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About

Marco Baglietto is a robotics researcher whose work spans autonomous navigation, multi-robot systems, and motion planning under uncertainty. His early contributions focused on coordinated multi-agent exploration, developing frameworks that enable teams of autonomous robots to efficiently map unknown environments using information-theoretic approaches — work that earned 27 citations and laid groundwork for collaborative robotic systems. He has also explored multi-robot coordination through innovative applications of RFID technology and frequency-based coverage strategies for meaningful environmental locations. A significant thread of Baglietto's research addresses the challenge of safe navigation under uncertainty. His work on human navigation with 6DOF IMU and laser scanning (35 citations, his most-cited contribution) demonstrates his interest in bridging human and robotic spatial reasoning. More recently, he has made notable advances in belief-space planning, developing integrated frameworks — including MPTP and task-motion planning architectures — that couple task-level reasoning with motion planning in probabilistic environments. His 2022 work on exact and bounded collision probability under Gaussian uncertainty offers principled guarantees for safe trajectory generation. Collectively, Baglietto's research addresses fundamental challenges in making autonomous robots operate reliably and safely in complex, uncertain real-world settings.