Papers
12
Total Citations
79
H-Index
4
About
Yuya Okadome’s research sits at the intersection of robotics, machine learning, and human-robot interaction, with a focus on creating adaptable, bio-inspired systems for real-world tasks. His most cited work, a 2019 paper on a mobile dual-arm manipulation robot for convenience store stocking, demonstrates his practical impact—achieving 39 citations by developing universal vacuum grippers that adapt to varied item shapes. Okadome has also pioneered human-like musculoskeletal robot platforms, such as HUMA, enabling physical interaction studies that mimic human adaptability in unstructured environments. His contributions extend to computational methods, including adaptive Locality-Sensitive Hashing for Gaussian process regression and predictive control for redundant robots, both published in 2014. Notably, his 2024 work on context-aware utterances in conversational android robots explores how personality traits influence user preference, bridging robotics and psychology. Across his career, Okadome’s research has garnered over 70 citations, reflecting his influence in advancing robot dexterity, learning, and social interaction. His achievements highlight a commitment to building robots that not only perform tasks but also understand and adapt to human environments.
Research Focus
Key Achievements
Top Papers
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