Papers
1
Total Citations
4
H-Index
1
About
Weihang Peng is a rising researcher at the forefront of bio-inspired robotics and artificial intelligence, with a focus on autonomous navigation in unknown environments. His work uniquely integrates spiking neural networks (SNNs) with reinforcement learning, drawing inspiration from the neural mechanisms animals use to represent self-motion and external cues. Peng’s most cited paper, “Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method” (2025), proposes a novel asymptotic gradient method that significantly improves the accuracy and generalization of autonomous navigation systems. This contribution addresses a critical bottleneck in robotics—how to achieve robust, adaptive movement without prior environmental maps. Though early in his career, Peng’s work has already garnered attention (4 citations), signaling its potential impact on fields ranging from autonomous vehicles to neuromorphic computing. His research bridges computational neuroscience and practical engineering, offering a pathway toward more efficient, animal-like navigation in machines. For students and researchers, Peng’s approach exemplifies how interdisciplinary thinking can solve real-world challenges in AI and robotics.
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Top Papers
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