Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments
Chung Hee Kim, Abhisesh Silwal, George Kantor
Abstract
Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system that leverages imitation learning for autonomous pepper harvesting designed to operate in these 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 potential feasibility and effectiveness of employing imitation learning for automated harvesting in unstructured agricultural environments. This work aims to advance scalable, automated robotic solutions for agriculture in natural settings.