Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency,(ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

翻译：基于视觉的触觉传感器展现出显著的应用前景，因其能够利用图像测量提供高空间分辨率、类人化的触觉感知性能。然而，当前VBTS的设计通常局限于机器人抓取器的指尖，这被证明存在一定不足，因为许多抓取与操作任务需要与物体形成多个接触点。以实现基于VBTS的大规模、多表面触觉感知为最终目标，本研究（i）开发了一种具有极低延迟的同步图像采集系统，（ii）提出了一种模块化的VBTS设计，便于集成至手指指骨，（iii）设计了一种零样本标定方法，以提高多个VBTS同时标定过程中的数据效率。通过在配备7个VBTS的微型三指机器人抓取器上验证该系统，我们展示了通过覆盖抓取器手指与手掌的接触表面，实现了触觉感知性能的提升。此外，我们证明了所提出的VBTS设计能够无缝集成到多种末端执行器形态中，并显著降低了标定所需的数据量。