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About

Curtis C. Johnson is a leading researcher in the field of soft robotics, with a focus on model-based control, dynamic modeling, and design optimization for compliant, continuum-joint robots. His work addresses the fundamental challenge of making soft robots—inherently underdamped and difficult to control—operate reliably and repeatably in real-world tasks. Johnson’s most cited paper, “Using First Principles for Deep Learning and Model-Based Control of Soft Robots” (2021, 46 citations), pioneers a hybrid approach that combines first-principles physics with deep learning to enable precise model-based optimal control. He further advanced the field with “Model Reference Predictive Adaptive Control for Large-Scale Soft Robots” (2020, 36 citations), introducing a novel control scheme that integrates model predictive control with adaptive mechanisms to overcome model inaccuracies. Johnson also developed the “Tractable and Intuitive Dynamic Model for Soft Robots via the Recursive Newton-Euler Algorithm” (2022), offering a computationally efficient yet accurate modeling tool. His work on design optimization for rough terrain traversal using compliant quadruped robots (2022) and the PneuDrive embedded pressure control system (2024) demonstrates his commitment to practical, large-scale applications. With growing citation impact, Johnson is shaping the future of soft robotics for search and rescue, exploration, and beyond.