Food waste management is critical for sustainability, yet inorganic contaminants hinder recycling potential. Robotic automation presents a compelling approach to this challenge by accelerating the sorting process through automated contaminant removal. Still, the diverse and unpredictable nature of contaminants creates major challenges for robotic grasping. Benchmarking frameworks are critical for evaluating challenges from various perspectives. However, existing protocols rely on limited simulation datasets, prioritise simple metrics such as success rate, and overlook key object and environment-related pre-grasp conditions. This paper introduces GRAB, a comprehensive Grasping Real-World Article Benchmarking framework that addresses this gap by integrating diverse deformable objects, advanced grasp-pose-estimation vision, and, importantly, pre-grasp conditions, establishing a set of critical graspability metrics. It systematically compares industrial grasping modalities through an in-depth experimental evaluation involving 1,750 food contaminant grasp attempts across four high-fidelity scenes. This large-scale evaluation provides an extensive assessment of grasp performance for food waste sorting, offering a level of depth that has rarely been explored in previous studies. The results reveal distinct gripper strengths and limitations, with object quality emerging as the dominant performance factor in cluttered environments, while vision quality and clutter levels play moderate roles. These findings highlight essential design considerations and reinforce the necessity of developing multimodal gripper technologies capable of robust cross-category performance for effective robotic food waste sorting.

翻译：食物垃圾管理对可持续发展至关重要，然而无机污染物阻碍了其回收潜力。机器人自动化通过自动移除污染物以加速分拣过程，为应对这一挑战提供了一种引人注目的解决方案。尽管如此，污染物的多样性和不可预测性给机器人抓取带来了重大挑战。基准测试框架对于从多角度评估这些挑战至关重要。然而，现有方案依赖于有限的仿真数据集，优先考虑成功率等简单指标，并忽视了关键的对象与环境相关的预抓取条件。本文提出了GRAB，一个全面的真实世界物品抓取基准测试框架，通过整合多样化的可变形物体、先进的抓取位姿估计视觉技术，以及至关重要的预抓取条件，建立了一套关键的抓取性能度量标准，从而弥补了这一空白。该框架通过在四个高保真场景中进行1,750次食物污染物抓取尝试的深入实验评估，系统性地比较了工业抓取模式。此次大规模评估为食物垃圾分类的抓取性能提供了广泛而深入的分析，其深度在以往研究中鲜有涉及。结果揭示了不同夹爪的独特优势与局限，其中在杂乱环境中，物体质量成为主导性能的因素，而视觉质量和杂乱程度则扮演着中等重要的角色。这些发现突显了关键的设计考量，并强化了开发能够实现稳健跨类别性能的多模态夹爪技术对于有效进行机器人食物垃圾分类的必要性。