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About

Flavio De Vincenti is a roboticist advancing the frontier of legged locomotion and multi-agent coordination. His primary research centers on control-aware design optimization and nonlinear model predictive control (NMPC) for quadrupedal robots, where he has pioneered methods to analytically differentiate whole-body controllers with respect to design parameters—enabling gradient-based co-design of robot morphology and control. His 2021 paper on this approach has garnered 15 citations, while his 2022 work on replicating animal motions via NMPC-driven locomotion controllers has accumulated 14 citations, demonstrating significant impact in the field. De Vincenti further contributed a versatile NMPC formulation leveraging second-order sensitivity analysis (7 citations) and developed Ungar, an open-source C++ framework for real-time optimal control that uses template metaprogramming for high-dimensional problems. More recently, he has expanded into multi-robot systems, introducing a scalable reinforcement learning approach for decentralized coordination of arbitrary robot teams to arbitrary goals. His work bridges theoretical rigor with practical implementation, offering tools and insights that empower other researchers to build more agile, efficient, and cooperative robotic systems.