Real-Time Tactile Grasp Force Sensing Using Fingernail Imaging via Deep Neural Networks

TL;DR

This paper presents a monocular vision-based method for real-time 3D tactile force estimation from fingernail images using deep neural networks, eliminating the need for physical sensors and enabling scalable, non-intrusive human-robot interaction.

Contribution

It introduces a novel vision-only approach for real-time tactile force sensing that is scalable, non-intrusive, and easily integrated with other perception systems.

Findings

Maximum RMS error of 8.4% across force range

Capable of 30 frames per second force estimation

Comparable accuracy to existing offline models

Abstract

This paper has introduced a novel approach for the real-time estimation of 3D tactile forces exerted by human fingertips via vision only. The introduced approach is entirely monocular vision-based and does not require any physical force sensor. Therefore, it is scalable, non-intrusive, and easily fused with other perception systems such as body pose estimation, making it ideal for HRI applications where force sensing is necessary. The introduced approach consists of three main modules: finger tracking for detection and tracking of each individual finger, image alignment for preserving the spatial information in the images, and the force model for estimating the 3D forces from coloration patterns in the images. The model has been implemented experimentally, and the results have shown a maximum RMS error of 8.4% (for the entire range of force levels) along all three directions. The estimation accuracy is comparable to the offline models in the literature, such as EigneNail, while, this model is capable of performing force estimation at 30 frames per second.