Yutaka Katoh
Papers
2
Total Citations
39
H-Index
2
About
Yutaka Katoh is a leading researcher in humanoid robotics, with a primary focus on integrating vision-based perception with reinforcement learning to generate adaptive, rhythmic locomotion. His work addresses the fundamental challenge of enabling humanoid robots to walk stably and purposefully in dynamic environments. Katoh’s major contribution lies in developing hierarchical control architectures that combine central pattern generators (CPGs) or neural oscillators for stable rhythmic motion with reinforcement learning for high-level decision-making. His most cited paper (2004, 30 citations) introduces a method for learning walking parameters directly from visual information, allowing a humanoid to adjust its gait in real time. A subsequent study (2004, 9 citations) extends this framework to generate complete vision-guided behaviors, demonstrating how robots can learn to navigate and interact with their surroundings. By bridging low-level motor control with high-level learning, Katoh’s work has been instrumental in advancing autonomous humanoid capabilities, influencing subsequent research in bio-inspired locomotion and sensorimotor integration.
Research Focus
Key Achievements
Top Papers
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