Papers
44
Total Citations
465
H-Index
12
About
Kotaro Funakoshi is a researcher whose work sits at the intersection of human-robot interaction, spoken dialogue systems, and multimodal machine learning. His research has made significant contributions to enabling robots to communicate more naturally and learn from their environments in human-like ways. Funakoshi is perhaps best known for pioneering the concept of "Artificial Subtle Expressions" (ASEs) — intuitive audio-visual cues, such as blinking lights and tonal sounds, that convey a robot's internal states to users. This work, which has garnered over 80 combined citations, addressed a fundamental challenge in human-robot speech interaction: reducing disruptive speech overlaps and making robotic communication feel more fluid and socially aware. His research also advances multimodal object concept formation, developing systems that allow robots to autonomously acquire and integrate visual, auditory, and haptic information to form object categories and learn language — mirroring human cognitive development. His work on the MLDA-NPYLM framework demonstrates how robots can jointly learn object concepts and linguistic meaning, accumulating over 40 citations. Additionally, Funakoshi has contributed a multi-expert framework for building modular, scalable conversational agents, influencing dialogue system architecture. Collectively, his research pushes robotics toward more intuitive, grounded, and communicatively capable machines.
Research Focus
Key Achievements
Top Papers
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- 2Artificial subtle expressions43 citations · 2010
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- 8How do users interact with a pet-robot and a humanoid19 citations · 2010
- 9A framework for building conversational agents based on a multi-expert model19 citations · 2008
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