Kohei Nomoto
Papers
2
Total Citations
27
H-Index
2
About
Kohei Nomoto is a robotics researcher whose work focuses on human-robot interaction and reinforcement learning. His most notable contribution is a pioneering study on "Fuzzy Inference based Mentality Expression for Eye Robot in Affinity Pleasure-Arousal Space" (2008, 25 citations), which proposed a system enabling household robots to express emotional states through eye movements. By mapping mentality to an affinity pleasure-arousal space and using fuzzy inference to interpret speech categories, Nomoto created a framework for more natural, empathetic communication between humans and robots. This work addresses a key challenge in social robotics: making machines appear emotionally aware. In reinforcement learning, Nomoto introduced "Eligibility Propagation to Speed up Time Hopping" (2009, 2 citations), a mechanism that enhances simulation efficiency by propagating eligibility traces through time, analogous to traditional eligibility traces in RL. While his citation counts are modest, Nomoto’s research sits at the intersection of affective computing and machine learning, contributing to the development of robots that can both learn faster and express themselves more intuitively. His work remains relevant for researchers exploring emotionally expressive autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2