Pin-Chu Yang
Papers
8
Total Citations
304
H-Index
5
About
Pin-Chu Yang is a pioneering roboticist whose research lies at the intersection of deep learning, humanoid robotics, and human-robot interaction. Yang’s major contributions center on enabling robots to perform complex, multi-step manipulation tasks—such as folding and put-in-box operations—by leveraging deep neural networks for task learning and execution. Their most cited work, “Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning” (2016, 235 citations), demonstrates a practical, machine-learning-based approach that allows humanoid robots to function as reliable production line workers. Yang has also advanced the field by exploring context-dependent trajectory generation for service tasks like toilet cleaning and by developing HATSUKI, an anime-character-inspired robot platform that combines stylized expressions with imitation learning to bridge otaku culture and robotics. More recently, Yang has investigated the use of large language models for parameter adjustment in physical care tasks. With a portfolio of highly cited papers and a creative vision that merges technical rigor with cultural relevance, Pin-Chu Yang is shaping the future of adaptable, user-friendly humanoid robots.
Research Focus
Key Achievements
Top Papers
- 1Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning235 citations · 2016
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