Kazuaki Yamada
Papers
7
Total Citations
28
H-Index
4
About
Kazuaki Yamada is a robotics and artificial intelligence researcher whose work centers on reinforcement learning, autonomous robot behavior acquisition, and multi-robot cooperative systems. Over more than two decades of research, Yamada has made consistent contributions to solving one of robotics' fundamental challenges: enabling robots to learn effective sensor-to-motor mappings through trial-and-error rather than explicit programming. His early work on Instance-Based Classifier Generators (1999) laid groundwork for reactive behavior learning, while subsequent investigations demonstrated how neural networks and CMAC architectures could replace memory-intensive lookup tables in Q-learning frameworks, making reinforcement learning more practical for real-world, high-dimensional environments. Yamada's research extends naturally into multi-robot coordination, exploring how distributed reinforcement learning units can enable cooperative manipulation and scalable swarm behaviors. More recently, his interest has broadened to human-robot interaction, including an insightful study analyzing pedestrian gaze behavior to inform the design of socially intuitive mobile robots. His publications, cited across the robotics and machine learning communities, reflect a career dedicated to bridging theoretical learning algorithms and practical autonomous systems — valuable reading for students interested in intelligent robotics and adaptive control.
Research Focus
Key Achievements
Top Papers
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