Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system for autonomous pepper harvesting designed to operate in these unprotected, complex settings. Utilizing a custom handheld shear-gripper, we collected 300 demonstrations to train a visuomotor policy, enabling the system to adapt to varying field conditions and crop diversity. We achieved a success rate of 28.95% with a cycle time of 31.71 seconds, comparable to existing systems tested under more controlled conditions like greenhouses. Our system demonstrates the feasibility and effectiveness of leveraging imitation learning for automated harvesting in unstructured agricultural environments. This work aims to advance scalable, automated robotic solutions for agriculture in natural settings.

翻译：在户外农田中实现任务自动化面临重大挑战，这源于环境的多变性、非结构化的地形以及作物特征的多样性。本文提出了一种用于自主辣椒采摘的机器人系统，该系统专为在这些无保护、复杂的环境中运行而设计。通过使用定制的手持式剪切夹持器，我们收集了300次演示数据来训练一个视觉运动策略，使系统能够适应不同的田间条件和作物多样性。我们实现了28.95%的成功率和31.71秒的循环时间，其性能与在温室等更受控环境下测试的现有系统相当。我们的系统证明了利用模仿学习在非结构化农业环境中实现自动化采摘的可行性和有效性。这项工作旨在推动适用于自然环境的、可扩展的自动化机器人农业解决方案的发展。