Norm2Tex：用纹理增强视觉-触觉仿真

中文摘要 AI 辅助

大规模数据集对于训练通用机器人控制策略至关重要。收集真实世界的触觉数据既昂贵又耗时，这促使人们使用触觉仿真。然而，当前的触觉仿真器仅捕获整体接触几何形状，而忽略了纹理等精细细节。这导致仿真与真实触觉数据之间存在显著的域差距。为了解决这一差距，我们提出了Norm2Tex，一种即插即用的方法，通过法线贴图纹理中的高频表面细节来增强基于视觉的触觉传感器的仿真。通过在触觉仿真器的渲染管线之前修改目标物体的深度图，Norm2Tex可以无缝集成到不同的触觉仿真器中。我们还使用材料分类和强化学习任务评估了仿真到现实的迁移。我们的结果表明，Norm2Tex跨域保留了与材料相关的触觉信息，改善了纹理识别，并在现实世界中产生了与材料相关的控制行为。

英文摘要

Large-scale datasets are essential for training generalist robot control policies. Collecting real-world tactile data is costly and time-consuming, motivating the use of tactile simulations. However, current tactile simulators capture only overall contact geometry and miss fine details like texture. This results in a significant domain shift between simulated and real tactile data. To address this gap, we introduce Norm2Tex, a plug-in method that augments simulations of vision-based tactile sensors with high-frequency surface details from normal map textures. By modifying the target object's depth map before a tactile simulator's rendering pipeline, Norm2Tex seamlessly integrates into different tactile simulators. We also evaluate sim-to-real transfer using material classification and a reinforcement learning task. Our results show that Norm2Tex preserves material-dependent tactile information across domains, improving texture recognition and producing material-dependent control behavior in the real world.

发表机构

• University of Technology Nuremberg（纽伦堡工业大学）
• Technical University of Dresden（德累斯顿工业大学）
• Deggendorf Institute of Technology（德根多夫理工学院）

机构由 AI 辅助整理，请以论文原文为准。