Papers
5
Total Citations
66
H-Index
3
About
Davide Liuzza’s research sits at the intersection of multi-robot systems, control theory, and human-robot interaction, with a focus on enabling complex, coordinated behaviors. His most impactful work, a 2021 paper with 54 citations, introduces a framework for planning feasible trajectories for fleets of quad-rotors using Signal Temporal Logic (STL) specifications. This approach allows multi-aerial systems to perform intricate inspection tasks—such as monitoring power lines or wind turbines—while avoiding obstacles and adhering to mission constraints, a significant advance for autonomous infrastructure maintenance. Liuzza also explores the human side of coordination, developing deep learning controllers for artificial avatars in joint motor tasks. His 2021 study on dynamic input deep learning control (4 citations) addresses how robots and virtual agents can seamlessly synchronize with humans in multi-agent scenarios, with applications in rehabilitation and collaborative work. By combining formal logic-based planning with adaptive learning, Liuzza’s work bridges the gap between theoretical guarantees and real-world adaptability, positioning him as a key contributor to the future of safe, cooperative autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Deep learning control of artificial avatars in group coordination tasks3 citations · 2019
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