Papers
5
Total Citations
110
H-Index
4
About
Yi-Zeng Hsieh is a leading researcher at the intersection of robotics, artificial intelligence, and educational technology, with a focus on developing intelligent robotic systems for assistive and learning applications. His pioneering work integrates anticipatory computing, emotional big data, and deep learning to enhance human-robot interaction and sustainable learning. Notably, his most cited paper (56 citations) introduces ARCS-assisted teaching robots that leverage emotional big data and anticipatory computing to boost learning motivation and efficiency. Hsieh has made significant contributions to assistive robotics, including a stereo vision robotic arm system using Q-learning optimization (31 citations) to aid people with disabilities, and a deep convolutional generative adversarial network for inverse kinematics (18 citations) enabling self-assembly robotic arms. He also developed RobotTell, a robot-based learning companion using user-centered design and computer vision. His recent work on deep learning-based assistance for visually impaired individuals in indoor environments further demonstrates his commitment to inclusive technology. With a growing citation impact, Hsieh’s research continues to push boundaries in creating adaptive, intelligent robotic systems that improve quality of life and educational outcomes.
Research Focus
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