Principal Components of Touch

TL;DR

This paper demonstrates that principal component analysis (PCA) effectively visualizes and reveals structure in tactile sensor data, enabling better interpretation, classification, and sensitivity assessment for robotic touch applications.

Contribution

The paper introduces a simple PCA-based visualization method for tactile data, applicable across different sensors, facilitating improved data interpretation and potential control strategies in robotics.

Findings

PCA reveals structure and regularities in tactile data.

Simple classifiers like k-NN perform well using PCA features.

Euclidean distance in PCA space measures tactile sensitivity.

Abstract

Our human sense of touch enables us to manipulate our surroundings; therefore, complex robotic manipulation will require artificial tactile sensing. Typically tactile sensor arrays are used in robotics, implying that a straightforward way of interpreting multidimensional data is required. In this paper we present a simple visualisation approach based on applying principal component analysis (PCA) to systematically collected sets of tactile data. We apply the visualisation approach to 4 different types of tactile sensor, encompassing fingertips and vibrissal arrays. The results show that PCA can reveal structure and regularities in the tactile data, which also permits the use of simple classifiers such as $k$-NN to achieve good inference. Additionally, the Euclidean distance in principal component space gives a measure of sensitivity, which can aid visualisation and also be used to find regions in the tactile input space where the sensor is able to perceive with higher accuracy. We expect that these observations will generalise, and thus offer the potential for novel control methods based on touch.