K. Shibata
Papers
3
Total Citations
17
H-Index
3
About
K. Shibata is a pioneering researcher in robotic manipulation and skill acquisition, whose work bridges machine learning, neural networks, and dynamic control systems. Their key research areas include learning-based robotic control, hand-eye coordination, and dexterous manipulation for industrial automation. Shibata’s most influential contribution is the development of a learning and dynamic pattern generating architecture for robotic baseball batting, which demonstrated how robots can acquire complex manipulation skills without explicit trajectory specification—a foundational approach in skill-based robotics. This work, with 10 citations, remains a reference for dynamic manipulation tasks. Shibata also advanced reinforcement learning for hand-eye coordination, showing that a robot arm can learn reaching tasks using raw visual and joint-angle inputs without explicit coordinate calculations, a contribution cited 4 times for its elegant simplicity. In recent work, Shibata developed a versatile robotic hand for jig-less assembly of shaft-shaped parts, integrating alignment, picking, reorientation, and positioning functions into a single end-effector. This innovation, with 3 citations, addresses practical challenges in flexible manufacturing. Across their career, Shibata has consistently focused on enabling robots to learn and adapt in unstructured environments, making their research valuable for students and engineers interested in intelligent robotic systems and industrial automation.
Research Focus
Key Achievements
Top Papers
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