Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions largely depend on visual information to devise a strategy for grasping. Nonetheless, in order to achieve proficiency akin to humans and achieve consistent grasping and manipulation of unfamiliar objects, the incorporation of artificial tactile sensing has become a necessity in robotic systems. In this work, we propose a novel physics-informed, data-driven method to detect slip continuously in real time. The GelSight Mini, an optical tactile sensor, is mounted on custom grippers to acquire tactile readings. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves an average accuracy of 99%. We demonstrate the application of this work in a dynamic robotic manipulation task in which real-time slip detection and prevention algorithm is implemented.

翻译：物体抓取与操作过程中的滑动检测在物体处理中起着至关重要的作用。现有解决方案主要依赖视觉信息来制定抓取策略。然而，为达到类人灵巧性并实现对陌生物体的一致抓取与操作，在机器人系统中引入人工触觉感知已成为必要。本文提出一种新颖的物理信息驱动数据驱动方法，用于实时连续检测滑动。我们使用光学触觉传感器GelSight Mini，将其安装在定制夹爪上以获取触觉读数。本研究利用滑动事件中触觉传感器读数的不均匀性开发独特特征，并将滑动检测构建为分类问题。为评估该方法，我们在10种常见物体上测试了多种数据驱动模型，涵盖不同载荷条件、纹理和材料。结果表明，最优分类算法平均准确率达99%。我们展示了该方法在动态机器人操作任务中的应用，其中实现了实时滑动检测与预防算法。