Purpose of review: We review recent advances in algorithmic development and validation for modeling and control of soft robots leveraging the Koopman operator theory. Recent findings: We identify the following trends in recent research efforts in this area. (1) The design of lifting functions used in the data-driven approximation of the Koopman operator is critical for soft robots. (2) Robustness considerations are emphasized. Works are proposed to reduce the effect of uncertainty and noise during the process of modeling and control. (3) The Koopman operator has been embedded into different model-based control structures to drive the soft robots. Summary: Because of their compliance and nonlinearities, modeling and control of soft robots face key challenges. To resolve these challenges, Koopman operator-based approaches have been proposed, in an effort to express the nonlinear system in a linear manner. The Koopman operator enables global linearization to reduce nonlinearities and/or serves as model constraints in model-based control algorithms for soft robots. Various implementations in soft robotic systems are illustrated and summarized in the review.

翻译：综述目的：我们回顾了利用Koopman算子理论对软体机器人进行建模与控制的最新算法进展与验证成果。近期研究发现：我们识别出该领域近期研究工作的以下趋势。（1）在数据驱动近似Koopman算子过程中，用于提升函数的设计对软体机器人至关重要。（2）鲁棒性考量得到重视。相关研究致力于降低建模与控制过程中不确定性与噪声的影响。（3）Koopman算子已被嵌入多种基于模型的控制框架中，以实现对软体机器人的驱动。总结：由于软体机器人具有柔顺性与非线性特性，其建模与控制面临关键挑战。为应对这些挑战，研究者提出了基于Koopman算子的方法，旨在以线性方式表达非线性系统。Koopman算子能够实现全局线性化以降低非线性程度，并/或作为软体机器人基于模型控制算法中的模型约束。本综述对软体机器人系统中的多种实现方式进行了阐述与总结。