Francesca Pistilli

Papers

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About

Francesca Pistilli is a rising researcher at the intersection of graph machine learning and robotics. Her work focuses on harnessing the power of deep neural networks for graph-structured data—a rapidly growing area essential for modeling complex, non-Euclidean relationships found in robotic perception and control. Her most-cited survey, "Graph Learning in Robotics: A Survey" (2023), with 14 citations, provides a comprehensive roadmap for applying graph-based learning to robotic tasks, bridging a critical gap between the machine learning and robotics communities. By systematically reviewing how graph neural networks can represent and reason about spatial, temporal, and relational data in robotic systems, Pistilli has helped establish a foundational framework for future research. Her contributions are particularly notable for their potential to advance autonomous navigation, multi-agent coordination, and human-robot interaction. As an early-career scholar, Pistilli is already shaping how robots learn from complex, interconnected environments, making her a promising voice in the next generation of intelligent robotics research.