Juntong Yun
Papers
15
Total Citations
463
H-Index
8
About
Dr. Juntong Yun is a leading researcher in robotics and intelligent perception, whose work bridges computer vision, deep learning, and robotic manipulation. His core contributions center on enabling robots to perceive and interact with complex, unstructured environments—from cluttered indoor spaces to stacking scenarios. Dr. Yun’s most influential work, “Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection” (128 citations), tackles the critical challenge of detecting small objects in variable indoor settings, directly enhancing robotic interaction capabilities. He has also made significant advances in robotic control, notably with “A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm” (78 citations), which provides efficient solutions for complex manipulator motion planning. His research on grasp planning is equally impactful: “Grasping posture of humanoid manipulator based on target shape analysis and force closure” (75 citations) and “Enhancement of real‑time grasp detection by cascaded deep convolutional neural networks” (56 citations) have set benchmarks for reliable, real-time grasping in unstructured environments. With over 450 total citations across his top papers, Dr. Yun’s work is foundational for the next generation of autonomous robots, from service robots to industrial manipulators, and continues to shape how machines see, plan, and act in the real world.
Research Focus
Key Achievements
Top Papers
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- 6Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam32 citations · 2022
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