Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics

TL;DR

This paper introduces a method combining Koopman operator theory and deep learning to linearize and analyze the complex non-linear dynamics of soft robots, enabling interpretable control and spectral analysis.

Contribution

It presents a novel approach that uses deep neural networks with Koopman operator theory to provide a globally linear and interpretable model of soft robot dynamics.

Findings

Spectral analysis reveals physical growth and oscillation modes.

Model achieves interpretable control of non-linear soft robots.

Demonstrates effective linearization of complex soft robot dynamics.

Abstract

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.