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日益普及。它们能提供高空间分辨率，但在实现精细时间分辨率方面存在困难。为克服这一不足，我们研究了用事件相机替代RGB相机的方案，并推出了一款新型事件型光学触觉传感器Evetac。除硬件设计外，我们还开发了触觉处理算法，能够以1000 Hz的频率在线处理其测量数据。针对传感器输出的稀疏特性，我们设计了一种高效算法，通过追踪弹性体表面印记标记点的形变来实现形变监测。基准测试实验表明，Evetac能够感知高达498 Hz的振动信号，重建剪切力，且与RGB光学触觉传感器相比显著降低了数据速率。此外，Evetac的输出数据与标记点追踪结果为学习数据驱动的滑移检测与预测模型提供了有效特征。基于习得的模型，我们构建了能够适应多种物体的鲁棒自适应闭环抓取控制器。我们相信，像Evetac这样快速高效的事件型触觉传感器，对于赋予机器人类人化的操作能力至关重要。该传感器设计已开源，详见 https://sites.google.com/view/evetac 。