Papers
5
Total Citations
94
H-Index
4
About
Matteo Santoro is a robotics researcher whose work bridges human-robot interaction, computer vision, and medical robotics. His research explores how robots can learn from and interact with humans, with a particular focus on the ethical and practical implications of autonomous systems. Santoro’s most cited paper, “Learning robots interacting with humans: from epistemic risk to responsibility” (2007, 37 citations), examines the challenges of robot learning in human environments, addressing critical questions about risk and accountability. He is also known for developing the iCub World dataset (2013, 30 citations), a pioneering object recognition dataset acquired through human-robot interaction using the iCub humanoid platform. This work enables rapid data collection with ground truth annotations, advancing vision research in robotics. Santoro further contributed to hierarchical representation learning for visual recognition in robotics (2013, 15 citations), investigating how sophisticated image representations can improve robotic perception. His work extends into medical applications, including a computer-assisted robotic platform for Focused Ultrasound Surgery (2015, 10 citations), demonstrating the translation of robotics into clinical settings. With a career spanning ethical frameworks, dataset creation, and surgical robotics, Santoro’s research has shaped how robots perceive, learn, and safely interact with humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2iCub World: Friendly Robots Help Building Good Vision Data-Sets30 citations · 2013
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- 5iCub World: Friendly Robots Help Building Good Vision Data-Sets2 citations · 2013