Jiayun Fu
Papers
3
Total Citations
21
H-Index
2
About
Jiayun Fu is a rising researcher in the field of robotics, with a primary focus on robot skill learning, human-robot interaction, and safe autonomous manipulation. Their work addresses a critical challenge in modern robotics: enabling robots to adapt learned skills to dynamic, real-world environments rather than static, controlled settings. Fu’s most-cited paper, “A Policy Searched-Based Optimization Algorithm for Obstacle Avoidance in Robot Manipulators” (2024, 11 citations), introduces a novel approach that allows robots to generalize learned behaviors while avoiding collisions in changing workspaces. This is complemented by their work on “Non-parametric Gaussian process movement primitive with via-point constraint for effective and safe robot skill learning” (2024, 8 citations), which enhances the safety and precision of skill transfer. Fu also contributed a comprehensive survey on learning autonomous dynamic systems for human-robot skill transfer (2025, 2 citations), providing a valuable roadmap for the field. Collectively, Fu’s research bridges the gap between demonstration-based learning and real-world deployment, making significant strides toward safer, more adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
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