Eugenio Fedeli
Papers
1
Total Citations
3
H-Index
1
About
Eugenio Fedeli is a researcher at the forefront of intelligent robotics and autonomous systems, with a primary focus on multi-robot coordination, deep reinforcement learning, and real-world sanitation applications. His most notable contribution is the development of a multi-robot Deep Q-Learning framework for priority-based sanitization of railway stations, a timely response to the challenges posed by the Covid-19 pandemic. This work, published in 2023 and already garnering 3 citations, demonstrates how distributed learning algorithms can leverage anonymous sensor data to enable fleets of robots to collaboratively and efficiently disinfect high-traffic public spaces. By integrating deep reinforcement learning with practical infrastructure needs, Fedeli’s research bridges the gap between theoretical AI advances and pressing societal demands. His work not only advances the field of multi-agent systems but also offers scalable, data-driven solutions for public health and safety. Fedeli’s contributions are particularly impactful for students and researchers interested in applying reinforcement learning to real-world robotics, showcasing how intelligent coordination can transform critical infrastructure management.
Research Focus
Key Achievements
Top Papers
- 1