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About

Fabio Vulpi is a robotics researcher whose work sits at the intersection of autonomous navigation, field robotics, and deep learning, with a particular focus on unstructured environments. His most influential contributions center on terrain classification and perception for both planetary exploration and agricultural robots. Vulpi’s top-cited paper, “Recurrent and convolutional neural networks for deep terrain classification by autonomous robots” (2021, 56 citations), demonstrates his pioneering use of hybrid deep learning architectures to improve how robots interpret complex ground surfaces. He extended this line of inquiry in “An RGB-D multi-view perspective for autonomous agricultural robots” (2022, 39 citations), advancing multi-modal perception for precision farming. His work on “On the role of feature and signal selection for terrain learning in planetary exploration robots” (2021, 34 citations) directly addresses a critical challenge in space robotics: enabling rovers to autonomously assess terrain safety during long-duration missions. Vulpi also developed a practical pose estimation algorithm for agricultural mobile robots using cost-effective RGB-D, IMU, and GNSS sensors. With over 135 citations across his key publications, Vulpi is recognized for bridging theoretical deep learning methods with real-world robotic applications in agriculture and space exploration.