Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation

TL;DR

Telextiles introduces a novel system that remotely transmits textile tactile sensations by creating a learned latent space, enabling recognition and reproduction of tactile features for online textile evaluation.

Contribution

The paper presents a new end-to-end interface that uses contrastive self-supervised learning to encode and transmit tactile sensations of textiles remotely.

Findings

Textiles with similar tactile features are close in the latent space.

The system can recognize unknown textiles based on proximity in the latent space.

Remote tactile transmission is feasible with the proposed method.

Abstract

The tactile sensation of textiles is critical in determining the comfort of clothing. For remote use, such as online shopping, users cannot physically touch the textile of clothes, making it difficult to evaluate its tactile sensation. Tactile sensing and actuation devices are required to transmit the tactile sensation of textiles. The sensing device needs to recognize different garments, even with hand-held sensors. In addition, the existing actuation device can only present a limited number of known patterns and cannot transmit unknown tactile sensations of textiles. To address these issues, we propose Telextiles, an interface that can remotely transmit tactile sensations of textiles by creating a latent space that reflects the proximity of textiles through contrastive self-supervised learning. We confirm that textiles with similar tactile features are located close to each other in the latent space through a two-dimensional plot. We then compress the latent features for known textile samples into the 1D distance and apply the 16 textile samples to the rollers in the order of the distance. The roller is rotated to select the textile with the closest feature if an unknown textile is detected.