Norm2Tex: Augmenting Visuo-Tactile Simulations with Texture

Abstract: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.

Robotics (cs.RO)

arXiv:2609.25398 [cs.RO]

(or arXiv:2609.25398v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.25398 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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