Papers
13
Total Citations
155
H-Index
7
About
Hiroshi Yamakawa is a multidisciplinary researcher whose work spans artificial general intelligence, cognitive architectures, robotics, and neuroscience-inspired computing. His most significant contributions center on developing probabilistic generative models that bridge neuroscientific insights with artificial intelligence, most notably his whole-brain probabilistic generative model (2022, 32 citations), which proposes a framework for realizing human-like cognitive development in robots — a foundational step toward AGI. Complementing this, his hippocampal formation-inspired models (2022, 21 citations; 2024) demonstrate how brain-derived spatial cognition principles can enhance robot navigation and self-localization. Yamakawa has also made meaningful contributions to human-robot interaction, developing reinforcement learning agents capable of autonomously explaining their own behavior (2017, 29 citations), making machine decision-making more transparent and collaborative. His engineering breadth is further evidenced by the design of the OCTOPUS disaster response robot (2016, 27 citations) and early work in surgical robotics (2006). Spanning rehabilitation robotics, design optimization, and early reinforcement learning theory, Yamakawa's career reflects a consistent drive to build intelligent, adaptable systems that take inspiration from both human biology and real-world operational demands.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Design of four-arm four-crawler disaster response robot OCTOPUS27 citations · 2016
- 4Hippocampal formation-inspired probabilistic generative model21 citations · 2022
- 5
- 6
- 7Two Dimensional Evaluation Reinforcement Learning7 citations · 2001
- 8
- 9
- 10