Fanrong Li
Papers
2
Total Citations
56
H-Index
2
About
Fanrong Li is a leading researcher in robotic perception and manipulation, with a primary focus on enabling robots to handle deformable objects with human-like dexterity. Their key research areas include visual-tactile fusion perception, grasp state assessment, and intelligent robotic control. Li’s most notable contribution is the development of a novel 3D convolution-based deep neural network that integrates visual and tactile data to assess the grasp state of deformable objects—a task that remains a significant challenge in robotics. This work, published in 2020, has garnered over 54 citations, underscoring its impact on advancing safe and precise robotic interactions. By mimicking the human ability to use both sight and touch to determine appropriate grasping forces, Li’s research directly addresses the critical problem of preventing object slippage or excessive deformation during manipulation. This achievement not only enhances the reliability of robotic systems in handling soft materials but also paves the way for applications in manufacturing, healthcare, and service robotics. Li’s work stands as a cornerstone in the field, inspiring further innovations in multimodal sensory integration for autonomous systems.
Research Focus
Key Achievements
Top Papers
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