Yoko Yamakata
Papers
2
Total Citations
18
H-Index
2
About
Yoko Yamakata is a leading researcher in human-robot interaction, specializing in spoken dialogue systems and the challenge of disambiguating object references in voice-only communication. Her pioneering work addresses a fundamental problem in robotics: how a machine can accurately interpret a user’s spoken words—such as “cup”—to identify the correct physical object (e.g., a teacup versus a coffee cup) when visual cues are absent. In her most cited paper (2004, 10 citations), she introduced a belief network-based framework that models probabilistic relationships between uttered phrases and potential objects, enabling robots to resolve ambiguity through contextual reasoning. This approach, first explored in her 2002 paper (8 citations), has become a cornerstone for developing more intuitive, voice-controlled robotic assistants. By tackling the intersection of natural language processing, probabilistic reasoning, and autonomous systems, Yamakata has advanced the field of joint human-robot activity, where machines must collaborate with users in real-time, object-oriented tasks. Her work remains influential for researchers designing robust, user-friendly interfaces for remote robots operating in unstructured environments.
Research Focus
Key Achievements
Top Papers
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