Fast adaptation of physics-informed hybrid models for pneumatic artificial muscles

G. Wang, R. Chalard, J. Cifuentes, M.T. Pham

Frontiers in Robotics and AI Vol. 13, pp. 1769141

Summary:

Recent advances in foundation models and physics-informed neural networks have demonstrated remarkable generalization and adaptation capabilities across diverse domains. Inspired by these properties, we investigate the adaptation potential of a previously proposed physics-informed hybrid model (PIHM) designed for pneumatic artificial muscles (PAMs). Through a series of experiments, it is demonstrated that, by incorporating an adapter based on physical prior knowledge, the PIHM model can be fine-tuned to transfer across different entity types while significantly reducing training time and maintaining competitive accuracy. The optimization efficiency of the proposed adapter has also been validated through comparison with other transfer learning techniques, such as full fine-tuning (FFT), partial fine-tuning (PFT), and low-rank adaptation (LoRA). These results suggest that embedding structured prior knowledge within hybrid architectures offers a promising solution for fast adaptation of PIHMs in dynamic system modeling.

Spanish layman's summary:

El estudio muestra que un modelo híbrido físico-informado para músculos artificiales neumáticos puede adaptarse a nuevos sistemas mediante un adaptador con conocimiento físico previo, logrando menor tiempo de entrenamiento y una precisión competitiva.

 

English layman's summary:

This study shows that a physics-informed hybrid model for pneumatic artificial muscles can be adapted to new systems using a physics-based adapter, reducing training time while maintaining competitive accuracy versus other transfer learning methods.

Keywords: fine-tuning, foundation model, model adaptation, physics-informed neural networks, pneumatic artificial muscles, transfer learning

JCR-JIF Impact Factor and WoS quartile: 3,000 - Q2 (2024)

DOI reference: https://doi.org/10.3389/frobt.2026.1769141

Published on paper: 2026.

Published on-line: May 2026.

Citation:
G. Wang, R. Chalard, J. Cifuentes, M.T. Pham, "Fast adaptation of physics-informed hybrid models for pneumatic artificial muscles", Frontiers in Robotics and AI, Vol. 13, pp. 1769141, 2026. [Online: May 2026] doi: 10.3389/frobt.2026.1769141