Harumo Sasatake
Papers
4
Total Citations
14
H-Index
2
About
Harumo Sasatake is a robotics researcher focused on addressing labor shortages in aging societies through intelligent robot manipulation. Her primary research areas include imitation learning, tool manipulation, and robotic control systems for manufacturing and domestic tasks. Sasatake’s major contribution lies in developing deep imitation learning frameworks that enable robots to acquire complex tool-use skills—such as broom handling and cleaning operations—using remarkably small training datasets, overcoming a key limitation of traditional deep learning approaches. Her 2021 paper on imitation learning system design for flexible tool manipulation (8 citations) demonstrates how robots can learn from minimal human demonstrations, while her work on high-speed imitation learning for cleaning tools (2 citations) advances practical deployment. Notably, Sasatake also addresses industrial challenges with her research on high-accuracy removal processing of convex metal parts, integrating feedback control systems for precision manufacturing. Her cumulative work, spanning 14 citations across four key publications, establishes her as an emerging voice in efficient, data-sparse robot learning—a critical step toward making robotic assistants viable in real-world environments with limited training resources.
Research Focus
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Top Papers
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