Kaiyang Wu
Papers
2
Total Citations
13
H-Index
2
About
Kaiyang Wu is a robotics and agricultural automation researcher whose work centers on intelligent manipulation and precision sensing for sustainable agriculture and renewable energy. His primary research areas include robotic grasping optimization, reinforcement learning-based control, and defect detection in photovoltaic systems. Wu’s most notable contribution is the development of a variable impedance control strategy for apple-picking robot end-effectors, integrating deep deterministic policy gradient (DDPG) reinforcement learning to minimize mechanical damage during fruit grasping. This work, published in 2025 and already garnering 10 citations, addresses a critical challenge in agricultural robotics by optimizing minimum stable grasping forces while maintaining adaptive compliance. Additionally, Wu has advanced surface defect and contamination detection in photovoltaic panels using few-shot data augmentation techniques, a method that significantly improves inspection efficiency with limited training data. His research demonstrates a dual focus on enhancing both food production and renewable energy infrastructure through intelligent automation. With his innovative application of reinforcement learning to real-world grasping problems, Wu is establishing himself as an emerging leader in the intersection of soft robotics and precision agriculture.
Research Focus
Key Achievements
Top Papers
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