Yoshihisa Ijiri
Papers
17
Total Citations
207
H-Index
9
About
Yoshihisa Ijiri is a robotics researcher specializing in robotic assembly, soft robotics, and machine learning for manipulation tasks. His work addresses some of the most persistent challenges in industrial automation, particularly the precise handling and placement of objects in complex assembly scenarios. Ijiri's most influential contributions center on in-hand pose estimation, where he has developed multi-modal sensing approaches that combine visual and tactile information to achieve high-precision object localization — a critical capability for insertion and assembly tasks. His 2021 paper on this topic has garnered 30 citations, reflecting strong community interest. Complementing this, his work on Bayesian state estimation and particle filtering for contact-based pose estimation (27 citations) offers flexible, jig-free solutions for real-world manufacturing. A defining theme across Ijiri's research is the strategic use of physical softness in robotic systems. His cable-driven soft wrist design (28 citations) and associated learning frameworks demonstrate how compliance can simplify control requirements and improve robustness. He has further shown that reinforcement learning — including learning from both successful and failed demonstrations — can efficiently train soft robots for assembly. With over 180 cumulative citations and contributions to the World Robot Challenge 2018, Ijiri's research is making a meaningful impact on the future of flexible, intelligent industrial robotics.
Research Focus
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Top Papers
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