S. Okumura
Papers
2
Total Citations
45
H-Index
2
About
S. Okumura investigates the intersection of social cognition, human-robot interaction, and motor learning, exploring how praise from artificial agents influences skill acquisition. Their research demonstrates that social rewards—such as verbal praise—from computer-graphics-based agents and robots can enhance offline improvements in motor skills, comparable to human praise. A key finding, published in a 2020 study (39 citations), reveals that praise from two agents yields greater offline gains than a single agent, highlighting the importance of social presence in learning. Earlier work (2017, 6 citations) laid the foundation by showing that robots’ social rewards effectively boost motor skill retention. Okumura’s contributions challenge assumptions about human-machine interaction, suggesting that artificial agents can serve as effective motivators in educational and rehabilitation contexts. This work bridges robotics, psychology, and neuroscience, offering practical insights for designing socially engaging technologies. Their findings have implications for automated coaching, therapy, and skill training, where artificial agents could supplement human instructors.
Research Focus
Key Achievements
Top Papers
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