Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this work, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas.

翻译：精确地模拟物体的当地表面特性对于从捕捉到物质识别等许多机器人应用至关重要。表面特性,例如摩擦,虽然难以估计,但表面特性,因为对物体的视觉观察不能传达足够的关于这些特性的信息。相反,偶然勘探耗时,因为它只提供与物体勘探部分有关的信息。在这项工作中,我们提议了一个共同的相对偏差物体模型,通过利用视觉和机能信息的相关性,以及机器人臂的有限偶然探索,来估计整个物体的表面摩擦系数。我们通过显示其能够估计一系列实际多物质物体上不同的摩擦系数,来证明拟议方法的有效性。此外,我们说明估计的摩擦系数如何通过引导一个牵引机规划员到高摩擦区来提高成功率。