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Tetsuji Ogawa is a pioneering researcher in robot audition and multi-modal perception for humanoid robotics. His work centers on enabling robots to navigate and interact in real-world environments through advanced auditory and visual processing. Ogawa’s major contributions include developing noise reduction methods using compact, head-mounted microphone arrays—such as his four-line ultra-miniature MEMS microphone system—which allow robots to isolate a conversational partner’s speech amidst background noise and multiple simultaneous utterances. He also proposed the zonal beamformer, a technique that adaptively enhances sound from a moving speaker. In the visual domain, his survey on vision-based SLAM for humanoid robots (24 citations) provides a foundational framework for mapping unknown environments using camera sensors. Ogawa’s work has been instrumental in making hands-free spoken dialogue systems feasible for autonomous mobile robots, bridging the gap between auditory perception and robust human-robot interaction. His research, though niche, has garnered steady recognition, with his most cited papers accumulating over 39 citations, reflecting its lasting relevance in the fields of robot audition and multi-sensor integration.