Papers
87
Total Citations
1,166
H-Index
18
About
Ruifeng Li is a prominent robotics researcher whose work spans physical human-robot interaction, motion planning, and autonomous robot perception. His most influential contribution lies in the domain of human-robot skill transfer, particularly leveraging electromyography (EMG) signals to extract and replicate human impedance and adaptive motor behaviors in robotic systems. His 2017 paper on interface design for physical human-robot interaction has accumulated over 220 citations, establishing him as a leading voice in bridging human neuromuscular dynamics with robotic control. This work builds on earlier foundational research, including adaptive impedance control implementations on the Baxter robot and EMG-based writing skill transfer to robotic platforms. Beyond human-robot interaction, Li has made meaningful contributions to mobile robot navigation, developing multi-obstacle path planning algorithms and deep learning-driven visual semantic navigation frameworks. His research also extends to 3D object detection for indoor robot perception and time-optimal trajectory planning for industrial manipulators. His body of work reflects a cohesive vision: creating robots that move intelligently, interact naturally with humans, and perceive complex environments reliably. With a cumulative citation record exceeding 550 across his top papers, Li's research continues to shape next-generation collaborative and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-obstacle path planning and optimization for mobile robot83 citations · 2021
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- 5SVM based simultaneous hand movements classification using sEMG signals32 citations · 2017
- 6Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots31 citations · 2018
- 7
- 8An Augmented Discrete‐Time Approach for Human‐Robot Collaboration29 citations · 2016
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