Learning to Pick: A Visuomotor Policy for Clustered Strawberry Picking

Abstract

Strawberries naturally grow in clusters, interwoven with leaves, stems, and other fruits, which frequently leads to occlusion. This inherent growth habit presents a significant challenge for robotic picking, as traditional percept-plan-control systems struggle to reach fruits amid the clutter. Effectively picking an occluded strawberry demands dexterous manipulation to carefully bypass or gently move the surrounding soft objects and precisely access the ideal picking point—located at the stem just above the calyx. To address this challenge, we introduce a strawberry-picking robotic system that learns from human demonstrations. Our system features a 4-DoF SCARA arm paired with a human teleoperation interface for efficient data collection. We propose the end pose assisted action chunking transformer (EPACT) to learn a fine-grained visuomotor policy. By incorporating end-pose supervision, our approach enables the robot to acquire dexterous manipulation skills such as pushing aside leaves or navigating tight clusters that are difficult to achieve with traditional pipelines. Experiments demonstrate that our method successfully transfers human-like dexterity to the robot, enabling clustered strawberry picking and outperforming imitation learning baselines, underscoring its potential for practical application in complex agricultural environments.

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