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About

Sergio Grammatico is a researcher working at the intersection of multi-agent systems, game theory, and autonomous robotics. His work spans several interconnected domains, including distributed decision-making in networked systems, generalized Nash equilibrium computation, and socially aware robot trajectory planning — areas of growing importance as autonomous systems become embedded in everyday human environments. Among his notable contributions, Grammatico has explored how game-theoretic frameworks can enhance the social acceptability of robots navigating human-populated spaces, a study that has garnered 8 citations and represents a meaningful bridge between formal control theory and real-world human-robot interaction. His investigations into time-varying proximal dynamics in multi-agent network games (6 citations) advance the theoretical foundations of distributed optimization, with applications spanning power systems, sensor networks, and consumer markets. Grammatico has also demonstrated a strong commitment to experimental validation, developing a multi-robot laboratory platform using ROSbots to test autonomous driving maneuvers, and contributing to applied competitions such as JRC AUTOTRAC 2020, which explored connected and automated vehicle coordination in urban environments. His work on extremum seeking control for learning Nash equilibria further reflects his ambition to make multi-agent coordination both theoretically rigorous and practically deployable.