Taylor Sorensen
Papers
1
Total Citations
46
H-Index
1
About
Taylor Sorensen is a leading researcher at the intersection of soft robotics, control theory, and machine learning. Their work focuses on overcoming the fundamental challenge of achieving precise, repeatable control in compliant, underdamped robotic systems—a problem that has long hindered the practical deployment of soft robots. Sorensen’s most impactful contribution, the 2021 paper "Using First Principles for Deep Learning and Model-Based Control of Soft Robots" (46 citations), introduces a novel framework that combines physics-based first-principles modeling with deep learning to create accurate, data-efficient dynamic models. This approach enables model-based optimal control strategies that allow soft robots to perform complex, repeatable tasks previously thought impossible for such flexible platforms. By bridging analytical modeling and data-driven methods, Sorensen has provided a scalable pathway for soft robots to transition from laboratory curiosities to reliable tools in manufacturing, healthcare, and exploration. Their work is widely cited by researchers in soft robotics and control, reflecting its significance in advancing the field toward practical, high-performance applications.
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