D. Suwimonteerabuth

Papers

Total Citations

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About

D. Suwimonteerabuth’s research centers on interactive robot learning, with a particular focus on enabling mobile robots to adapt their behavior through real-time human feedback. Their most notable contribution, presented in the 2003 paper “Online robot learning by reward and punishment for a mobile robot,” introduced a flexible learning framework where a human observer can directly influence a robot’s actions using reward and punishment signals, bypassing the need for predefined goals. This work, which has garnered 5 citations, represents an early and practical step toward more intuitive human-robot interaction, allowing robots to learn socially in dynamic environments. Suwimonteerabuth’s approach stands out for its emphasis on real-time, online adaptation, making it relevant for applications in assistive robotics and autonomous systems where rigid goal-setting is impractical. While their citation count is modest, the work’s conceptual contribution to interactive machine learning and behavior shaping remains a valuable reference for researchers exploring human-in-the-loop training paradigms.