Papers
50
Total Citations
2,097
H-Index
23
About
Yingbai Hu is a prominent robotics and intelligent control researcher whose work spans robotic manipulation, human-robot interaction, mobile robotics, and rehabilitation engineering. With a body of research accumulating hundreds of citations, Hu has made significant contributions to advancing how robots learn, move, and interact safely with humans and their environments. Among his most influential contributions is his development of reinforcement learning strategies for humanoid-like mobile manipulators, leveraging Dynamical Movement Primitives to enable robots to acquire complex manipulation and grasping skills — a paper that has garnered nearly 200 citations. His work on recurrent neural network-based manipulator control with remote center of motion constraints (205 citations) and fuzzy-torque approximation-enhanced sliding mode control for mobile robot stability (196 citations) demonstrate his expertise in combining advanced machine learning with robust control theory. Hu has also pioneered incremental learning frameworks for anthropomorphic manipulators and sensory-motor fusion systems integrating vision and electromyography for robotic hand-eye coordination. Notably, his timely work on COVID-19 oropharyngeal swab sampling robots showcases his ability to translate research into real-world medical applications. His contributions to EMG-driven neuromusculoskeletal modeling further bridge robotics and rehabilitation, reflecting a genuinely interdisciplinary research vision.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10