Papers
4
Total Citations
58
H-Index
3
About
Yuki Kato is a researcher whose work spans autonomous robotics, deep reinforcement learning, and human-robot interaction. Best known for pioneering contributions to mobile robot navigation, Kato's most influential work — "Autonomous robot navigation system with learning based on deep Q-network and topological maps" (2017, 36 citations) — introduced an innovative hybrid framework combining deep reinforcement learning for local navigation with topological maps for global path planning, specifically designed to handle dynamic human traffic environments. Building on this foundation, Kato extended the approach to outdoor settings in 2019, developing a grid-map-free navigation system leveraging RTK-GNSS localization and Double Deep Q-Networks, further demonstrating the versatility of reinforcement learning in real-world robotic deployment. Kato's research curiosity extends beyond navigation into social robotics, exploring how a massage robot's motion patterns and hand shapes influence human social perception — work that underscores a commitment to designing robots that interact naturally with people. A notable outlier in Kato's portfolio is a 2023 clinical study on robot-assisted mitral valve repair, suggesting collaborative or interdisciplinary engagement with surgical robotics. Collectively, Kato's contributions reflect a forward-thinking researcher dedicated to bridging intelligent autonomous systems with meaningful human-centered applications.
Research Focus
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