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Learning to Measure the Static Friction Coefficient in Cloth Contact


Measuring friction coefficients between cloth and an external body is a longstanding issue in mechanical engineering, never yet addressed with a pure vision-based system. The latter offers the prospect of simpler, less invasive friction measurement protocols compared to traditional ones,and can vastly benefit from recent deep learning advances. Such a novel measurement strategy however proves challenging, as no large labelled dataset for cloth contact exists, and creating one would require thousands of physics work-bench measurements with broad coverage of cloth-material pairs. Using synthetic data instead is only possible assuming the availability of a soft-body mechanical simulator with true-to-life friction physics accuracy, yet to be verified. We propose a first vision-based measurement network forfriction between cloth and a substrate, using a simple and repeatable video acquisition protocol. We train our network on purely synthetic data generated by a state-of-the-art frictional contact simulator, which we carefully calibrate and validate against real experiments under controlled conditions. We show promising results on a large set of contact pairs between real cloth samples and various kinds of substrates, with 93.6% of all measurements predicted within 0.1 range of standard physics bench measurements.
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Dates and versions

hal-02511646 , version 1 (23-03-2020)



Abdullah-Haroon Rasheed, Victor Romero, Florence Bertails-Descoubes, Stefanie Wuhrer, Jean-Sébastien Franco, et al.. Learning to Measure the Static Friction Coefficient in Cloth Contact. CVPR 2020 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2020, Seattle, United States. pp.9909-9918, ⟨10.1109/CVPR42600.2020.00993⟩. ⟨hal-02511646⟩
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