发表机构
Georgia Institute of Technology; Vanderbilt University（佐治亚理工学院; 范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SAFE利用低成本声学传感和电机本体感觉，通过统一分类器实时检测抓取中的滑移与断裂，实现高成功率且无需视觉或材料先验知识的自适应抓取控制。
AI 中文摘要
操控易碎物体仍然具有挑战性，因为机器人必须理解其所抓取物体的状态（如滑移或断裂）才能做出适当响应，尤其是在材料属性未知的情况下。在本文中，我们提出了SAFE：一种低成本、通用型的传感方法，利用两个被动聚偏二氟乙烯（PVDF）声学传感器和电机本体感觉，在不依赖视觉或先验材料知识的情况下实时检测滑移和断裂。传感器安装在柔顺的Fin Ray夹持器上，一个统一的HistGradientBoosting分类器从79维特征向量中报告状态（正常、滑移或断裂）。在留一抓取交叉验证下，SAFE实现了0.884的Alert-F1分数，且滑移-断裂混淆接近零，消融实验证实声学传感不可或缺。基于该检测层的自适应抓取控制器在Jetson Orin Nano上以104 Hz运行，在涵盖多种物体类别的46次闭环机器人试验中实现了91.3%的成功率，而每种固定力策略在与其预设不匹配的物体条件下成功率降至0%。它进一步在训练中未见的新物体上达到82.4%的成功率，展示了无需物体特定校准的鲁棒、故障感知抓取控制。
英文摘要
Manipulating fragile objects remains challenging as robots must understand the state of what they grasp, such as slip or fracture, to respond appropriately, especially when material properties are unknown. In this paper, we present SAFE: a low-cost, general-purpose sensing approach that detects both slip and fracture in real time using two passive polyvinylidene fluoride (PVDF) acoustic sensors and motor proprioception, without relying on vision or prior material knowledge. The sensors are mounted on a compliant Fin Ray gripper, and a unified HistGradientBoosting classifier reports the state (normal, slip, or fracture) from a 79-dimensional feature vector. Under leave-one-grasp-out cross-validation, SAFE achieves an Alert-F1 of 0.884 with near-zero slip-fracture confusion, and ablations confirm that acoustic sensing is indispensable. An adaptive grasp controller built on this detection layer runs at 104 Hz on a Jetson Orin Nano, achieving 91.3% success across 46 closed-loop robot trials spanning diverse object categories, while each fixed-force strategy drops to 0% on object conditions that mismatch its preset. It further reaches 82.4% success on novel objects unseen during training, demonstrating robust, failure-aware grasp control without object-specific calibration.
Comments8 pages, 6 figures, 5 tables
