Tianbo Yang
Papers
2
Total Citations
6
H-Index
1
About
Tianbo Yang is a robotics researcher whose work bridges control theory and industrial automation, with a focus on humanoid locomotion and intelligent inspection systems. His primary research areas include model predictive control (MPC) for bipedal gait generation and computer vision for industrial quality assurance. Yang’s major contribution lies in developing flexible MPC frameworks that enable bounded, stable gait generation in humanoid robots—a critical advancement for navigating complex environments. His 2025 paper on this topic has already garnered 5 citations, reflecting its impact on the field of legged robotics. Additionally, Yang has explored human-like observation systems for industrial product inspection, proposing a universal image acquisition framework that mimics human visual attention to detect defects on complex surfaces. This work, though newer, demonstrates his versatility in applying bio-inspired principles to manufacturing challenges. Yang’s research not only advances theoretical understanding of robot locomotion but also offers practical solutions for real-world deployment, making him a promising voice in the intersection of robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 2