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About

Alessio Del Bue is a robotics and computer vision researcher whose work spans autonomous exploration, active perception, robotic grasping, and human-robot interaction. His research is unified by a central challenge: enabling robots to intelligently perceive, navigate, and interact with complex real-world environments. Among his most recognized contributions is a novel entropy-based information gain metric for autonomous 3D indoor mapping, which blends hand-crafted and data-driven approaches to solve the next-best-view problem, earning 47 citations. His development of the world's first autonomous endoscope for avionic duct inspection — capable of navigating cavities as narrow as 6mm — demonstrates his ability to translate fundamental research into demanding industrial applications, garnering 27 citations. Del Bue has also made notable advances in robotic grasping through 3DSGrasp, a shape-completion framework that improves grasp reliability from partial point cloud data. His POMP and POMP++ frameworks tackle active visual search in unknown environments using Monte Carlo planning under partial observability. More recently, his XBG system advances imitation learning for humanoid robots in collaborative scenarios. With contributions ranging from multi-robot visual coverage to self-training object detection, Del Bue's body of work represents a broad and impactful research agenda at the frontier of intelligent autonomous systems.