用少量代理材料推算任意材料间摩擦系数，大幅减少实验量。
Generalizable Friction Coefficient Estimation via Material Embedding and Proxy Interaction Modeling
- 通过代理材料构建每种材料的嵌入向量，实现摩擦系数预测。
- 在模拟与实测数据上准确率高，部分缺失数据下仍稳定表现。
- 适合机器人、物理仿真等需快速评估摩擦的应用场景。
精确估算任意材料对间的摩擦系数对机器人、数字制造和物理仿真至关重要，但成对测试随材料数量呈二次增长。本文提出基于代理的建模框架，仅需少量固定代理材料集 $C=[c_1, dots,c_k]$，通过学习每种材料的嵌入 $z_A = g(f(A,c1), dots,f(A,ck))$ 及融合函数 $p$，近似任意材料对摩擦 $f(A,B) o p(z_A,z_B)$。给出 $g$ 与 $p$ 的确定性与概率化实现，提供多样代理集选择方法及缺失/噪声测量处理机制。学习到的嵌入紧凑可解释，支持下游决策的校准不确定性估计。在模拟与实测摩擦数据集上，本方法预测准确、部分观测下鲁棒性强，显著降低成对测试开销。
原文摘要 · Abstract (English)
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.
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