面向农业操作的通用视觉模仿学习的任务相关与无关区域感知增强
Task-Relevant and Irrelevant Region-Aware Augmentation for Generalizable Vision-Based Imitation Learning in Agricultural Manipulation
AI总结：
本文提出DRAIL框架，通过区分任务相关与无关区域，提升农业操作中视觉模仿学习的泛化能力。
摘要：
基于视觉的模仿学习在机器人操作中显示出潜力；然而，其在实际农业任务中的泛化能力仍然有限。这种限制源于示范数据不足以及由i）作物特定的外观多样性和ii）背景变化引起的显著视觉领域差距。为了解决这一限制，我们提出了双区域增强模仿学习（DRAIL），一种面向农业操作的通用视觉模仿学习的区域感知增强框架。DRAIL将视觉观察明确分为任务相关和任务无关区域。任务相关区域以领域知识驱动的方式进行增强，以保留关键的视觉特征，而任务无关区域则被激进地随机化，以抑制虚假背景相关性。通过同时处理两种视觉变化源，DRAIL促进了依赖任务本质特征而非偶然视觉线索的学习策略。我们通过在人工蔬菜采摘和真实生菜病叶采摘准备任务上的机器人实验，评估了DRAIL在扩散策略基础上的视觉-运动控制器。结果表明，与基线方法相比，在未见过的视觉条件下，成功率有持续提高。进一步的注意力分析和表示泛化度量表明，学习的策略更依赖于任务本质的视觉特征，从而增强了鲁棒性和泛化能力。
英文摘要：
Vision-based imitation learning has shown promise for robotic manipulation; however, its generalization remains limited in practical agricultural tasks. This limitation stems from scarce demonstration data and substantial visual domain gaps caused by i) crop-specific appearance diversity and ii) background variations. To address this limitation, we propose Dual-Region Augmentation for Imitation Learning (DRAIL), a region-aware augmentation framework designed for generalizable vision-based imitation learning in agricultural manipulation. DRAIL explicitly separates visual observations into task-relevant and task-irrelevant regions. The task-relevant region is augmented in a domain-knowledge-driven manner to preserve essential visual characteristics, while the task-irrelevant region is aggressively randomized to suppress spurious background correlations. By jointly handling both sources of visual variation, DRAIL promotes learning policies that rely on task-essential features rather than incidental visual cues. We evaluate DRAIL on diffusion policy-based visuomotor controllers through robot experiments on artificial vegetable harvesting and real lettuce defective leaf picking preparation tasks. The results show consistent improvements in success rates under unseen visual conditions compared to baseline methods. Further attention analysis and representation generalization metrics indicate that the learned policies rely more on task-essential visual features, resulting in enhanced robustness and generalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
