percent over existing generative augmentation methods.
  arXiv  Detail & Related papers  (2025-12-04T20:08:46Z)
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems [88.05152114775498]
  AgiBot World is a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios.<n>AgiBot World guarantees high-quality and diverse data distribution.<n>GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks.
  arXiv  Detail & Related papers  (2025-03-09T15:40:29Z)
• Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets [24.77850617214567]
  We propose a foundation representation learning framework capturing both visual features and the dynamics information such as actions and proprioceptions of manipulation tasks. Specifically, we pre-train a visual encoder on the DROID robotic dataset and leverage motion-relevant data such as robot proprioceptive states and actions. We introduce a novel contrastive loss that aligns visual observations with the robot's proprioceptive state-action dynamics, combined with a behavior cloning (BC)-like actor loss to predict actions during pre-training, along with a time contrastive loss.
  arXiv  Detail & Related papers  (2024-10-29T17:58:13Z)
• An Unbiased Look at Datasets for Visuo-Motor Pre-Training [20.094244564603184]
  We show that dataset choice is just as important to this paradigm's success. We observe that traditional vision datasets are surprisingly competitive options for visuo-motor representation learning. We show that common simulation benchmarks are not a reliable proxy for real world performance.
  arXiv  Detail & Related papers  (2023-10-13T17:59:02Z)
• Scaling Data Generation in Vision-and-Language Navigation [116.95534559103788]
  We propose an effective paradigm for generating large-scale data for learning. We apply 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning.
  arXiv  Detail & Related papers  (2023-07-28T16:03:28Z)
• Scaling Robot Learning with Semantically Imagined Experience [21.361979238427722]
  Recent advances in robot learning have shown promise in enabling robots to perform manipulation tasks. One of the key contributing factors to this progress is the scale of robot data used to train the models. We propose an alternative route and leverage text-to-image foundation models widely used in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-02-22T18:47:51Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.