[2510.17640] RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

Computer Science > Robotics arXiv:2510.17640 (cs) [Submitted on 20 Oct 2025 (v1), last revised 10 Apr 2026 (this version, v3)] Title:RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation Authors:Yuquan Xue, Guanxing Lu, Zhenyu Wu, Chuanrui Zhang, Bofang Jia, Zhengyi Gu, Ziwei Wang View a PDF of the paper titled RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation, by Yuquan Xue and 6 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models have demonstrated remarkable performance on complex tasks through imitation learning in recent robotic manipulation works. Based on large-scale and high-quality demonstration datasets, existing imitation learning method arms VLA models acquired with strong capabilities. However, these datasets that predominantly consist of successful trajectories, are costly to collect and often limited in distribution, leading to capability bottlenecks when faced with out-of-distribution (OOD) scenarios during deployment while unable to recover. To address this issue, we propose an automated data augmentation framework named RESample that effectively improves the distribution coverage of VLA training datasets through the well-designed exploratory sampling mechanism. Specifically, the exploratory sampling mechanism identifies the potential coverage gaps during the policy rollout and actively samples exploratory actions to extend the cover...