Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called ``events''), offer an elegant feature when mounted on a robot's end effector: since they only detect motion relative to their viewpoint, a properly grasped object produces no events, while a slipping object immediately triggers them. To research this feature, representative datasets are essential, both for analytic approaches and for training machine learning models. The majority of current research on slip detection with event-based data is done on real-world scenarios and manual data collection, as well as additional setups for data labeling. This can result in a significant increase in the time required for data collection, a lack of flexibility in scene setups, and a high level of complexity in the repetition of experiments. This paper presents a simulation pipeline for generating slip data using the described camera-gripper configuration in a robot arm, and demonstrates its effectiveness through initial data-driven experiments. The use of a simulator, once it is set up, has the potential to reduce the time spent on data collection, provide the ability to alter the setup at any time, simplify the process of repetition and the generation of arbitrarily large data sets. Two distinct datasets were created and validated through visual inspection and artificial neural networks (ANNs). Visual inspection confirmed photorealistic frame generation and accurate slip modeling, while three ANNs trained on this data achieved high validation accuracy and demonstrated good generalization capabilities on a separate test set, along with initial applicability to real-world data. Project page: https://github.com/tub-rip/event_slip

翻译：机器人操作是工业制造等领域的常见任务。检测物体何时从机器人抓握中滑移对于安全可靠运行至关重要。事件相机以高时间分辨率记录像素级亮度变化（称为“事件”），当安装在机器人末端执行器上时具有独特优势：由于仅检测相对于其视点的运动，被稳定抓取的物体不会产生事件，而滑动物体会立即触发事件。为研究此特性，无论是分析方法还是机器学习模型训练，具有代表性的数据集都至关重要。当前基于事件数据的滑移检测研究大多依赖真实场景和手动数据采集，以及额外的数据标注设置，这可能导致数据收集时间显著增加、场景设置缺乏灵活性、实验重复复杂度高。本文提出了一种仿真流程，用于在机器人手臂中通过所述相机-夹爪配置生成滑移数据，并通过初步数据驱动实验验证其有效性。仿真器一旦建立，有望减少数据收集时间，支持随时修改实验设置，简化重复过程并生成任意规模的数据集。研究创建了两个独立数据集，并通过视觉检查和人工神经网络（ANN）进行验证。视觉检查确认了照片级真实感的帧生成与精确的滑移建模；基于此数据训练的三个ANN在验证集上取得高准确率，在独立测试集上表现出良好泛化能力，并初步展现出对真实数据的适用性。项目页面：https://github.com/tub-rip/event_slip