PINN-Ray: A Physics-Informed Neural Network to Model Soft Robotic Fin Ray Fingers

TL;DR

This paper introduces PINN-Ray, a physics-informed neural network that accurately models the complex deformation of soft robotic fins, improving over traditional methods and enabling faster, more reliable soft robot design and analysis.

Contribution

The paper presents PINN-Ray, a novel physics-informed neural network that incorporates elastic mechanics principles and experimental data for modeling soft robotic deformation, addressing data scarcity and generalization issues.

Findings

PINN-Ray outperforms finite element models in accuracy.

The method is robust to data scarcity and complex geometries.

It enables fast prototyping and deformation characterization of soft robotic fingers.

Abstract

Modelling complex deformation for soft robotics provides a guideline to understand their behaviour, leading to safe interaction with the environment. However, building a surrogate model with high accuracy and fast inference speed can be challenging for soft robotics due to the nonlinearity from complex geometry, large deformation, material nonlinearity etc. The reality gap from surrogate models also prevents their further deployment in the soft robotics domain. In this study, we proposed a physics-informed Neural Networks (PINNs) named PINN-Ray to model complex deformation for a Fin Ray soft robotic gripper, which embeds the minimum potential energy principle from elastic mechanics and additional high-fidelity experimental data into the loss function of neural network for training. This method is significant in terms of its generalisation to complex geometry and robust to data scarcity as compared to other data-driven neural networks. Furthermore, it has been extensively evaluated to model the deformation of the Fin Ray finger under external actuation. PINN-Ray demonstrates improved accuracy as compared with Finite element modelling (FEM) after applying the data assimilation scheme to treat the sim-to-real gap. Additionally, we introduced our automated framework to design, fabricate soft robotic fingers, and characterise their deformation by visual tracking, which provides a guideline for the fast prototype of soft robotics.