Yusuke Hatae
Papers
2
Total Citations
12
H-Index
2
About
Yusuke Hatae is a robotics researcher whose work sits at the intersection of autonomous navigation, human-robot interaction, and anomaly detection. His most influential contributions focus on enabling mobile robots to intelligently monitor human activities and identify unusual behaviors in real-world environments. In his highly cited 2021 paper, "Detecting Anomalies from Human Activities by an Autonomous Mobile Robot based on 'Fast and Slow' Thinking," Hatae introduced a novel cognitive architecture inspired by Kahneman’s dual-process theory, allowing robots to efficiently balance rapid, reactive responses with deeper, deliberative reasoning for anomaly detection. This work, along with his 2020 study on "Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring," has collectively garnered over a dozen citations, establishing him as a rising voice in applied machine learning for robotics. Hatae’s research is particularly notable for its practical, experimental rigor—testing algorithms on actual office monitoring tasks rather than relying solely on simulations. His achievements demonstrate a clear commitment to bridging theoretical AI models with deployable robotic systems, making his work highly relevant for students and researchers interested in autonomous surveillance, cognitive robotics, and real-world anomaly detection.
Research Focus
Key Achievements
Top Papers
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