Xiaohui Jia
Papers
2
Total Citations
13
H-Index
1
About
Xiaohui Jia is a robotics researcher focused on advancing robot manipulation through learning and adaptive control. Their work primarily addresses two critical challenges in industrial robotics: trajectory planning and precision assembly. Jia’s most-cited paper, “A trajectory planning method for robotic arms based on improved dynamic motion primitives” (2024, 12 citations), introduces a novel approach that leverages trajectory learning and generalization to overcome the limitations of traditional methods, which often suffer from poor adaptability and low generalization performance. This work is foundational for enabling robots to handle varied tasks without extensive reprogramming. Building on this, Jia’s 2025 paper on “A robotic peg-in-hole assembly method based on demonstration learning and adaptive impedance control” (1 citation) tackles the complex problem of high-precision assembly. By combining demonstration learning with adaptive impedance control, this method simplifies modeling and enhances a robot’s ability to adapt to environmental changes—a key hurdle in automated manufacturing. Though early in their career, Jia’s research is carving a clear path toward more flexible, intelligent robotic systems, with significant potential impact on industrial automation and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2