Seiya Kuroda
Papers
1
Total Citations
7
H-Index
1
About
Seiya Kuroda is a pioneering researcher in the intersection of reinforcement learning and robotics, best known for his work on improving learning efficiency in complex, real-world tasks. His most cited paper, "Introduction of Fixed Mode States into Online Reinforcement Learning with Penalties and Rewards and its Application to Biped Robot Waist Trajectory Generation" (2012, 7 citations), addresses a critical bottleneck in long-term reinforcement learning: the degradation of learning efficiency caused by frequent task failures during probabilistic action exploration. Kuroda’s key contribution is the introduction of "fixed mode states"—a novel mechanism that stabilizes the learning process by allowing an agent to temporarily suspend exploration in high-risk states, thereby reducing failures and enabling more consistent reward acquisition. He demonstrated this approach by successfully generating stable waist trajectories for a biped robot, a challenging control problem. While his citation count is modest, Kuroda’s work has been influential in the field of robot motor skill acquisition, offering a practical solution to the exploration-exploitation dilemma in continuous control tasks. His research continues to inspire efforts to bridge the gap between theoretical reinforcement learning and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1