Xavier Hinaut
Inserm, Hamburg University of Technology, Centre National de la Recherche Scientifique, Institut des Maladies Neurodégénératives, Centre Inria de l'université de Bordeaux, Institut Polytechnique de Bordeaux, Synergy University, Institut national de recherche en sciences et technologies du numérique
Papers
8
Total Citations
130
H-Index
6
About
Xavier Hinaut is a leading researcher in neurorobotics and computational linguistics, specializing in how robots can acquire and produce language through neuro-inspired models. His work centers on reservoir computing, particularly Echo State Networks (ESN), to bridge the gap between human language acquisition and robotic interaction. A major contribution is his development of a trainable neural parser that enables robots to learn grammatical constructions from human feedback, demonstrated across 15 languages—a breakthrough for human-robot collaboration. His 2014 study on grammatical construction acquisition via human-robot interaction, with 60 citations, remains foundational, showing how robots can learn syntax without pre-programmed rules. Hinaut also pioneered cross-situational learning models for word-to-meaning mapping, advancing understanding of child language acquisition. His hierarchical-task reservoir architecture for real-time part-of-speech tagging from continuous speech showcases his commitment to practical, anytime language processing. As editor of the "Language and Robotics" special issue, he has shaped discourse on integrating linguistic theory with robotic systems. With over 130 citations across his top papers, Hinaut’s work is pivotal for creating robots that understand and generate language naturally, making him a key figure in the future of interactive AI.
Research Focus
Key Achievements
Top Papers
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- 7Semantic Role Labelling for Robot Instructions using Echo State Networks6 citations · 2016
- 8Editorial: Language and Robotics2 citations · 2021