Robotic strawberry harvesting is challenging under partial occlusion, where leaves induce significant geometric uncertainty and make grasp decisions based on a single deterministic shape estimate unreliable. From a single partial observation, multiple incompatible 3D completions may be plausible, causing grasps that appear feasible on one completion to fail on another. We propose an uncertainty-aware grasping pipeline for partially occluded strawberries that explicitly models completion uncertainty arising from both occlusion and learned shape reconstruction. Our approach uses 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-based metrics. Rather than selecting a grasp based on a single estimate, we aggregate feasibility across completions and apply a conservative lower confidence bound (LCB) criterion to decide whether a grasp should be attempted or safely abstained. We evaluate the proposed method in simulation and on a physical robot across increasing levels of synthetic and real leaf occlusion. Results show 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：在部分遮挡条件下进行机器人草莓采摘具有挑战性，因为叶片会引入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合理三维补全结果，导致在某个补全上看似可行的抓取在另一个补全上失败。我们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到的形状重建所产生的补全不确定性。我们的方法使用带有蒙特卡洛丢弃法的点云补全来采样多个形状假设，为每个补全生成候选抓取，并使用基于物理的力闭合指标评估抓取可行性。我们并非基于单一估计选择抓取，而是聚合跨补全的可行性，并应用保守的下置信界（LCB）准则来决定是尝试抓取还是安全地放弃。我们在仿真和物理机器人上，针对逐渐增强的合成及真实叶片遮挡水平，对所提方法进行了评估。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。