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.

翻译：作为固有的非线性(如弹性和变形),软体机器人对于模型和控制具有挑战性,因为其固有的非线性(如弹性和变形)往往需要复杂的、以物理为基础的分析模型(如先验几何定义),因此,软体机器人对于模型和控制具有挑战性。虽然机器学习可用于在数据驱动方法中学习非线性控制模型,但这些模型往往缺乏直观的内部物理解释和代表,限制了动态分析。为解决这一问题,本文件介绍了一种方法,用Koopman操作员理论和深神经网络来提供非线性控制系统的全球线性描述。具体地说,通过全球线性线性动态,对Koopman操作员进行分析时使用了光谱分解定位来描述重要的基于物理学的解释,如功能增长和振荡。本文中的实验展示了这种控制非线性软机器人的方法,并展示了模型产出可以在光谱分析中解释。