Jonathan Cerbaro
Papers
2
Total Citations
12
H-Index
2
About
Jonathan Cerbaro is a researcher at the intersection of robotics, human-robot interaction, and intelligent control systems. His work focuses on making robotic systems more intuitive and adaptable, particularly through the integration of deep learning and fuzzy logic. A key contribution is his development of a human-robot interface that uses deep learning for motion recognition, enabling remote control via IoT communication—a breakthrough that allows operators to guide robots naturally without fixed, restrictive controls. This work has garnered 7 citations, reflecting its relevance in advancing teleoperation. Cerbaro also authored "WaiterBot," a study comparing fuzzy logic approaches for obstacle avoidance in dynamic, unmapped environments using LiDAR. By testing against both static and moving obstacles like pedestrians, he demonstrated robust, real-time navigation strategies, earning 5 citations. His research is notable for its practical applications in service robotics and autonomous navigation, bridging the gap between theoretical control methods and real-world deployment. Cerbaro’s contributions are shaping the future of responsive, human-centered robotic systems.
Research Focus
Key Achievements
Top Papers
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