Yoshiyuki Ishihara
Papers
4
Total Citations
84
H-Index
3
About
Yoshiyuki Ishihara is a leading researcher in intelligent robotic manipulation, with a focus on vision-guided bin-picking and automated industrial inspection. His work addresses critical challenges in warehouse automation and manufacturing, particularly for textureless objects and cluttered environments. Ishihara pioneered deep learning approaches for grasp planning using depth images, achieving robust picking of small parcels without relying on expensive human-labeled datasets—his 2020 paper on this topic has garnered 45 citations. He further advanced the field by developing methods to learn suction graspability that account for both grasp quality and robot reachability, a contribution cited 22 times. Notably, Ishihara introduced a novel multiple-suction-cup vacuum gripper system capable of simultaneously grasping multiple objects in cluttered scenes, significantly improving bin-picking efficiency. Beyond grasping, he has contributed to automated visual inspection, creating a one-shot BRDF imaging system for micro-defect detection on curved surfaces using a 6-DOF robot arm, addressing labor shortages in Japan. His research, characterized by practical, industrially-relevant solutions, has been widely recognized for its impact on robotic automation and manufacturing efficiency.
Research Focus
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Top Papers
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