Jiaqi Ye
Papers
2
Total Citations
56
H-Index
2
About
Jiaqi Ye is a pioneering researcher in intelligent transportation and robotic automation, whose work bridges advanced 3D sensing and machine learning for infrastructure inspection and sustainable manufacturing. Ye’s most influential contribution, “Use of a 3D model to improve the performance of laser-based railway track inspection” (2018, 52 citations), introduced a novel 3D reconstruction framework that dramatically enhanced defect detection accuracy in railway systems—a field traditionally reliant on 2D imaging. This work not only advanced non-destructive evaluation but also demonstrated how computer vision techniques from virtual reality and robotics could be repurposed for critical infrastructure safety. More recently, Ye has broken new ground in circular economy robotics with “Robotic Disassembly Skill Acquisition Based on Reinforcement Learning With External Knowledge Injection” (2025, 4 citations), proposing an efficient RL method that integrates domain-specific knowledge to accelerate learning for complex disassembly tasks—a key enabler for e-waste recycling and remanufacturing. By tackling the inefficiency of real-world RL training, this work promises to make autonomous disassembly economically viable. With a growing citation footprint and a clear trajectory from sensor-based inspection to intelligent robotic manipulation, Ye is shaping the future of automated maintenance and sustainable production systems.
Research Focus
Key Achievements
Top Papers
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