The development of tactile sensing and its fusion with computer vision is expected to enhance robotic systems in handling complex tasks like deformable object manipulation. However, readily available industrial grippers typically lack tactile feedback, which has led researchers to develop and integrate their own tactile sensors. This has resulted in a wide range of sensor hardware, making it difficult to compare performance between different systems. We highlight the value of accessible open-source sensors and present a set of fingertips specifically designed for fine object manipulation, with readily interpretable data outputs. The fingertips are validated through two difficult tasks: cloth edge tracing and cable tracing. Videos of these demonstrations, as well as design files and readout code can be found at https://github.com/RemkoPr/icra-2023-workshop-tactile-fingertips.

翻译：触觉感知的发展及其与计算机视觉的融合，有望提升机器人系统处理复杂任务（如可变形物体操作）的能力。然而，现成的工业夹爪通常缺乏触觉反馈，这促使研究人员自行开发并集成触觉传感器。这导致了传感器硬件的多样化，使得不同系统之间的性能难以比较。我们强调了可获取的开源传感器的价值，并提出了一套专为精细物体操作设计的指尖夹具，其数据输出易于解读。该指尖夹具通过两项困难任务进行了验证：布料边缘追踪和线缆追踪。相关演示视频、设计文件及读取代码可在 https://github.com/RemkoPr/icra-2023-workshop-tactile-fingertips 获取。