Ying-Jer Chou
Papers
2
Total Citations
38
H-Index
2
About
Ying-Jer Chou is a robotics researcher whose work centers on humanoid robot vision and autonomous navigation, particularly in dynamic, real-world environments. His most influential contributions focus on enabling humanoid robots to perform complex ball-handling tasks—specifically, the “search, track, and kick to a virtual target point” (STKVTP) sequence—using neural-network-based active embedded vision systems. In his highly cited 2013 paper (28 citations), Chou developed a system where the robot does not rely on a pre-known target location but instead estimates the virtual target point online, integrating visual search, tracking, and precise kicking into a seamless, autonomous pipeline. A follow-up study (10 citations) systematically compared two visual navigation strategies for this task, further refining the approach. Chou’s work is notable for bridging computer vision, neural networks, and real-time control in humanoid robotics, addressing the practical challenge of operating without complete environmental knowledge. His research has direct implications for robotics competitions, assistive technologies, and autonomous systems, demonstrating how embedded vision can empower robots to interact intelligently with moving objects in unstructured settings.
Research Focus
Key Achievements
Top Papers
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