Large-scale Deployment of Vision-based Tactile Sensors on Multi-fingered Grippers

TL;DR

This paper presents a comprehensive system for large-scale deployment of vision-based tactile sensors on multi-fingered robotic grippers, enhancing tactile perception and calibration efficiency across multiple contact surfaces.

Contribution

It introduces a synchronized image acquisition system, a modular VBTS design for multi-surface integration, and a zero-shot calibration method for efficient multi-sensor calibration.

Findings

Improved tactile perception on multi-surface contact

Seamless integration into various gripper morphologies

Reduced calibration data requirements

Abstract

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.