Optical tactile sensors have recently become popular. They provide high spatial resolution, but struggle to offer fine temporal resolutions. To overcome this shortcoming, we study the idea of replacing the RGB camera with an event-based camera and introduce a new event-based optical tactile sensor called Evetac. Along with hardware design, we develop touch processing algorithms to process its measurements online at 1000 Hz. We devise an efficient algorithm to track the elastomer's deformation through the imprinted markers despite the sensor's sparse output. Benchmarking experiments demonstrate Evetac's capabilities of sensing vibrations up to 498 Hz, reconstructing shear forces, and significantly reducing data rates compared to RGB optical tactile sensors. Moreover, Evetac's output and the marker tracking provide meaningful features for learning data-driven slip detection and prediction models. The learned models form the basis for a robust and adaptive closed-loop grasp controller capable of handling a wide range of objects. We believe that fast and efficient event-based tactile sensors like Evetac will be essential for bringing human-like manipulation capabilities to robotics. The sensor design is open-sourced at https://sites.google.com/view/evetac .

通过使用事件相机替代RGB相机，我们提出了一种新型的事件光学触觉传感器Evetac，该传感器具有较高的时空分辨率。通过硬件设计和触觉处理算法，我们实现了对Evetac测量数据的在线1000 Hz处理。我们开发了一种有效的算法，通过印记标记跟踪弹性体的变形，克服了传感器稀疏输出的限制。基准实验表明，Evetac能够感知高达498 Hz的振动、重构剪切力，并且与RGB光学触觉传感器相比，数据传输速率显著降低。此外，Evetac的输出和标记跟踪为学习数据驱动的滑移检测和预测模型提供了有意义的特征。所学模型为基于数据的稳健自适应闭环抓握控制器提供了基础，能够处理各种物体。我们相信，像Evetac这样快速高效的事件光学触觉传感器对于实现类人操作能力的机器人至关重要。传感器设计在此链接中开源。

Evetac: 用于机器人操纵的基于事件的光学触觉传感器