  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.
