  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.
