Physics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks

TL;DR

This paper introduces a physics-inspired machine learning method using Kolmogorov-Arnold Networks for static friction modeling in robotic joints, achieving high accuracy and interpretability even with unknown functional structures.

Contribution

It proposes a novel KAN-based approach that combines spline activation and symbolic regression for interpretable friction modeling in robotics.

Findings

Achieves R^2 > 0.95 on synthetic and real data.

Successfully extracts concise, physically meaningful friction expressions.

Demonstrates robustness and generalization in noisy conditions.

Abstract

Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; however, they typically require predefined functional assumptions, which poses significant challenges when dealing with unknown functional structures. To address this issue, this paper proposes a physics-inspired machine learning approach based on the Kolmogorov Arnold Network (KAN) for static friction modeling of robotic joints. The method integrates spline activation functions with a symbolic regression mechanism, enabling model simplification and physical expression extraction through pruning and attribute scoring, while maintaining both high prediction accuracy and interpretability. We first validate the method's capability to accurately identify key parameters under known functional models, and further demonstrate its robustness and generalization ability under conditions with unknown functional structures and noisy data. Experiments conducted on both synthetic data and real friction data collected from a six-degree-of-freedom industrial manipulator show that the proposed method achieves a coefficient of determination greater than 0.95 across various tasks and successfully extracts concise and physically meaningful friction expressions. This study provides a new perspective for interpretable and data-driven robotic friction modeling with promising engineering applicability.