Papers
15
Total Citations
143
H-Index
6
About
Dingkui Tian is a robotics researcher whose work spans humanoid robot motion planning, legged locomotion, and rehabilitation exoskeleton systems — fields that sit at the exciting intersection of control theory, biomechanics, and artificial intelligence. Tian has made notable contributions to deep reinforcement learning-based motion control, including a twin synchro-control framework for multitasking humanoid robot arms that integrates digital twin technology, a concept closely aligned with Industry 4.0 paradigms. His research on biped walking employs sophisticated optimization techniques such as hierarchical quadratic programming and spring-loaded inverted pendulum models, with further work addressing disturbance rejection and highly dynamic behaviors like vertical jumping. More recently, Tian has directed significant effort toward rehabilitation robotics, developing AutoLEE-II, a self-balancing lower limb exoskeleton enabling crutch-free multi-movement rehabilitation, alongside biomimetic viscoelastic compliance controllers and EMG-driven intent recognition frameworks that personalize human-robot interaction. With over 120 cumulative citations across his most recognized publications, Tian's research demonstrates a clear trajectory from fundamental locomotion control toward clinically meaningful assistive technologies, making his work increasingly relevant to both the robotics research community and the rehabilitation engineering field.
Research Focus
Key Achievements
Top Papers
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- 5Vertical Jumping for Legged Robot Based on Quadratic Programming9 citations · 2021
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