Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a novel protocol, the Soft Grasping Benchmarking and Evaluation (SoGraB) method, to evaluate grasping quality, which quantifies object deformation by using the Density-Aware Chamfer Distance (DCD) between point clouds of soft objects before and after grasping. We validated our protocol in extensive experiments, which involved ranking three Fin-Ray gripper designs with a subset of the EGAD object dataset. The protocol appropriately ranked grippers based on object deformation information, validating the method's ability to select soft grippers for complex grasping tasks and benchmark them for comparison against future designs.

翻译：近年来，软体机器人夹爪因其能够稳健抓取柔软易碎物体而受到越来越多的关注。然而，目前尚未建立普遍可用的标准化评估协议来评估不同软体机器人夹爪设计的性能。本研究提出了一种新颖的协议——软体抓取基准测试与评估（SoGraB）方法，用于评估抓取质量。该方法通过计算抓取前后软体对象点云之间的密度感知倒角距离（DCD）来量化物体形变。我们在大量实验中验证了该协议，实验涉及使用EGAD物体数据集的子集对三种Fin-Ray夹爪设计进行排序。该协议能够基于物体形变信息对夹爪进行合理排序，验证了该方法为复杂抓取任务选择软体夹爪以及建立基准以与未来设计进行比较的能力。