[
    {
        "id": "osp-26144",
        "type": "article-journal",
        "title": "Norm2Tex: Augmenting Visuo-Tactile Simulations with Texture",
        "author": [
            {
                "family": "Bien",
                "given": "Seongjin"
            },
            {
                "family": "Makowski",
                "given": "Débora Oliveira"
            },
            {
                "family": "Calandra",
                "given": "Roberto"
            },
            {
                "family": "Walter",
                "given": "Florian"
            },
            {
                "family": "Burgard",
                "given": "Wolfram"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/norm2tex-augmenting-visuo-tactile-simulations-with-texture",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]