Learning to Classify Surface Roughness Using Tactile Force Sensors

Abstract

This article explores the use of Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks to classify force sequences for the purpose of distinguishing surface roughness levels. The force data utilized for this classification is extracted from simulated interactions on the MuJoCo platform. This study presents a methodology that will later be used for haptic feedback. It is involving the classification of force profiles to distinguish three distinct surface textures. This article also demonstrates the potential of employing MLP and LSTM networks to enhance the accuracy of surface roughness identification in haptic interfaces, thereby fostering advancements in human-robot interactions. The outcomes presented in this article showcase the results achieved by our neural networks using MuJoCo data. The overarching goal of this surface roughness detection is to offer an improved haptic system to our robot avatar.