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About

Yuichi Nagao is a researcher advancing the field of multimodal perception for human-robot interaction, with a focus on audio-visual sensing and object property estimation. His work addresses a critical challenge in robotics: enabling safe and intuitive human-to-robot handovers by contactlessly estimating the physical properties of containers and their contents. Nagao’s most-cited paper, "Audio-Visual Hybrid Approach for Filling Mass Estimation" (2021, 10 citations), introduces a novel method that combines auditory and visual cues to infer the mass of liquid or granular materials inside opaque or transparent containers—a task complicated by material variability, shape diversity, and visual occlusions. Expanding on this, his co-authored "The CORSMAL Benchmark for the Prediction of the Properties of Containers" (2022, 9 citations) provides a standardized framework for evaluating algorithms that estimate container weight, fill level, and type, serving as a key resource for the robotics community. By tackling the complexities of real-world object manipulation, Nagao’s contributions are paving the way for more adaptive and reliable robotic systems in domestic and industrial settings.