Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
For researchers in soft robotics, this work provides a hybrid modeling approach that balances physical consistency and data-driven accuracy, though it is limited to planar rod-like robots.
The paper introduces a port-Hamiltonian Gaussian Process Regression framework that integrates Cosserat rod theory with data-driven inference to model planar soft robot dynamics, achieving accurate and energy-consistent representations in numerical simulations.
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.