Papers
20
Total Citations
473
H-Index
11
About
Matteo Leonetti is a leading researcher at the intersection of artificial intelligence, robotics, and autonomous decision-making. His work focuses on bridging the gap between high-level AI planning and real-world robot execution, with major contributions in automated planning, reinforcement learning, and human-robot interaction. Leonetti’s most influential paper, "BWIBots: A platform for bridging the gap between AI and human–robot interaction research" (2017, 115 citations), introduced a custom multi-robot platform that enables complex service tasks in open environments. His synthesis of automated planning and reinforcement learning (2016, 102 citations) provides a foundational framework for efficient, robust decision-making in robotics. In the medical domain, his feasibility study on autonomous tissue retraction for robotic-assisted minimally invasive surgery (2020, 76 citations) demonstrates the potential for semi-autonomous surgical assistance. Leonetti has also explored fault-tolerant control for autonomous underwater vehicles, human-like planning for cluttered environments, and tissue segmentation using deep learning. With over 400 total citations and a portfolio spanning from foundational AI theory to applied surgical robotics, Leonetti’s work is shaping the future of autonomous systems that can reason, learn, and act in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 4Planning in Action Language BC while Learning Action Costs for Mobile Robots31 citations · 2014
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- 6Online discovery of AUV control policies to overcome thruster failures19 citations · 2014
- 7Human-like Planning for Reaching in Cluttered Environments18 citations · 2020
- 8Automatic Generation and Learning of Finite-State Controllers17 citations · 2012
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