Yosuke Kawasaki
Papers
8
Total Citations
33
H-Index
4
About
Yosuke Kawasaki is a robotics researcher whose work sits at the intersection of human-robot interaction, autonomous navigation, and task planning for intelligent service robots. His research addresses one of the field's most demanding challenges: enabling robots to understand and execute complex, real-world instructions in dynamic human environments. Kawasaki's most recognized contribution — his work on Switching Head-Tail Funnel UNITER for dual referring expression comprehension (2023, 10 citations) — tackles the sophisticated problem of domestic service robots interpreting free-form natural language to fetch and deliver everyday objects, bridging vision-language understanding with physical manipulation. Complementing this, his ASTRON framework (2021) advances spatio-temporal robot navigation through action-based planning, while his ProTAMP system (2022) addresses probabilistic task and motion planning in collaborative human-robot environments. His earlier work on multimodal potential fields for autonomous navigation (2018) and action modeling through spatial factorization (2021) demonstrates a consistent commitment to grounding robot behavior in real-world, human-centered scenarios. His most recent research on onboard semantic mapping (2024) further extends robots' capacity for scene understanding and autonomous task planning. Across his career, Kawasaki has built a coherent and growing body of work pushing domestic service robots closer to genuine real-world utility.
Research Focus
Key Achievements
Top Papers
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- 3ASTRON: Action-Based Spatio-Temporal Robot Navigation5 citations · 2021
- 4Bottom-up action modeling via spatial factorization for serving food4 citations · 2021
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- 8Onboard Semantic Mapping for Action Graph Estimation1 citations · 2024