The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: https://sites.google.com/view/incipient-slip-detection.

翻译：摘要：滑移检测能力，特别是微滑移检测，使机器人系统能够采取纠正措施，防止被抓取物体掉落。因此，滑移检测可增强机器人抓取的整体安全性。然而，精确检测微滑移仍是一项重大挑战。本文提出了一种新颖的基于学习的方法，利用PapillArray（澳大利亚Contactile公司）触觉传感器检测微滑移。所获模型在识别与微滑移相关的模式方面极为有效，在离线数据集测试中达到了95.6%的检测成功率。此外，我们引入了多种数据增强方法以提升模型的鲁棒性。当将训练好的模型迁移至不同于训练数据采集环境的机器人抓取场景时，该模型仍保持着鲁棒的性能，成功率达96.8%，为稳定多项实际抓取任务提供了及时反馈。项目网站：https://sites.google.com/view/incipient-slip-detection。