Jingkang Xia
Papers
14
Total Citations
150
H-Index
8
About
Jingkang Xia is a robotics researcher whose work sits at the intersection of iterative learning control, physical human-robot interaction, and autonomous path learning. With a growing body of highly cited publications, Xia has established himself as a significant contributor to the field of collaborative robotics, accumulating over 137 citations across his key works. His most influential contribution, "Spatial Iterative Learning Control for Robotic Path Learning" (2022, 35 citations), introduced the sILC framework, enabling robots to autonomously learn and adapt desired paths through environmental interaction — a landmark advance for robots operating in uncertain conditions. Complementary work extended this framework to incorporate human guidance and visual detection, bridging demonstration-based programming with real-time adaptability. Xia's research consistently addresses a core challenge in human-robot collaboration: enabling robots to proactively infer and respond to human intent. His period-varying ILC schemes, force/motion observer mechanisms, and Adam-optimization-enhanced path learning methods reflect a systematic effort to make robots safer, smarter, and more intuitive partners. More recently, his work on bilateral rehabilitation robots and industrial polishing demonstrates meaningful translation of these principles into clinical and manufacturing contexts, highlighting both the breadth and real-world relevance of his research agenda.
Research Focus
Key Achievements
Top Papers
- 1Spatial iterative learning control for robotic path learning35 citations · 2022
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