# Learning Transferable Friction Models and LuGre Identification Via Physics-Informed Neural Networks

arXiv: https://arxiv.org/abs/2504.12441 · HTML: https://mlsanity.com/paper/2504.12441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networks](https://mlsanity.com/paper/1909.02487/) (2019)
- [iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes](https://mlsanity.com/paper/2012.02924/) (2020)
- [Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks](https://mlsanity.com/paper/2005.03596/) (2020)
- [Contact Models in Robotics: a Comparative Analysis](https://mlsanity.com/paper/2304.06372/) (2023)
- [Learning-based adaption of robotic friction models](https://mlsanity.com/paper/2310.16688/) (2023)
- [Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives](https://mlsanity.com/paper/2410.12685/) (2024)

## Abstract (arXiv)

Accurately modeling friction in robotics remains a core challenge, as robotics simulators like MuJoCo and PyBullet use simplified friction models or heuristics to balance computational efficiency with accuracy, where these simplifications and approximations can lead to substantial differences between simulated and physical performance. In this paper, we present a physics-informed friction estimation framework that enables the integration of well-established friction models with learnable components, requiring only minimal, generic measurement data. Our approach enforces physical consistency yet retains the flexibility to capture complex friction phenomena. We demonstrate, on an underactuated and nonlinear system, that the learned friction models, trained solely on small and noisy datasets, accurately reproduce dynamic friction properties with significantly higher fidelity than the simplified models commonly used in robotics simulators. Crucially, we show that our approach enables the learned models to be transferable to systems they are not trained on. This ability to generalize across multiple systems streamlines friction modeling for complex, underactuated tasks, offering a scalable and interpretable path toward improving friction model accuracy in robotics and control.