Abstract
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.
Highlights
N OWADAYS, robots are used extensively to perform various tasks from simple pick-and-place to sophisticated object manipulation in complex environments from factory floors to hospitals
Afterwards, we present a grasping case study in order to demonstrate the benefits of accurate friction estimation in practical robotics applications
We presented an approach that enables the estimation of local object physical properties, like the surface friction coefficient, from visual and haptic cues, which goes beyond the state-of-the-art by lifting the assumption that the target object has uniform friction across its surface
Summary
N OWADAYS, robots are used extensively to perform various tasks from simple pick-and-place to sophisticated object manipulation in complex environments from factory floors to hospitals. For such tasks, robots are required to interact with, and adapt to, unknown environments and objects. In order to successfully accomplish these tasks, robots need to identify various properties of the objects to be handled. For these reasons, identifying object models that can represent the properties of objects has become a crucial issue in robotics. Surface properties such as surface friction, texture, and roughness are vital for manipulation planning
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