Kazuaki Tanaka

Papers

Total Citations

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About

Kazuaki Tanaka is a researcher working at the intersection of human-robot interaction and machine learning, with a particular focus on how robots can effectively learn new behaviors through collaborative engagement with human partners. His work addresses a nuanced and practically significant challenge: enabling robots to intelligently integrate both reward signals and direct human instructions during the learning process, thereby minimizing the cognitive and physical burden placed on human teachers. A notable contribution from Tanaka is his 2010 study examining hesitation behaviors in robots — specifically, how strategically introduced delays or "intervals" in a robot's actions can create natural opportunities for humans to provide feedback, instructions, and evaluations. This insight reflects a sophisticated understanding that effective human-robot learning is not solely a computational problem, but a deeply interactive, socially-embedded process. While still building its citation footprint with 2 citations, this work touches on foundational questions about the design of learnable, responsive robotic systems. Tanaka's research contributes meaningfully to the broader goal of making robots more intuitive, adaptive partners in real-world environments where human guidance remains essential.