提出新评估方法，量化软夹爪抓取时物体形变
SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation
- 用点云密度感知距离衡量抓取前后物体形变
- 在EGAD数据集上成功区分三种软夹爪性能优劣
- 适合评估软体机器人抓取设计，推动标准评测
近年来，软体机器人夹爪因能稳健抓取柔软易碎物体而受到关注。然而，目前尚缺乏标准化的评估协议来衡量不同软体夹爪设计的性能。本文提出一种新型评估方法——软抓取基准评测（SoGraB），通过计算抓取前后软体物体点云间的密度感知切比雪夫距离（DCD）来量化物体形变程度。我们在大量实验中验证了该方法，使用EGAD数据集的子集对三种Fin-Ray夹爪设计进行排名。结果表明，该协议能根据形变信息准确排序夹爪性能，验证了其在复杂抓取任务中优选软体夹爪及未来设计对比的能力。
原文摘要 · Abstract (English)
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.
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