Robotic strawberry harvesting remains challenging under partial occlusion, where leaf interference introduces significant geometric uncertainty and renders grasp decisions based on a single deterministic shape estimate unreliable. From a single partial observation, multiple incompatible 3D shape completions may be plausible, such that grasps deemed feasible on one completion can fail on another. This paper presents an uncertainty-aware grasping pipeline for partially occluded strawberries that explicitly models geometric uncertainty arising from both occlusion and learned shape completion. The proposed approach employs point cloud completion with Monte Carlo dropout to sample multiple shape hypotheses, generates candidate grasps for each completion, and evaluates grasp feasibility using physically grounded force-closure metrics. Rather than selecting a grasp from a single shape estimate, feasibility is aggregated across completions and a conservative lower confidence bound (LCB) criterion is used to decide whether grasping a strawberry should be attempted or safely abstained. The method is evaluated in simulation and on a physical robot under increasing levels of synthetic and real leaf occlusion. Experimental results demonstrate that uncertainty-aware decision making enables reliable abstention from high-risk grasp attempts under severe occlusion while maintaining robust grasp execution when geometric confidence is sufficient, outperforming deterministic baselines in both simulated and physical robot experiments.

翻译：在部分遮挡条件下，机器人草莓采摘仍然面临挑战，其中叶片干扰引入了显著的几何不确定性，使得基于单一确定性形状估计的抓取决策不可靠。从单一局部观测出发，可能存在多个互不相容的3D形状补全结果，导致在某一补全结果上可行的抓取在另一结果上可能失败。本文提出了一种针对部分遮挡草莓的不确定性感知抓取流程，该流程显式地建模了由遮挡和学习的形状补全所产生的几何不确定性。所提方法采用带蒙特卡洛丢弃的点云补全技术来采样多个形状假设，为每个补全结果生成候选抓取，并使用基于物理的力闭合指标评估抓取可行性。该方法并非从单一形状估计中选择抓取，而是跨补全结果聚合可行性，并采用保守的下置信界（LCB）准则来决定是尝试抓取草莓还是安全地放弃。该方法在仿真和物理机器人上，针对递增水平的合成及真实叶片遮挡进行了评估。实验结果表明，不确定性感知决策能够在严重遮挡下可靠地放弃高风险抓取尝试，同时在几何置信度足够时保持稳健的抓取执行，在仿真和物理机器人实验中均优于确定性基线方法。