RETRO: REthinking Tactile Representation Learning with Material PriOrs
This work addresses a gap in tactile perception for robotics and haptic systems, though it appears incremental by building on existing frameworks with added priors.
The paper tackles the neglect of material characteristics in tactile representation learning by incorporating material-aware priors, resulting in improved accuracy and contextual richness for tactile feedback across diverse materials and textures.
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.