Papers
75
Total Citations
1,385
H-Index
22
About
Chenglong Fu is a versatile robotics researcher whose work spans humanoid locomotion, wearable assistive devices, point cloud learning, and autonomous robot perception. His foundational contribution to humanoid robotics — a gait synthesis and sensory control framework for stair climbing — has accumulated 122 citations since 2008, establishing him as an early authority in robust bipedal locomotion. Fu has since made significant strides in exoskeleton technology, developing a novel nonlinear series elastic actuator to enhance transparency and safety in hip exoskeletons, and pioneering supernumerary robotic limbs that assist users during load carriage and overhead tasks — collectively drawing over 100 citations. His interdisciplinary reach extends into machine learning, where his Linked Dynamic Graph CNN — cited 142 times — offers an elegant hierarchical approach to processing sparse, unordered point clouds for environmental understanding in robotics. Fu has further advanced intelligent rehabilitation, applying exoskeleton-based systems to assess stroke recovery, and tackled cross-subject locomotion intent prediction using unsupervised adaptation techniques. With over 700 cumulative citations across diverse domains, his research consistently bridges fundamental algorithmic innovation with real-world assistive and autonomous robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Gait Synthesis and Sensory Control of Stair Climbing for a Humanoid Robot122 citations · 2008
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- 4Supernumerary Robotic Limbs to Assist Human Walking With Load Carriage61 citations · 2020
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- 6Unsupervised Cross-Subject Adaptation for Predicting Human Locomotion Intent49 citations · 2020
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