Papers
9
Total Citations
79
H-Index
4
About
Bingtao Hu is a leading researcher at the forefront of human-robot collaboration (HRC) and intelligent manufacturing, with a particular focus on integrating human digital twins (HDT) into cyber-physical systems. His work centers on enabling robots to perceive, predict, and respond to human intentions through multimodal data fusion, deep domain adaptation, and spatiotemporal modeling. Hu’s most impactful contribution is the construction of a human digital twin model using multimodal data for locomotion mode identification (33 citations), establishing a foundational framework for human-aware robotics. He further advanced the field with a human-robot handover task intention recognition framework that fuses HDT with deep domain adaptation (17 citations), addressing a critical bottleneck in Industry 5.0. His recent work on learning human-to-robot object handover policies from 4D spatiotemporal flow (5 citations) and fine-grained motion latent diffusion for human motion prediction (3 citations) demonstrates his ongoing push toward more natural, safe, and adaptive HRC. Hu has also contributed to industrial exoskeletons for secure human-robot interaction and equipment-level digital twin methods for robotic machining. With over 80 citations across his most-cited papers, Hu is shaping the next generation of intelligent, human-centric manufacturing systems.
Research Focus
Key Achievements
Top Papers
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- 5Industrial exoskeletons for secure human–robot interaction: a review4 citations · 2024
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