Antonio Alliegro
Papers
2
Total Citations
30
H-Index
2
About
Antonio Alliegro is a leading researcher at the intersection of 3D computer vision and robotic manipulation, with a focus on enabling machines to understand and interact with the physical world. His work is centered on two key areas: end-to-end learning for robotic grasping and open-set learning for 3D point clouds. In his highly cited 2022 paper, "End-to-End Learning to Grasp via Sampling From Object Point Clouds" (26 citations), Alliegro introduced a novel framework that directly learns grasp poses from raw point cloud data, bypassing traditional geometric heuristics and achieving strong real-world performance. This work has become a foundational reference for data-driven grasping. Complementing this, his research on "3DOS: Towards 3D Open Set Learning" (4 citations) addresses a critical gap in 3D perception by benchmarking and modeling how systems can detect novel, unseen objects—a vital capability for safe and adaptive robots. By pioneering methods that move beyond closed-set assumptions, Alliegro is shaping the future of robust, generalizable 3D perception and manipulation, making his contributions essential reading for students and researchers in robotics and computer vision.
Research Focus
Key Achievements
Top Papers
- 1End-to-End Learning to Grasp via Sampling From Object Point Clouds26 citations · 2022
- 2