Papers
15
Total Citations
243
H-Index
8
About
Weiran Yao is a robotics and control systems researcher whose work spans intelligent robot control, cybersecurity for autonomous systems, and multi-robot coordination. His research is particularly distinguished by the integration of deep reinforcement learning and advanced control theory into complex robotic platforms, including cable-driven parallel robots (CDPRs), continuum soft robots, and free-floating space robots. Yao's most impactful contribution applies deep reinforcement learning to overcome the intricate cable dynamics and environmental uncertainties inherent in CDPRs, earning 60 citations and establishing him as a notable voice in data-driven robot control. Complementing this, his highly cited work on secure robot learning frameworks (51 citations) addresses the critical and often overlooked challenge of cyber resilience in intelligent robotic systems, tackling adversarial threats such as denial-of-service attacks. His research on space robotics — including trajectory optimization for capturing non-cooperative tumbling objects — further demonstrates his breadth across terrestrial and orbital applications. With contributions spanning adaptive sliding mode control, self-attention-enhanced dynamics learning, and homotopic task allocation, Yao's body of work reflects a researcher committed to bridging theoretical rigor with practical robustness. His growing citation record signals increasing recognition within the robotics and cyber-physical systems communities.
Research Focus
Key Achievements
Top Papers
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- 5Event-Triggered Secure Control Under Aperiodic DoS Attacks19 citations · 2024
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