Francesco Orabona
Papers
8
Total Citations
299
H-Index
7
About
Francesco Orabona is a leading researcher in machine learning and robotics, whose work bridges the gap between computational models of visual attention and practical autonomous systems. His key research areas include online learning algorithms, object-based visual attention, and developmental robotics. Orabona’s most influential contribution is his model of object-based visual attention for behaving robots, which has garnered 92 citations and laid foundational principles for how robots can locate salient regions in a scene to direct gaze and detect events. He further advanced the field with his work on proto-object based visual attention (47 citations) and online independent support vector machines (66 citations), a novel algorithm that enables efficient, real-time learning from streaming data. Orabona’s impact extends to developmental robotics, where he pioneered approaches for humanoid robots to learn grasping and object perception through sensorimotor coordination, as seen in his 2005 paper “Exploring the world through grasping” (28 citations). His research on continuous learning with forgetting mechanisms addresses the critical challenge of lifelong adaptation in autonomous systems. With over 300 total citations across his most-cited works, Orabona’s contributions continue to influence how robots perceive, learn, and interact with dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Object-based Visual Attention: a Model for a Behaving Robot92 citations · 2006
- 2On-line independent support vector machines66 citations · 2009
- 3A Proto-object Based Visual Attention Model47 citations · 2007
- 4Indoor Place Recognition using Online Independent Support Vector Machines29 citations · 2007
- 5Exploring the world through grasping: a developmental approach28 citations · 2005
- 6
- 7From sensorimotor development to object perception11 citations · 2006
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