Hideki Deguchi

Toyota Central Research and Development Laboratories (Japan)

Papers

Total Citations

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About

Hideki Deguchi is a rising researcher at the intersection of robotics, natural language processing, and human-robot interaction. His work focuses on a critical challenge: enabling robots to navigate complex, real-world environments by understanding and executing human instructions. Deguchi’s major contributions lie in developing novel frameworks that move beyond traditional visual-language navigation (VLN). He pioneered the use of "geometric instruction" and "human pointing" to resolve the inherent ambiguities of natural language, creating more robust and intuitive human-robot communication. His 2022 paper on "Fast Bayesian graph update for SLAM" (5 citations) addresses the foundational need for precise robot localization, while his 2023 work on "Enhanced Robot Navigation with Human Geometric Instruction" (4 citations) directly tackles the problem of ambiguous verbal commands. Most notably, his 2024 paper "Language to Map" (3 citations) proposes a groundbreaking method to generate topological maps directly from natural language path descriptions, a significant step toward truly autonomous and communicative robots. Though early in his career, Deguchi’s innovative, multi-modal approach to robot navigation is establishing him as a key voice in the field.