Tsuyoshi Yokoya
Papers
5
Total Citations
349
H-Index
4
About
Tsuyoshi Yokoya is a robotics researcher whose work spans robot learning, autonomous systems, and multi-robot coordination. He is best known for his pioneering contributions to applying deep reinforcement learning to high-precision manufacturing tasks, particularly the challenging peg-in-hole assembly problem, where robotic accuracy must exceed mechanical tolerances. His 2017 paper on this topic has garnered over 300 citations, establishing it as a landmark reference in robot learning for industrial automation and demonstrating that robots can autonomously master tight-clearance assembly without the tedious manual parameter tuning traditionally required. Beyond learning-based manipulation, Yokoya has made meaningful contributions to autonomous environmental perception and mapping. His research on coordinated mobile robot teams using laser range finders for 3D map generation of unknown environments, alongside his work on calibrating distributed vision networks into unified coordinate systems, reflects a broader interest in enabling robots to sense, understand, and navigate real-world spaces with minimal human intervention. Together, these research threads paint a picture of a scientist deeply committed to reducing human burden in robot deployment — both on factory floors and in dynamic everyday environments — making his work highly relevant to students and researchers pursuing intelligent, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Deep reinforcement learning for high precision assembly tasks300 citations · 2017
- 2Deep Reinforcement Learning for High Precision Assembly Tasks29 citations · 2017
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