让触觉模型学会识别材料特性，提升机器人触感精度
RETRO: REthinking Tactile Representation Learning with Material PriOrs
- 引入材料先验知识，增强触觉表征对材质的感知能力
- 在多种材料上实现更准确的触觉反馈，提升泛化性能
- 适用于机器人操作、触觉交互等需要精细触感的应用
触觉感知深受接触物体表面特性的影响。然而，现有触觉表征学习方法大多忽略材料本身的固有属性，主要关注触觉数据与视觉或文本信息的对齐。本文重新思考触觉表征学习框架，将材料感知先验融入学习过程。这些先验代表不同材料的预学习特征，使触觉模型能更好捕捉和泛化表面纹理的细微差别。该方法在多种材料和纹理上实现了更精确、更具上下文意义的触觉反馈，显著提升了机器人、触觉反馈系统及材料编辑等实际应用中的表现。
原文摘要 · Abstract (English)
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.
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