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This paper introduces a proxy-based framework for estimating friction coefficients between arbitrary material pairs, avoiding exhaustive pairwise testing. The method learns per-material embeddings based on friction measurements against a small set of proxy materials and then uses a fusion function to predict friction between novel pairs. Both deterministic and probabilistic implementations are explored, along with strategies for proxy selection and handling noisy data. The approach demonstrates high accuracy and robustness on both simulated and real-world friction datasets, significantly reducing the need for extensive physical experiments.
Unlock accurate friction estimation for any material pairing with just a handful of proxy material measurements, slashing experimental costs.
Accurately estimating friction coefficients between arbitrary material pairs is critical for robotics, digital fabrication, and physics-based simulation, but exhaustive pairwise testing scales quadratically with the number of materials. We introduce a proxy-based modeling framework that approximates any pairwise friction $f(A,B)$ from a small, fixed set of proxy materials $C=[c_1,\dots,c_k]$ by learning a per-material embedding $z_A = g(f(A,c1),\dots,f(A,ck))$ and a fusion function $p$ such that $f(A,B)\approx p\big(z_A,z_B\big)$. We present deterministic and probabilistic realizations of $g$ and $p$, procedures for selecting diverse proxy sets, and mechanisms for handling missing or noisy proxy measurements. The learned embeddings are compact, interpretable, and enable calibrated uncertainty estimates for downstream decision making. On simulated and measured friction datasets, our approach achieves high predictive accuracy, robust performance with partial observations, and substantial experimental savings by significantly reducing pairwise testing.
