Satoshi Shiba
Papers
1
Total Citations
3
H-Index
1
About
Satoshi Shiba is a researcher in the field of robotics and artificial intelligence, with a particular focus on autonomous systems and adaptive control. His most notable contribution, "Autonomous reconfiguration of robot shape by using Q-learning" (2009), explores the intersection of reinforcement learning and modular robotics, demonstrating how robots can autonomously alter their physical configurations to adapt to changing environments. This work, while accumulating 3 citations to date, represents an early and innovative application of Q-learning—a model-free reinforcement learning algorithm—to the challenge of robotic self-reconfiguration, a problem central to the development of versatile, resilient robotic systems. Shiba's research addresses key challenges in autonomous decision-making and adaptive morphology, laying groundwork for future advances in self-assembling and shape-shifting robots. Though his citation count is modest, his work contributes to the foundational literature on learning-based control in robotics, offering insights that could inform more complex autonomous systems in the future.
Research Focus
Key Achievements
Top Papers
- 1Autonomous reconfiguration of robot shape by using Q-learning3 citations · 2009