Papers
4
Total Citations
41
H-Index
3
About
Daisuke Kitakoshi is a pioneering researcher at the intersection of robotics, reinforcement learning, and gerontechnology, whose work addresses Japan’s pressing super-aging society crisis. His primary research areas include preventive care systems, human-robot interaction, and adaptive machine learning algorithms. Kitakoshi’s most significant contribution lies in developing learning communication robots that engage older adults through game playing and fall-prevention activities, aiming to maintain cognitive and physical function while reducing the burden on healthcare personnel. His foundational 2015 study on evaluating basic characteristics and user adaptability of such systems (22 citations) demonstrates how reinforcement learning enables robots to personalize interactions over time. Earlier work (2013, 12 citations) established the theoretical framework for game-based preventive care, while his 2017 fall-prevention study (4 citations) extends this to physical safety. Beyond healthcare, Kitakoshi has advanced reinforcement learning for dynamic environments, proposing a policy-improving system using mixture probability and clustering distribution (2014, 3 citations) with applications for disaster-response robots. His research uniquely combines algorithmic innovation with pressing societal needs, creating socially assistive robots that learn and adapt to individual users—a critical step toward sustainable eldercare in aging populations worldwide.
Research Focus
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Top Papers
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