Enrico Regolin

University of Pavia, University of Trieste

Papers

Total Citations

H-Index

About

Enrico Regolin is a robotics researcher focused on the intersection of motion planning, control theory, and machine learning for autonomous systems operating in complex, dynamic environments. His major contributions lie in two key areas: agile autonomous vehicle control and robot navigation in crowded spaces. In his most cited work, "Search-based task and motion planning for hybrid systems: Agile autonomous vehicles" (2023, 17 citations), Regolin addresses the challenge of achieving time-optimal driving on low-friction surfaces by incorporating vehicle drifting into predictive control frameworks—a significant advance for autonomous driving in extreme conditions. Additionally, his research on "Robot Navigation in Crowded Environments" (2023, 7 citations; 2022, 2 citations) leverages reinforcement learning to train neural controllers for differential drive robots, enabling safe navigation through densely packed human crowds where traditional techniques often fail. These works demonstrate Regolin’s ability to bridge theoretical planning algorithms with practical, real-world deployment, making his research highly relevant for autonomous vehicles, service robotics, and human-robot interaction. With a growing citation record, Regolin is establishing himself as a promising voice in the field of intelligent, adaptive robotic systems.