Kentaro Oguchi

Toyota Motor Corporation (United States)

Papers

Total Citations

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About

Kentaro Oguchi is a researcher whose work sits at the intersection of human-robot interaction and service robotics, with a focused expertise on the nuanced dynamics of object handovers. His primary research area investigates how robots can move beyond simple, repetitive actions to become more intuitive and responsive assistants. Oguchi’s major contribution lies in systematically exploring and modeling user preferences during robot-human handovers. His foundational 2017 paper, "Learning user preferences for robot-human handovers," which has garnered 12 citations, challenges the assumption that a one-size-fits-all handover strategy is sufficient. Instead, he demonstrates that robots must learn and adapt their grip, orientation, and timing based on individual user needs. In a related 2017 work, "Towards understanding user preferences in robot-human handovers: How do we decide?" (6 citations), he further dissects the decision-making process behind these preferences. By proving that user satisfaction is deeply tied to personalized interaction, Oguchi has laid critical groundwork for developing service robots that are not just functional, but genuinely considerate and effective partners in daily tasks.