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. T o 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 and leverages an End Pose Assisted Action Chunking Transformer (ACT) to develop a fine-grained visuomotor picking policy. Experiments under various occlusion scenarios demonstrate that our modified approach significantly outperforms the direct implementation of ACT, underscoring its potential for practical application in occluded strawberry picking. LOBAL demand for strawberries, a high-value crop, continues to rise. While China led production in 2023 with 3,336,690 tons, followed by the US with 1,055,963 tons [1], harvesting remains labor-intensive due to the fruit's fragility. This contrasts with mechanized harvesting of crops like corn and wheat.