Papers

23

Total Citations

206

H-Index

9

About

Hitoshi Aonuma is a pioneering researcher at the intersection of neurobiology, behavioral biology, and bio-inspired robotics, whose work has fundamentally advanced our understanding of decentralized intelligence in biological systems and its applications to adaptive robotics. His most celebrated contributions center on brittle stars — marine echinoderms lacking a central nervous system — where he has demonstrated how decentralized neural architectures enable remarkable locomotion, real-time damage adaptation, and direction-making decisions. His 2017 robot modeled on brittle star neurobiology (35 citations) stands as a landmark achievement, offering a solution to one of robotics' most persistent challenges: rapid physical damage recovery without prohibitive computational costs. Beyond echinoderms, Aonuma has made substantial contributions to insect behavioral biology, studying cricket dominance hierarchies, group-size-dependent aggression, and pheromone-driven social behavior through innovative robot-insect mixed societies. His honeybee waggle dance research further demonstrates his interest in collective intelligence and knowledge propagation. Across these diverse systems, a unifying theme emerges: Aonuma consistently draws biological insights to engineer smarter, more resilient robots, while simultaneously using robotic tools to unlock deeper understanding of animal behavior — a genuinely bidirectional scientific dialogue that has earned him well-deserved recognition in both robotics and neuroscience communities.

Research Focus

Key Achievements

9
H-Index
23
Papers
206
Total Citations
9
Avg Citations/Paper
🏆 Most Cited Paper
A brittle star-like robot capable of immediately adapting to unexpected physical damage
35 citations · 2017
📈 Most Prolific Year: 2014 (4 Papers)
🤝 Key Collaborators: 32
🏛 Institutions: Hokkaido University, Tokyo Institute of Technology, Hokkaido University of Science

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago