Papers
14
Total Citations
77
H-Index
5
About
Shunsuke Ota is a robotics researcher whose work spans two compelling and complementary domains: autonomous robot navigation using computer vision and deep learning, and human-robot physical interaction. His most influential contribution is a vision-based robot navigation framework that leverages deep learning — specifically Deep Belief Neural Networks — to detect drivable road areas in outdoor and urban environments using a single, cost-effective camera. This work, which has garnered up to 14 citations, addresses real-world challenges such as illumination variability and unstructured environments, with practical applications in autonomous wheelchair systems for aging populations. Equally distinctive is Ota's sustained research into socially aware robotics, particularly handshake and hug interaction systems. Beginning in 2012, he developed motion models that enable robots to generate emotionally acceptable greeting behaviors — including handshake request and response motions — during active human approach. This body of work reflects a nuanced understanding of embodied human communication and has accumulated citations across multiple publications. His 2016 study on hug behavior generation further extends this vision into affective physical interaction. With contributions spanning navigation, human-robot interaction, and low-cost sensor systems for pipeline inspection, Ota represents a versatile robotics researcher committed to building machines that operate safely and naturally alongside humans.
Research Focus
Key Achievements
Top Papers
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- 4A handshake response motion model during active approach to a human6 citations · 2014
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