Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

2026-06-18

AI Summary

This work addresses the challenge of accurately modeling soft robots, whose continuum structures and highly nonlinear dynamics are difficult to capture with conventional approaches: physics-based models often lack expressive power, while purely data-driven methods sacrifice physical interpretability and energy consistency. To bridge this gap, the authors propose a Gaussian process regression framework that integrates Cosserat rod theory with port-Hamiltonian systems, embedding the port-Hamiltonian structure into data-driven modeling for the first time. This approach learns the dynamics of planar rod-like soft robots while preserving the system’s inherent energy conservation properties. Numerical experiments demonstrate that the method achieves a compelling balance of physical interpretability, energy consistency, and data adaptability, enabling accurate and stable characterization of soft robot dynamics and offering a novel paradigm for modeling continuum systems.

Abstract

Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order principles is typically time-demanding, and their expressiveness is limited, whereas data-driven models lack interpretability and physical consistency. This work aims to overcome these challenges by introducing a port-Hamiltonian Gaussian Process Regression framework for learning and simulating the dynamics of planar, rod-like soft robots. In detail, the proposed model integrates Cosserat rod theory and Hamiltonian physics with data-driven inference to preserve the system's energy structure while accurately learning the rod dynamics. Numerical simulations show that we can achieve accurate and energy-consistent representations of a rod-like soft robot, showing the potential for a robust and interpretable pathway for modeling complex continuum mechanics.

Problem

• soft robots
• dynamics modeling
• physical consistency
• interpretability
• continuum mechanics

Innovation

• port-Hamiltonian
• Gaussian Process Regression
• Cosserat rod theory
• soft robotics
• energy-consistent modeling