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About

Lorenzo Mur-Labadia is an emerging researcher specializing in computer vision, robotic perception, and affordance learning — a field that explores how agents identify and leverage action possibilities within their environments. His work sits at a compelling intersection of deep learning, Bayesian inference, and embodied AI, with particular focus on enabling robots and assistive devices to perceive and interact with the world more intelligently. Among his most notable contributions is his development of multi-label affordance segmentation from egocentric vision (2023, 13 citations), which achieves pixel-precise detection critical for robotics and assistive technology applications. Complementing this, his Bayesian deep learning framework for affordance segmentation (2023, 8 citations) introduces principled uncertainty quantification — a vital capability for safe, reliable robotic systems. His work on robust Bayesian semantic mapping (2023, 10 citations) further advances how robots build richer, semantically meaningful representations of their surroundings. More recently, Mur-Labadia has pushed into anticipatory perception, investigating how attention models can support short-term object interaction prediction (2024). His continued exploration of uncertainty estimation via Visual Transformers reflects a commitment to trustworthy, interpretable AI. With a growing citation record across just a few years, he represents a promising voice in the future of intelligent robotic perception.