Tomoyuki Arai
Papers
1
Total Citations
2
H-Index
1
About
Tomoyuki Arai’s research lies at the intersection of reinforcement learning, robotics, and adaptive neural systems, with a focus on enabling autonomous agents to operate effectively in real-world environments. His major contribution centers on addressing the critical challenge of state space construction in reinforcement learning for mobile robots. In his most-cited work, Arai proposed an innovative method using Growing Neural Gas (GNG) to dynamically build state spaces from continuous sensory information, allowing robots to learn and adapt without predefined discretization. This approach overcomes a fundamental limitation of traditional reinforcement learning, which struggles with the unbounded, high-dimensional data typical of physical tasks. While his citation count is modest—his key paper has garnered 2 citations—the work represents a foundational step in bridging neural network-based state representation with practical robotics. Arai’s research is particularly notable for its emphasis on real-world applicability, moving beyond simulated environments to tackle the messy, continuous nature of actual robot navigation and control. His contributions offer a promising pathway for developing more flexible, self-organizing learning systems in autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1