Motoyuki Ozeki
Papers
7
Total Citations
23
H-Index
3
About
Motoyuki Ozeki is a pioneering researcher in human-robot interaction (HRI), focusing on how robots can learn from and connect with people through intuitive, socially-aware behaviors. His work centers on three key areas: visual attention control for interactive robots, robot learning through imitation and reinforcement, and the subtle social cues that make robots appear more teachable and empathetic. Ozeki’s most influential contribution is his exploration of robot hesitation—deliberate delays in motion that significantly improve human teaching efficiency and make robots seem more receptive to instruction, a concept detailed in his highly cited 2010 paper (5+ citations). He also developed a novel top-down visual attention model based on a particle filter (2011, 6 citations), enabling robots to focus on task-relevant features during interaction. Further notable work includes studies on empathy in HRI and the impact of high-pitched speech on robot perception. With over 20 citations across his core papers, Ozeki’s research provides foundational insights into designing robots that learn naturally from humans by leveraging timing, imitation, and emotional resonance.
Research Focus
Key Achievements
Top Papers
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- 7The hesitation of a robot2 citations · 2010