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 .

翻译：光学触觉传感器近年来变得流行。它们能提供高空间分辨率，但在实现精细的时间分辨率方面存在困难。为克服这一缺陷，我们研究了用事件相机替代RGB相机的思路，并引入了一种名为Evetac的新型基于事件的光学触觉传感器。除硬件设计外，我们开发了触觉处理算法，能以1000Hz的频率在线处理其测量数据。我们设计了一种高效算法，尽管传感器输出稀疏，仍能通过印记标记追踪弹性体的形变。基准测试实验表明，与RGB光学触觉传感器相比，Evetac具备高达498Hz的振动传感能力、剪切力重构能力，并能显著降低数据速率。此外，Evetac的输出及标记追踪为学习数据驱动的滑移检测与预测模型提供了有意义的特征。这些学习模型构成了一个鲁棒且自适应的闭环抓取控制器的基础，该控制器能处理多种物体。我们相信，像Evetac这样快速高效的基于事件的触觉传感器，对于将类人操作能力赋予机器人至关重要。该传感器设计已开源，地址为https://sites.google.com/view/evetac。