Research Bio

Giuseppe Loianno develops perception, learning, and control algorithms that enable agile, autonomous robots—especially aerial robots—to navigate complex, dynamic environments. His research advances perception, learning, and control for agile and collaborative autonomous robots with minimal human supervision. Loianno's work links rigorous control theory with data-driven perception and real-time systems, producing open-source tools and benchmarks that transfer from laboratory settings to field deployments. Applications include autonomous inspection, search-and-rescue, and human–robot collaboration.

Loianno is an Associate Professor in UC Berkeley's Department of Electrical Engineering & Computer Sciences. Previously at NYU, he has led academic–industry collaborations and serves on editorial boards in robotics. His expertise centers on autonomous robot perception and control, combining visual sensing, machine learning, and systems engineering.

Research Expertise and Interest

robotics, artificial intelligence, visual perception