Agricultural robotics has emerged as a critical solution to the labor shortages and rising costs associated with manual crop harvesting. Bell pepper harvesting, in particular, is a labor-intensive task, accounting for up to 50% of total production costs. While automated solutions have shown promise in controlled greenhouse environments, harvesting in unstructured outdoor farms remains an open challenge due to environmental variability and occlusion. This paper presents VADER (Vision-guided Autonomous Dual-arm Extraction Robot), a dual-arm mobile manipulation system designed specifically for the autonomous harvesting of bell peppers in outdoor environments. The system integrates a robust perception pipeline coupled with a dual-arm planning framework that coordinates a gripping arm and a cutting arm for extraction. We validate the system through trials in various realistic conditions, demonstrating a harvest success rate exceeding 60% with a cycle time of under 100 seconds per fruit, while also featuring a teleoperation fail-safe based on the GELLO teleoperation framework to ensure robustness. To support robust perception, we contribute a hierarchically structured dataset of over 3,200 images spanning indoor and outdoor domains, pairing wide-field scene images with close-up pepper images to enable a coarse-to-fine training strategy from fruit detection to high-precision pose estimation. The code and dataset will be made publicly available upon acceptance.

翻译：农业机器人已成为应对人工作物收获相关劳动力短缺和成本上升的关键解决方案。甜椒收获尤其是一项劳动密集型任务，占总生产成本的比例高达50%。尽管自动化解决方案在受控温室环境中已显示出潜力，但由于环境多变性和遮挡问题，在非结构化户外农场中的收获仍是一个开放挑战。本文提出VADER（视觉引导自主双臂采摘机器人），这是一种专为户外环境自主收获甜椒设计的双臂移动操作系统。该系统集成了鲁棒的感知流程与双臂规划框架，协调抓取臂和切割臂进行采摘。我们通过在多种现实条件下的试验验证该系统，证明其收获成功率超过60%，单果循环时间低于100秒，同时基于GELLO遥操作框架设计了遥操作故障安全机制以确保鲁棒性。为支持鲁棒感知，我们贡献了一个包含3200多张图像的分层结构化数据集，涵盖室内和室外场景，将广域场景图像与甜椒特写图像配对，实现了从果实检测到高精度姿态估计的由粗到精训练策略。代码和数据集将在论文录用后公开提供。