Papers
66
Total Citations
1,238
H-Index
20
About
Zhenshan Bing is a robotics and artificial intelligence researcher whose work sits at the dynamic intersection of bio-inspired computing, robot learning, and motion control. He has made significant contributions to the field of spiking neural networks (SNNs), most notably through his highly cited 2018 survey on robotics control using learning-inspired SNNs (180 citations), which established a foundational reference for researchers exploring biologically plausible alternatives to conventional deep learning. His work demonstrates how spike-based computation can enable energy-efficient, neuromorphic approaches to real-world robotic tasks, including autonomous lane-keeping vehicles trained end-to-end using reward-modulated plasticity rules. Beyond neuromorphic computing, Bing has advanced snake-like robot locomotion through CPG-based gait control and reinforcement learning-driven energy-efficient motion strategies. His more recent research addresses meta-reinforcement learning in dynamic environments, sparse-reward robotic manipulation, and adaptive balance control for wheel-bipedal robots, reflecting a broadening research vision. With over 600 cumulative citations across his top works, Bing's research has meaningfully shaped discussions around sample-efficient robot learning, bio-inspired control, and intelligent autonomous systems, making his profile essential reading for students entering neurorobotics or embodied AI.
Research Focus
Key Achievements
Top Papers
- 1A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks180 citations · 2018
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- 3Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments63 citations · 2022
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- 9Motion planning for robotics: A review for sampling-based planners41 citations · 2025
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