  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.
