Grasping Force Estimation for Markerless Visuotactile Sensors

TL;DR

This paper evaluates different visuotactile representations for estimating grasping force with vision-based tactile sensors, finding RGB images most effective and demonstrating the generalization of their RGBmod approach on unseen objects.

Contribution

The study compares various visuotactile data types for force estimation and introduces RGBmod, a method that outperforms existing approaches and generalizes well to new objects.

Findings

RGB representation outperforms depth and combined images for force estimation

RGBmod achieves an average relative error of 0.125 on unseen objects

Our approach surpasses existing methods using RGB and depth data

Abstract

Tactile sensors have been used for force estimation in the past, especially Vision-Based Tactile Sensors (VBTS) have recently become a new trend due to their high spatial resolution and low cost. In this work, we have designed and implemented several approaches to estimate the normal grasping force using different types of markerless visuotactile representations obtained from VBTS. Our main goal is to determine the most appropriate visuotactile representation, based on a performance analysis during robotic grasping tasks. Our proposal has been tested on the dataset generated with our DIGIT sensors and another one obtained using GelSight Mini sensors from another state-of-the-art work. We have also tested the generalization capabilities of our best approach, called RGBmod. The results led to two main conclusions. First, the RGB visuotactile representation is a better input option than the depth image or a combination of the two for estimating normal grasping forces. Second, RGBmod achieved a good performance when tested on 10 unseen everyday objects in real-world scenarios, achieving an average relative error of 0.125 +- 0.153. Furthermore, we show that our proposal outperforms other works in the literature that use RGB and depth information for the same task.