Andrew Hess

Papers

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About

Andrew Hess is a leading researcher in the field of soft robotics, with a particular focus on bio-inspired robotic swimmers and the complex fluid-structure interactions that govern their motion. His work bridges computational modeling and control theory to advance the design of flexible, underwater robots. Hess made a significant contribution with his 2018 study, which developed a fictitious domain/active-strain method to simulate soft robotic swimmers, earning 24 citations for its novel approach to capturing nonlinear fluid dynamics. Building on this, his 2020 paper on control-oriented modeling using Koopman operators—cited 33 times—introduced a data-driven framework that simplifies the highly nonlinear behavior of soft swimmers for real-time control. This work is pivotal for enabling autonomous, agile underwater robots inspired by jellyfish and rays. Hess’s research is notable for its interdisciplinary impact, merging mechanics, control theory, and bio-inspired design. His achievements highlight a commitment to solving fundamental challenges in soft robotics, making him a key figure for students and researchers interested in the future of adaptive, underwater robotic systems.