Papers
45
Total Citations
717
H-Index
12
About
Yukiyasu Domae is a prominent robotics researcher whose work centers on industrial automation, robotic bin picking, and human-robot collaboration. Best known for pioneering contributions to the challenge of automating object retrieval from unstructured environments, Domae has fundamentally advanced how robots perceive, evaluate, and execute grasps in real-world manufacturing settings. His 2014 paper on fast graspability evaluation using single depth maps (151 citations) established an influential framework for enabling robots to assess grasping feasibility efficiently — a cornerstone contribution to modern bin-picking systems. Domae has since extended this work to tackle some of the field's most stubborn challenges, including picking tangled and flexible objects such as wire harnesses, developing both learning-based approaches and topological methods to address entanglement — problems previously considered largely unsolved. Beyond grasping, Domae has shaped the broader industrial robotics landscape through systematic technology analyses of robot competitions and comprehensive reviews of industrial robot trends. His 2021 work on digital twin-driven human-robot collaboration (92 citations) reflects his expanding vision for safe, ergonomically aware manufacturing environments. With over 500 cumulative citations, Domae's research represents a sustained and impactful effort to bridge the gap between laboratory robotics and practical factory automation.
Research Focus
Key Achievements
Top Papers
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- 2Digital Twin-Driven Human Robot Collaboration Using a Digital Human92 citations · 2021
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- 5Learning Based Robotic Bin-picking for Potentially Tangled Objects46 citations · 2019
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- 8Recent Trends in the Research of Industrial Robots and Future Outlook24 citations · 2019
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