Shilong Sun
Papers
8
Total Citations
67
H-Index
4
About
Shilong Sun is a rising researcher at the intersection of robotics, dynamics, and intelligent fault diagnosis. His work centers on two critical challenges: ensuring the reliability of harmonic reducers—the precision components essential to industrial robots—and advancing the locomotion control of humanoid robots. Sun’s most impactful contributions include developing novel fault diagnosis methods for flexible thin-walled elliptical bearings, where his 2024 paper has already garnered 20 citations. He has pioneered innovative approaches such as a Self-Constructed Graph Fault Feature Extractor (SCG-GFFE) and a hybrid MCVAE-GAN model to address the scarcity and diversity of real-world fault signals. In parallel, Sun is making strides in humanoid robotics by fusing dynamics control with reinforcement learning to achieve precise, robust gait planning. Notably, he is exploring the use of large language models for comprehensive locomotion control, a forward-looking approach that has attracted 14 citations. His recent work also tackles data privacy in industrial settings through swarm learning-based diagnostic algorithms. With over 65 total citations across his 2024-2025 publications, Sun is establishing himself as a versatile engineer bridging mechanical reliability and intelligent control.
Research Focus
Key Achievements
Top Papers
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