Learning Decoupled Multi-touch Force Estimation, Localization and Stretch for Soft Capacitive E-skin

TL;DR

This paper presents a machine learning approach to decouple and predict stretch, force, and localization stimuli in a soft capacitive e-skin, enabling multi-modal sensing for soft robots.

Contribution

It introduces a method to decouple coupled deformation stimuli using machine learning, improving multi-modal sensing in soft robotic e-skins.

Findings

Machine learning models effectively predict stretch, force, and location.

Decoupling measurements improves multi-stimuli detection accuracy.

Simultaneous force application affects localization and force detection.

Abstract

Distributed sensor arrays capable of detecting multiple spatially distributed stimuli are considered an important element in the realisation of exteroceptive and proprioceptive soft robots. This paper expands upon the previously presented idea of decoupling the measurements of pressure and location of a local indentation from global deformation, using the overall stretch experienced by a soft capacitive e-skin. We employed machine learning methods to decouple and predict these highly coupled deformation stimuli, collecting data from a soft sensor e-skin which was then fed to a machine learning system comprising of linear regressor, gaussian process regressor, SVM and random forest classifier for stretch, force, detection and localisation respectively. We also studied how the localisation and forces are affected when two forces are applied simultaneously. Soft sensor arrays aided by appropriately chosen machine learning techniques can pave the way to e-skins capable of deciphering multi-modal stimuli in soft robots.