Efficient data-driven approach for bending deformation capture of fiber-reinforced soft actuators

To address the inherent nonlinear response and the complexity of structural and material parameters that hinder the customization of fiber-reinforced soft actuators (FRSAs), this study proposes a robust data-driven method for accurately predicting their bending deformation. The approach creatively integrates finite element analysis (FEA) models, multiple advanced machine learning algorithms, and the concept of transfer learning, leveraging both simulation and experimental data for modeling. Five