Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. 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 a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.

翻译：在物体抓取和操作过程中检测滑动对于物体处理至关重要。现有解决方案主要依赖视觉信息来制定抓取策略。然而，为了让机器人系统达到与人类相当的操作熟练度，尤其是在一致地处理和不熟悉物体操作方面，集成人工触觉感知日益重要。我们提出了一种新颖的、基于物理信息的数据驱动方法，用于实时连续检测滑动。我们采用GelSight Mini光学触觉传感器，安装在定制设计的夹爪上，以收集触觉数据。我们的工作利用滑动事件期间触觉传感器读数的非均匀性来开发独特特征，并将滑动检测视为一个分类问题。为了评估我们的方法，我们在10种常见物体上测试了多种数据驱动模型，这些物体具有不同的载荷条件、纹理和材料。结果表明，最佳分类算法实现了95.61%的高平均准确率。我们进一步展示了我们的研究在动态机器人操作任务中的实际应用，其中实现了实时滑动检测与预防算法。