Diego Reforgiato Recupero
University of Cagliari, Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", R2M Solution (Italy)
Papers
20
Total Citations
326
H-Index
10
About
Diego Reforgiato Recupero is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction (HRI). His work centers on enabling intelligent, autonomous robots to understand and navigate both physical and social environments. A key contribution is the development of the "Boundary Node Method" for path planning, a novel approach that has garnered over 77 citations across multiple papers for solving complex single- and multi-robot, multi-goal navigation problems. In parallel, Recupero has pioneered the integration of deep learning and sentiment analysis into HRI, creating systems where robots can interpret human emotions and natural language. His highly cited work on "mimicked and polarized word embeddings" (48 citations) has advanced multi-domain sentiment analysis, while his recent research on integrating conversational agents with knowledge graphs (40 citations) pushes the boundaries of how robots access and reason with structured information. Beyond navigation and language, Recupero has explored virtual reality interfaces for remote robot control and ontology-based knowledge management for social robots in geriatric care. His interdisciplinary approach, combining path planning, sentiment analysis, and knowledge representation, has produced over 250 citations, establishing him as a pivotal figure in creating more responsive, intelligent, and socially aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A Boundary Node Method for path planning of mobile robots58 citations · 2019
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- 5Deep Learning and Sentiment Analysis for Human-Robot Interaction21 citations · 2018
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- 10Merging open knowledge extracted from text with MERGILO10 citations · 2016