教机器人从人示范中学习，在密集草莓簇中精准采摘。
Learning to Pick: A Visuomotor Policy for Clustered Strawberry Picking
- 用人类操作数据训练，通过动作分块变换模型实现精细控制。
- 在多种遮挡场景下成功率显著高于直接使用ACT模型。
- 适合需要处理柔软遮挡物的复杂采摘任务，如农业机器人。
草莓自然成簇生长，常被叶片、茎秆和其他果实遮挡，导致严重遮挡问题。传统感知-规划-控制系统难以在杂乱环境中触及目标果实。精准采摘被遮挡草莓需灵巧操作，以避开或轻柔移动周围软性障碍物，并精确抵达花萼上方的茎部最佳采摘点。为此，我们提出一种基于人类示范学习的草莓采摘机器人系统。系统采用4自由度SCARA机械臂与人机遥控界面，高效收集数据，并利用末端位姿辅助的动作分块变换模型（End Pose Assisted Action Chunking Transformer, ACT）构建细粒度视觉运动策略。在多种遮挡场景下的实验表明，改进后的方法显著优于直接使用ACT的实现，凸显其在实际遮挡草莓采摘中的应用潜力。
原文摘要 · Abstract (English)
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 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.
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