Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements

TL;DR

This paper introduces a joint visuo-haptic model that estimates surface friction across objects by leveraging visual data and limited haptic exploration, enhancing robotic grasping performance.

Contribution

It presents a novel method combining visual and haptic data to estimate surface friction over entire objects, reducing exploration time and improving grasping accuracy.

Findings

Accurately estimates surface friction on real multi-material objects.

Improves robotic grasping success rate by guiding grasp planning.

Validates the correlation between visual cues and haptic measurements.

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.