Koopman Operators for Modeling and Control of Soft Robotics

TL;DR

This paper reviews recent developments in using Koopman operator theory to model and control soft robots, highlighting the importance of lifting functions, robustness, and integration into control strategies for handling nonlinearities.

Contribution

It provides a comprehensive overview of how Koopman operators are applied to soft robotics, emphasizing recent advancements and key challenges addressed.

Findings

Design of lifting functions is crucial for accurate modeling.

Robustness to noise and uncertainty is increasingly prioritized.

Koopman operators are integrated into control frameworks for soft robots.

Abstract

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.