Textile Taxonomy and Classification Using Pulling and Twisting

TL;DR

This paper proposes a structured textile taxonomy and investigates how robotic pulling and twisting actions can classify textiles, aiming to bridge robotics and textile industry for improved manipulation and recognition.

Contribution

It introduces a textile taxonomy based on fiber types and production methods, along with datasets and analysis of robotic actions for textile classification.

Findings

Robotic pulling and twisting can effectively classify textiles.

Visualization techniques enhance interpretability of textile data.

The taxonomy bridges robotics and textile industry applications.

Abstract

Identification of textile properties is an important milestone toward advanced robotic manipulation tasks that consider interaction with clothing items such as assisted dressing, laundry folding, automated sewing, textile recycling and reusing. Despite the abundance of work considering this class of deformable objects, many open problems remain. These relate to the choice and modelling of the sensory feedback as well as the control and planning of the interaction and manipulation strategies. Most importantly, there is no structured approach for studying and assessing different approaches that may bridge the gap between the robotics community and textile production industry. To this end, we outline a textile taxonomy considering fiber types and production methods, commonly used in textile industry. We devise datasets according to the taxonomy, and study how robotic actions, such as pulling and twisting of the textile samples, can be used for the classification. We also provide important insights from the perspective of visualization and interpretability of the gathered data.