Papers
14
Total Citations
286
H-Index
7
About
Tatsuya Nagatani is a robotics researcher whose work sits at the intersection of robotic manipulation, automated manufacturing, and intelligent grasping systems. His research has made significant contributions to some of the most persistent challenges in industrial robotics, particularly bin picking — the automated retrieval of randomly arranged parts — and dexterous in-hand manipulation. Nagatani's most influential work, a 2019 analysis of bin-picking technologies drawn from robot competitions (78 citations), established a rigorous performance-metric framework for benchmarking state-of-the-art robotic systems, providing the field with a valuable comparative lens. His earlier investigations into tactile array sensors (48 citations) demonstrated how rich tactile feedback could enable robots to localize and recognize objects during grasping — a contribution that helped bridge perception and manipulation. Complementing this, his research on robust grasping strategies under pose uncertainty (38 citations) addressed real-world assembly environments where part positioning is never perfectly controlled. Beyond manipulation, Nagatani has contributed to robotic cellular manufacturing, developing multiobjective optimization methods for system layout and task scheduling (41 and 29 citations respectively). His 2020 work on a general parts-feeding system integrating bin-picking, regrasping, and kitting reflects a career-long commitment to solving practical, end-to-end automation challenges in industrial settings.
Research Focus
Key Achievements
Top Papers
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- 3Multiobjective layout optimization of robotic cellular manufacturing systems41 citations · 2012
- 4Robust grasping strategy for assembling parts in various shapes38 citations · 2014
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- 6Robotic General Parts Feeder: Bin-picking, Regrasping, and Kitting14 citations · 2020
- 7Bin-picking System for General Objects8 citations · 2015
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