Papers
4
Total Citations
23
H-Index
2
About
Lingfeng Sun is a robotics researcher whose work spans robotic manipulation, assembly automation, and human-robot interaction. His research addresses some of the most demanding challenges in intelligent robotics, combining machine learning with physical dexterity to advance autonomous systems in real-world environments. Among his most significant contributions is a holistic multi-level reasoning framework for robotic assembly, which moves beyond conventional target segmentation and pose regression to enable flexible, blueprint-free part assembly — a breakthrough with broad implications for manufacturing, maintenance, and recycling. His work on data-efficient grasp learning, accumulating 9 citations, introduces a maximum likelihood grasp sampling loss that dramatically reduces the supervision burden typically required to train robust grasping models. Sun has also tackled the persistent sim-to-real transfer problem in contact-rich manipulation, proposing online admittance residual learning to achieve safer, more stable robot-environment interaction. More recently, his "Imagined Potential Games" framework addresses the nuanced challenge of predicting and navigating around human agents in collaborative indoor environments. With publications across 2022–2024 and a growing citation record, Sun represents an emerging voice pushing the boundaries of autonomous manipulation and interactive robotic behavior.
Research Focus
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