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About

Matteo Lucchi is a robotics researcher whose work lies at the intersection of human-robot interaction, multi-vehicle coordination, and intelligent control systems. His key contributions span three critical areas: contactless control for sensitive environments, mission planning for industrial logistics, and deep reinforcement learning for robotic manipulation. His most cited work (2020, 25 citations) introduces gesture-based capacitive sensing for contactless control of mobile manipulators, addressing contamination risks in clean rooms and operating theaters while enabling safe human-robot collaboration. In his 2015 paper (20 citations), Lucchi developed a dynamic mission assignment methodology for multi-vehicle systems in industrial logistics, explicitly modeling traffic patterns for optimized fleet coordination—work supported by the European Union's FP7 framework. More recently, he contributed to the open-source robo-gym toolkit (2020, 5 citations), bridging the gap between simulated and real-world deep reinforcement learning for robotics. His 2022 work tackles dynamic obstacle avoidance for manipulators using DRL, pushing toward more adaptive industrial automation. With a research portfolio spanning fundamental control theory to practical open-source tools, Lucchi demonstrates how sophisticated algorithms can be translated into real-world robotic systems, making him a notable figure in modern robotics research.