Max Robotics — Global Service Robot Directory + US Certification

About

Mattia Cenedese is a researcher specializing in nonlinear dynamics, model reduction, and data-driven control of high-dimensional robotic systems. His work sits at the intersection of applied mathematics and robotics, where he develops principled frameworks that bridge theoretical dynamical systems theory with practical engineering challenges. Cenedese's most significant contributions center on Spectral Submanifold (SSM) reduction — a mathematically rigorous approach to constructing low-dimensional surrogates of complex nonlinear systems directly from data. His 2023 paper on data-driven SSM reduction for optimal control of high-dimensional robots (24 citations) demonstrated that these geometric structures could be exploited to dramatically reduce computational complexity while preserving essential system dynamics, enabling real-time nonlinear optimal control that generalist learning-based methods struggle to achieve. Building on this foundation, he extended SSM-based reduction into robust model predictive control frameworks, explicitly addressing the model uncertainty introduced during dimensionality reduction — a critical practical concern often overlooked in the field. More recently, his work on continuum robots (2025) has demonstrated how highly dissipative robotic structures naturally expose dominant low-dimensional dynamics, opening transformative possibilities for flexible robotics in both terrestrial and extraterrestrial applications. Collectively, his research offers generalizable, structure-preserving tools for next-generation robotic control.