Data-driven Super-resolution on a Tactile Dome

TL;DR

This paper introduces a data-driven method for high-accuracy tactile contact localization on curved 3D surfaces, using embedded pressure sensors and finite element modeling to improve robotic tactile sensing.

Contribution

It presents a novel tactile sensing approach combining soft material sensors, data-driven mapping, and finite element modeling for improved 3D surface contact localization.

Findings

Achieved 1.1mm localization accuracy

Validated on a 1300mm^2 curved tactile dome

Demonstrated potential for robotic finger applications

Abstract

While tactile sensor technology has made great strides over the past decades, applications in robotic manipulation are limited by aspects such as blind spots, difficult integration into hands, and low spatial resolution. We present a method for localizing contact with high accuracy over curved, three dimensional surfaces, with a low wire count and reduced integration complexity. To achieve this, we build a volume of soft material embedded with individual off-the-shelf pressure sensors. Using data driven techniques, we map the raw signals from these pressure sensors to known surface locations and indentation depths. Additionally, we show that a finite element model can be used to improve the placement of the pressure sensors inside the volume and to explore the design space in simulation. We validate our approach on physically implemented tactile domes which achieve high contact localization accuracy ($1.1mm$ in the best case) over a large, curved sensing area ($1,300mm^2$ hemisphere). We believe this approach can be used to deploy tactile sensing capabilities over three dimensional surfaces such as a robotic finger or palm.