Papers
2
Total Citations
27
H-Index
2
About
Yuanxiong Guo is a leading researcher at the intersection of federated learning, human-robot collaboration, and privacy-preserving artificial intelligence. His work focuses on enabling intelligent, adaptive robots that can predict human intentions in real time—particularly within collaborative assembly and construction tasks. Guo’s most notable contribution is the development of FedHIP, a federated learning framework designed for privacy-preserving human intention prediction in human-robot collaborative assembly. This work, published in 2024, has already garnered 25 citations, underscoring its timely impact on the field. Additionally, his research on multi-task deep learning for human intention prediction in construction robotics addresses the critical challenge of relieving workers from repetitive, physically demanding tasks by allowing robots to anticipate and adapt to human motion. Guo’s contributions are foundational to building safer, more efficient, and privacy-conscious collaborative environments. His work is highly relevant for researchers and students interested in embodied AI, human-robot interaction, and privacy-preserving machine learning in industrial settings.
Research Focus
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Top Papers
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