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About

Lucio Marcenaro is a leading researcher in autonomous systems, with a focus on endowing artificial agents with computational self-awareness. His work centers on developing generative and descriptive models that allow autonomous systems to understand their own state and environment, enabling them to incrementally learn from experience and detect abnormalities. Marcenaro’s most cited paper, “Multisensorial Generative and Descriptive Self-Awareness Models for Autonomous Systems” (2020, 33 citations), establishes foundational frameworks for machines to build and update internal models of their surroundings. He has also contributed to the theoretical underpinnings of the field, participating in high-level discussions on future development, as seen in his 2021 plenary panel report (12 citations). Beyond theory, Marcenaro applies his expertise to practical challenges in human-robot interaction, such as estimating container mass from visual data using RGB-D cameras. His work on incremental learning of abnormalities (2019, 9 citations) is particularly notable for enabling autonomous systems to adapt to novel situations without forgetting prior knowledge. Through his research, Marcenaro is shaping how intelligent machines perceive, learn, and interact with the world.