  Soft robots are challenging to model and control as inherent non-linearities
(e.g., elasticity and deformation), often requires complex explicit
physics-based analytical modeling (e.g., a priori geometric definitions). While
machine learning can be used to learn non-linear control models in a
data-driven approach, these models often lack an intuitive internal physical
interpretation and representation, limiting dynamical analysis. To address
this, this paper presents an approach using Koopman operator theory and deep
neural networks to provide a global linear description of the non-linear
control systems. Specifically, by globally linearising dynamics, the Koopman
operator is analyzed using spectral decomposition to characterises important
physics-based interpretations, such as functional growths and oscillations.
Experiments in this paper demonstrate this approach for controlling non-linear
soft robotics, and shows model outputs are interpretable in the context of
spectral analysis.
