About

Guang Yang is a robotics researcher whose work spans autonomous navigation, care robotics, and intelligent systems for both healthcare and construction environments. His research addresses two interconnected challenges: enabling robots to operate effectively in complex, dynamic physical spaces, and empowering them to anticipate and fulfill human needs proactively. Yang's most impactful contributions lie in desire-driven reasoning for personal care robots, where he pioneered methods allowing robots to interpret abstract physiological needs—such as hunger or discomfort—rather than waiting for explicit commands, a particularly significant advance for patients with communication disorders. This work has accumulated over 20 citations and has evolved into a broader proactive care architecture. Alongside this, his hallway exploration-inspired guidance for autonomous material transport in construction sites (20 citations) demonstrates his versatility, translating navigation intelligence into industrial applications. His broader portfolio encompasses heuristic path planning for substation inspection robots, deep reinforcement learning-based crowd navigation, and monocular visual navigation via deep learning. Together, these contributions reflect a sustained commitment to making robots genuinely useful in messy, unpredictable real-world environments—whether assisting bedridden patients or autonomously docking carts on busy construction sites.

Research Focus

Key Achievements

4
H-Index
14
Papers
79
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
Hallway exploration-inspired guidance: applications in autonomous material transportation in construction sites
20 citations · 2021
📈 Most Prolific Year: 2021 (4 Papers)
🤝 Key Collaborators: 28
🏛 Institutions: Kochi University of Technology, Southwest Jiaotong University, Osaka Institute of Technology, Stevens Institute of Technology, Xi’an University of Posts and Telecommunications, Pingdingshan University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago