Papers
9
Total Citations
60
H-Index
5
About
Fengjun Mu is a pioneering researcher at the intersection of human-robot interaction, rehabilitation robotics, and computer vision. His work centers on developing intelligent, multimodal interfaces that enable seamless communication between humans and robotic systems, particularly for assistive and rehabilitation applications. Mu’s major contributions include the creation of novel human-exoskeleton interfaces that fuse EEG and sEMG signals to predict limb movement intent, significantly improving the reliability and naturalness of control for rehabilitation training. His MCSNet framework, leveraging channel synergy in sEMG, has garnered attention for advancing intuitive exoskeleton control. In parallel, Mu has made notable strides in 6D object pose estimation for robotic grasping, introducing temporal motion reasoning and weakly supervised learning methods (e.g., Weak6D) to enhance robot perception in cluttered, occluded scenes. His work on optimization-based adaptive assistance and terrain-adaptive gait planning for lower limb exoskeletons addresses critical challenges in real-world mobility support. With papers accumulating over 60 citations, Mu’s research is shaping the future of assistive robotics, combining neural signal processing, computer vision, and adaptive control to create more responsive, intelligent robotic systems for human augmentation and rehabilitation.
Research Focus
Key Achievements
Top Papers
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