用模仿学习实现户外辣椒自主采摘，适应复杂自然环境。
Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments
- 通过300次人工示范训练视觉运动策略，适配无序田间环境。
- 成功率达28.95%，单次采摘耗时31.71秒，接近温室系统表现。
- 为自然农田中规模化农业机器人提供可行技术路径。
在户外农业环境中自动化作业面临环境变化大、地形不规则和作物多样性等挑战。本文提出一套面向自主辣椒采摘的机器人系统，可在无保护的复杂环境下运行。采用定制手持式剪切夹持器，收集了300组操作示范数据，用于训练视觉-运动策略，使系统能适应多变田间条件与作物差异。实验结果显示，系统成功率可达28.95%，单次循环耗时31.71秒，性能与在受控温室条件下测试的现有系统相当。本研究验证了模仿学习在非结构化农业环境中的可行性与有效性，旨在推动自然场景下可扩展的农业自动化机器人解决方案的发展。
原文摘要 · Abstract (English)
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.
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