Papers
30
Total Citations
1,022
H-Index
13
About
Yong‐Jin Liu is a leading researcher at the intersection of robotics, computational fabrication, and intelligent systems, whose work has fundamentally advanced how machines perceive, plan, and create in three-dimensional space. He is perhaps best known for pioneering support-free 3D printing using robotic systems, most notably through his RoboFDM platform and subsequent multi-axis volumetric printing method, which together have garnered nearly 400 citations and redefined the flexibility of fused deposition modeling by eliminating the need for structural supports through curved, volumetrically optimized toolpaths. His contributions extend into active robotic vision, where his comprehensive survey on view planning and his deep-learning-driven Next Best View frameworks — including the point-cloud-based PC-NBV — have become key references for autonomous reconstruction tasks. Liu has also made notable strides in agricultural robotics through multi-robot plant phenotyping, and in bioprinting, developing a multi-axis system capable of preserving natural cell function for cardiac tissue fabrication. Complementing this engineering breadth, his work in pose estimation, collision avoidance, and stylized motion synthesis demonstrates a versatile command of deep learning across robotics and computer graphics. With over 860 combined citations, Liu's research consistently bridges theoretical innovation with real-world robotic application.
Research Focus
Key Achievements
Top Papers
- 1Support-free volume printing by multi-axis motion248 citations · 2018
- 2RoboFDM: A robotic system for support-free fabrication using FDM150 citations · 2017
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- 4Plant Phenotyping by Deep-Learning-Based Planner for Multi-Robots76 citations · 2019
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- 8PPR-Net++: Accurate 6-D Pose Estimation in Stacked Scenarios39 citations · 2021
- 9Autoregressive Stylized Motion Synthesis with Generative Flow39 citations · 2021
- 10Audio-Driven Stylized Gesture Generation with Flow-Based Model23 citations · 2022