Papers
9
Total Citations
178
H-Index
5
About
Shaokun Jin is a robotics and autonomous systems researcher whose work spans robot learning, simultaneous localization and mapping (SLAM), and intelligent control. His research is particularly distinguished by contributions to learning from demonstration (LfD), where he has developed methods enabling robots to acquire accurate, stable motion strategies directly from human examples rather than relying on traditional preprogramming. Jin's most cited work, a 2019 SLAM framework integrating low-cost LiDAR with vision sensing for 2.5D map building (72 citations), addresses the practical challenge of deploying affordable sensors in consumer robotics without sacrificing navigation reliability. Equally influential is his 2017 paper on Extreme Learning Machine-based dynamical systems (71 citations), which significantly accelerated the speed and stability of robot motion learning — a critical bottleneck in real-world deployment. Beyond motion learning, Jin has explored object pose estimation using convolutional neural networks, monocular depth estimation of deformable objects, and real-world applications such as automated cavity filter tuning guided by human expertise. His work on manifold immersion and submersion further refines the mathematical rigor underlying stable dynamical systems. Collectively, Jin's research bridges theoretical machine learning with tangible robotics applications, offering solutions that are both computationally efficient and practically deployable in unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 6Depth Estimation of a Deformable Object via a Monocular Camera3 citations · 2019
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