Learning physics property parameters of fabrics and garments with a physics similarity neural network

Duan, L., Boyd, L. and Aragon Camarasa, G. (2022) Learning physics property parameters of fabrics and garments with a physics similarity neural network. IEEE Access, 10, pp. 114725-114734. (doi: 10.1109/ACCESS.2022.3217458)

Abstract

Predicting the physics properties of deformable objects such as garments and fabrics is a challenge in robotic research. Directly measuring their physics properties in a real environment is difficult [1]. Therefore, learning and predicting the physics property parameters of garments and fabrics can be conducted in simulated environments. However, garments have collars, sleeves, pockets and buttons that change the way garments deform and simulating these is time-consuming. Therefore, in this paper, we propose to predict the physics parameters of real fabrics and garments by learning the physics similarities between simulated fabrics via a Physics Similarity Network (PhySNet). For this, we estimate wind speeds generated by an electric fan and area weights to predict the bending stiffness parameters of real fabrics and garments. We found that PhySNet coupled with a Bayesian optimiser can predict physics property parameters and improve state-of-art by 34.0% for fabrics and 68.1% for garments.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Duan, Li and Aragon Camarasa, Dr Gerardo
• Authors: Duan, L., Boyd, L., and Aragon Camarasa, G.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Access
• Publisher: IEEE
• ISSN: 2169-3536
• ISSN (Online): 2169-3536
• Published Online: 26 October 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in IEEE Access 10: 114725-114734
• Publisher Policy: Reproduced under a Creative Commons License