Xavier Domont
Papers
2
Total Citations
22
H-Index
2
About
Xavier Domont’s research lies at the intersection of developmental robotics, human-robot interaction, and cognitive systems, with a focus on enabling robots to learn autonomously through natural, multimodal interaction. His major contributions center on how humanoid robots can build internal concepts by associating visual and auditory information in real time, drawing inspiration from infant development. In his most cited work, “Interactive online multimodal association for internal concept building in humanoids” (2009, 13 citations), Domont demonstrates how the humanoid robot ASIMO can autonomously acquire cognitive structures through interactive learning, integrated into the ALIS 3 system. A second influential paper, “Teaching a humanoid robot: Headset-free speech interaction for audio-visual association learning” (2009, 9 citations), explores how robots can learn associations between spoken labels and visual representations without requiring a headset, using only a few trigger phrases. These works highlight Domont’s pioneering approach to making robot learning more natural, intuitive, and scalable. His research has significant implications for developing socially aware robots capable of learning from everyday human interaction, laying groundwork for more adaptive and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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