Papers
8
Total Citations
169
H-Index
5
About
Guoyuan Liang is a robotics and intelligent systems researcher whose work spans computer vision, robotic manipulation, exoskeleton control, and autonomous systems. He is best known for his pioneering contributions to vision-based robotic grasping, with his 2013 paper on deep learning for 3D object recognition and 6D pose estimation accumulating 76 citations — a foundational contribution that helped establish deep neural networks as a viable approach for enabling robots to perceive and interact with their environment. Liang has consistently advanced this thread, later developing manufacturing-oriented pose estimation pipelines (27 citations) and improved recognition architectures for cluttered scenes. Beyond manipulation, he has made meaningful inroads into rehabilitation robotics, proposing kernel-based methods for gait phase classification in lower limb exoskeletons (24 citations) and knowledge-tracing frameworks for continuous joint angle estimation from multi-stream biosignals (23 citations). His research portfolio also encompasses UAV localization through heterogeneous sensor fusion and reinforcement learning for dexterous robotic hands, reflecting a broad commitment to bridging perception, learning, and physical robot control across both industrial and assistive applications.
Research Focus
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