Manabu Gouko
Papers
17
Total Citations
73
H-Index
5
About
Manabu Gouko is a researcher at the intersection of robotics, reinforcement learning, and human-robot interaction. His work focuses on developing intelligent systems that can autonomously acquire exploratory behaviors and generate complex actions through motor primitive modules and time-series prediction. Gouko’s foundational contributions include an action generation model that sequentially switches motor primitives based on sensory input, enabling robots to navigate and adapt in dynamic environments. His research on online exploratory behavior acquisition, grounded in reinforcement learning, has garnered over 60 citations, with his most-cited paper (2014) receiving 11 citations. Notably, Gouko has pioneered a novel line of "encouragement robotics," creating stationery holder and coaster robots that nudge office workers to tidy desks and stay hydrated—blending technical innovation with behavioral psychology. His work on epsilon-greedy babbling and discernment behavior reinforcement learning further advances efficient motor learning and object categorization. Through these contributions, Gouko demonstrates how robots can not only learn autonomously but also positively influence human habits in everyday settings.
Research Focus
Key Achievements
Top Papers
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- 6Epsilon-greedy babbling5 citations · 2017
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- 8An Action Generation Model Using Time Series Prediction4 citations · 2007
- 9A Coaster Robot that Encourages Office Workers to Drink Water4 citations · 2017
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