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About

Matteo Bernabei is a leading researcher in the field of soft robotics, with a primary focus on data-driven modeling, adaptive control, and the unique challenges posed by modular and interchangeable soft robotic systems. His work addresses a critical bottleneck in the field: the inherent variability and non-linearity of soft materials, which make traditional control methods ineffective. Bernabei’s major contributions include pioneering the use of data-driven techniques—such as BiLSTM neural networks—to create controllers that can adapt to different module configurations and manufacturing inconsistencies. His highly cited 2024 review, "Data-Driven Methods Applied to Soft Robot Modeling and Control," with 94 citations, has become a foundational resource for researchers seeking to navigate this complex landscape. Beyond reviews, his innovative hybrid adaptive controller tackles the critical issue of interchangeability, enabling consistent performance across physically different but functionally similar soft robots. This work is vital for scaling soft robotics from lab prototypes to real-world applications in surgery, rehabilitation, and industrial gripping. Bernabei’s research is shaping a future where soft robots are not just compliant, but also reliably and intelligently controlled.