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About

Jacopo Aleotti is a distinguished robotics researcher whose work spans robot programming by demonstration, grasp planning, human-robot interaction, and autonomous underwater systems. Over two decades, he has made foundational contributions to how robots learn from human behavior, accumulating hundreds of citations across his most influential publications. Aleotti's early research pioneered virtual reality-based programming by demonstration, enabling robots to acquire manipulation skills directly from human examples — work recognized with 88 and 82 citations respectively. His trajectory learning methods, including clustering and stochastic approximation techniques, provided robust solutions for handling sensor noise and movement variability in real-world settings, earning over 150 combined citations. His grasp recognition framework, which leverages contact point geometry and hand posture classification, remains a landmark contribution to robot manipulation research. Beyond skill acquisition, Aleotti advanced human-robot collaboration through comfort-aware object handover systems and affordance-sensitive approaches, reflecting a deep commitment to safe and intuitive robot behavior around people. His later work expanded into 3D shape segmentation for part-based grasping and next-best view planning for unknown objects. He also contributed to Italy's national MARIS project, addressing the complex challenge of autonomous underwater intervention robotics. Collectively, his research has significantly shaped modern intelligent robot manipulation and human-robot cooperation.