Zhuangyi Jiang
Papers
9
Total Citations
189
H-Index
8
About
Zhuangyi Jiang is a pioneering researcher at the intersection of neuromorphic computing, bio-inspired robotics, and brain-inspired learning systems. His work centers on harnessing spiking neural networks (SNNs) and biologically plausible learning rules — particularly Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) — to enable autonomous locomotion and intelligent perception in snake-like robots. Jiang's most influential contribution, cited 41 times, demonstrated that SNNs could achieve supervised learning for real-world vehicle control tasks, advancing the field of energy-efficient, biologically realistic machine intelligence. Building on this foundation, he pioneered end-to-end SNN architectures for target tracking in snake robots and developed neurorobotic platforms that integrate hardware and software systems inspired by living organisms — work that has collectively attracted nearly 190 citations across his portfolio. His research addresses some of the most demanding challenges in robotics: coordinating complex multi-joint locomotion, coupling visual perception with movement control, and designing energy-efficient gaits through reinforcement learning. By incorporating neuromorphic vision sensors and retina-inspired processing, Jiang pushes the boundaries of what autonomous, brain-inspired robots can achieve in unstructured environments — making his work essential reading for researchers in neurorobotics and embodied AI.
Research Focus
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Top Papers
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