Food waste management is critical for sustainability, yet inorganic contaminants hinder recycling potential. Robotic automation accelerates sorting through automated contaminant removal. Nevertheless, the diverse and unpredictable nature of contaminants introduces major challenges for reliable robotic grasping. Grasp performance benchmarking provides a rigorous methodology for evaluating these challenges in underexplored field contexts like food waste sorting. However, existing approaches suffer from limited simulation datasets, over-reliance on simplistic metrics like success rate, inability to account for object-related pre-grasp conditions, and lack of comprehensive failure analysis. To address these gaps, this work introduces GRAB, a real-world grasping-in-clutter (GIC) performance benchmark incorporating: (1) diverse deformable object datasets, (2) advanced 6D grasp pose estimation, and (3) explicit evaluation of pre-grasp conditions through graspability metrics. The benchmark compares industrial grasping across three gripper modalities through 1,750 grasp attempts across four randomized clutter levels. Results reveal a clear hierarchy among graspability parameters, with object quality emerging as the dominant factor governing grasp performance across modalities. Failure mode analysis shows that physical interaction constraints, rather than perception or control limitations, constitute the primary source of grasp failures in cluttered environments. By enabling identification of dominant factors influencing grasp performance, GRAB provides a principled foundation for designing robust, adaptive grasping systems for complex, cluttered food waste sorting.

翻译：厨余垃圾管理对可持续性至关重要，但无机污染物阻碍了其回收潜力。机器人自动化通过自动去除污染物加速了分拣过程。然而，污染物的多样性和不可预测性为可靠抓取带来了重大挑战。抓取性能基准测试为在厨余垃圾分拣等尚未充分探索的现场环境中评估这些挑战提供了严格的方法论。然而，现有方法存在以下问题：模拟数据集有限、过度依赖成功率等简单指标、无法考虑与对象相关的抓取前条件，以及缺乏全面的失效分析。针对这些不足，本工作引入了GRAB——一个真实世界的杂乱环境抓取（GIC）性能基准，其包含：（1）多样化可变形物体数据集；（2）高级六自由度（6D）抓取姿态估计；（3）通过可抓取性指标显式评估抓取前条件。该基准通过1750次抓取尝试，比较了三种夹爪模态在不同随机杂乱程度（四级）下的工业抓取性能。结果揭示了可抓取性参数间清晰的层次关系，其中物体质量是影响各模态抓取性能的主导因素。失效模式分析表明，物理交互约束（而非感知或控制限制）是杂乱环境中抓取失败的主要根源。通过识别影响抓取性能的主导因素，GRAB为设计面向复杂杂乱厨余垃圾分拣场景的鲁棒自适应抓取系统提供了原则性基础。