Jiadong Xiao
Papers
5
Total Citations
89
H-Index
3
About
Jiadong Xiao is a pioneering researcher in robotic control systems, with a primary focus on precision manipulation and intelligent automation. His work centers on three interconnected areas: constant-force control for robotic grinding and surface tracking, time-optimal path planning, and the integration of reinforcement learning with traditional control algorithms. Xiao's most significant contribution is the development of a "press-and-release" model combined with model-based reinforcement learning for robotic constant-force grinding, which achieved 39 citations and represents a breakthrough in adaptive force control for industrial applications. He further advanced this field by introducing neural networks for angle identification in curved surface tracking, enabling robots to maintain consistent contact force on complex geometries. His research on time-optimal path tracking, which employs a numerical integration-like approach combined with iterative learning algorithms, has garnered 15 citations and addresses critical efficiency challenges in industrial robotics. Xiao's innovative use of prior knowledge to accelerate reinforcement learning for path tracking demonstrates his commitment to practical, deployable solutions. With a growing citation record and a focus on bridging simulation and real-world application, his work continues to influence the development of more intelligent, efficient, and adaptable robotic systems for manufacturing and automation.
Research Focus
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Top Papers
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