Friction from vision: A study of algorithmic and human performance with consequences for robot perception and teleoperation

Abstract

Friction estimation from vision is an important problem for robot locomotion through contact. The problem is challenging due to its dependence on many factors such as material, surface conditions and contact area. In this paper we 1) conduct an analysis of image features that correlate with humans' friction judgements; and 2) compare algorithmic to human performance at the task of predicting the coefficient of friction between different surfaces and a robot's foot. The analysis is based on two new datasets which we make publicly available. One is annotated with human judgements of friction, illumination, material and texture; the other is annotated with static coefficient of friction (COF) of a robot's foot and human judgements of friction. We propose and evaluate visual friction prediction methods based on image features, material class and text mining. And finally, we make conclusions regarding the robustness to COF uncertainty which is necessary by control and planning algorithms; the low performance of humans at the task when compared to simple predictors based on material label; and the promising use of text mining to estimate friction from vision.