Shotaro Kamio
Papers
8
Total Citations
110
H-Index
5
About
Shotaro Kamio is a pioneering researcher in evolutionary robotics and multi-agent systems, whose work bridges the gap between theoretical algorithms and real-world robot adaptation. His key research areas include genetic programming, reinforcement learning, and cooperative multi-agent planning. Kamio’s most significant contribution is the development of an integrated technique combining genetic programming (GP) and reinforcement learning (RL) for real robots, enabling them to adapt actions directly in physical environments without precise simulators—a breakthrough that has garnered 48 citations. He further advanced multi-agent cooperation with a random sampling path planning algorithm for tasks like object transport, achieving 24 and 13 citations for related works. Notably, his research on humanoid robots demonstrates practical applications, such as cooperative object transport using RRT path planning. Kamio also coined the term “ingeniously behaving agents” (IBA) to describe robots that exhibit adaptive, intelligent behaviors in real-world settings. With over 110 total citations across his publications, his work has laid foundational methods for evolutionary learning and multi-robot coordination, inspiring subsequent research in autonomous systems and embodied intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Random sampling algorithm for multi-agent cooperation planning13 citations · 2005
- 4
- 5Researches on ingeniously behaving agents6 citations · 2003
- 6Evolutionary construction of a simulator for real robots4 citations · 2005
- 7
- 8