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

Koopman Operators for Modeling and Control of Soft Robotics