Fabio Maria Carlucci

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About

Fabio Maria Carlucci’s research sits at the intersection of computer vision, robotics, and socially-aware AI, with a focus on enabling machines to perceive and interact with the world more intelligently. His major contributions include pioneering work on depth estimation through colorization—introducing the (DE)²CO framework, which leverages deep networks to infer depth from RGB images, a critical capability for robots navigating unstructured environments. This work has garnered 37 citations and highlights his knack for bridging simulation and reality. Carlucci also advanced robot social intelligence by proposing explicit representations of social norms (30 citations), ensuring robots can behave appropriately in human-populated spaces. Additionally, his research on data augmentation (27 citations) directly addresses the domain gap between computer vision datasets and real-world robot perception, improving object recognition robustness. Through these efforts, Carlucci has demonstrated a clear talent for translating theoretical vision advances into practical robotic systems, making him a notable figure in the push toward autonomous, socially-compliant machines.