Effective Data-Driven Joint Friction Modeling and Compensation With Physical Consistency

Abstract

The complex nonlinear nature of friction in real-world applications renders traditional physical models inadequate for accurately capturing its characteristics. While numerous learning-based approaches have addressed this challenge, they often lack interpretability and fail to uphold the physical guarantees essential for reliable modeling. Additionally, existing structured data-driven methods, despite their efficacy in handling nonlinear systems, seldom account for the specific traits of friction or ensure passivity. To overcome these limitations, we introduce a structured Gaussian Process (GP) model that adheres to the physical consistency of joint friction torque, enabling data-driven modeling in function space that accurately captures Coulomb and viscous friction characteristics while further guaranteeing passivity. We experimentally validate our approach by deploying the friction model on a two-degree-of-freedom (2-DoF) leg prototype. Our approach exhibits robust performance in the presence of non-passive and high-noise data. Experimental results demonstrate that our joint friction model achieves enhanced data efficiency, superior friction compensation performance, and improved trajectory tracking dynamics compared to other friction models.