RETRO: REthinking Tactile Representation Learning with Material PriOrs

TL;DR

This paper introduces a novel tactile representation learning approach that incorporates material-aware priors, enabling models to better understand and generalize surface textures for improved tactile feedback in various applications.

Contribution

It proposes integrating pre-learned material priors into tactile learning, addressing the neglect of material properties in existing methods.

Findings

Enhanced tactile perception accuracy across diverse materials

Improved generalization of tactile models to new textures

Better performance in real-world tactile tasks

Abstract

Tactile perception is profoundly influenced by the surface properties of objects in contact. However, despite their crucial role in shaping tactile experiences, these material characteristics have been largely neglected in existing tactile representation learning methods. Most approaches primarily focus on aligning tactile data with visual or textual information, overlooking the richness of tactile feedback that comes from understanding the materials' inherent properties. In this work, we address this gap by revisiting the tactile representation learning framework and incorporating material-aware priors into the learning process. These priors, which represent pre-learned characteristics specific to different materials, allow tactile models to better capture and generalize the nuances of surface texture. Our method enables more accurate, contextually rich tactile feedback across diverse materials and textures, improving performance in real-world applications such as robotics, haptic feedback systems, and material editing.