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.

远程传输纺织品的触觉感的 Telextiles 接口通过对比自监督学习创建一个反映纺织品近距离的潜在空间，从而解决了远程使用中无法评估纺织品触觉感的难题。

Telextiles: 织物触感的端到端远程传输

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

In this paper, we explore the impact of adding tactile sensation to video
prediction models for physical robot interactions. Predicting the impact of
robotic actions on the environment is a fundamental challenge in robotics.
Current methods leverage visual and robot action data to generate video
predictions over a given time period, which can then be used to adjust robot
actions. However, humans rely on both visual and tactile feedback to develop
and maintain a mental model of their physical surroundings. In this paper, we
investigate the impact of integrating tactile feedback into video prediction
models for physical robot interactions. We propose three multi-modal
integration approaches and compare the performance of these tactile-enhanced
video prediction models. Additionally, we introduce two new datasets of robot
pushing that use a magnetic-based tactile sensor for unsupervised learning. The
first dataset contains visually identical objects with different physical
properties, while the second dataset mimics existing robot-pushing datasets of
household object clusters. Our results demonstrate that incorporating tactile
feedback into video prediction models improves scene prediction accuracy and
enhances the agent's perception of physical interactions and understanding of
cause-effect relationships during physical robot interactions.

本论文探究将触觉感应应用于物理机器人相互作用的视频预测模型，结果表明增加触觉反馈有助于提高场景预测准确性和增强机器人在物理相互作用过程中的感知和理解能力。