Meijie Huo

North China University of Science and Technology

Papers

Total Citations

H-Index

About

Meijie Huo’s research lies at the intersection of robotics, bionic intelligence, and reinforcement learning, with a focus on enabling autonomous systems to navigate and balance with greater efficiency. In their most cited work, Huo introduced a bionic learning algorithm for two-wheeled robot balance control, combining a growing cell structure (GCS) network with Q-learning—an approach that mimics biological self-organization to improve stability and adaptability. This work, with 6 citations, laid a foundation for applying neural plasticity principles to robotic control. Huo further advanced the field by proposing the Q-ELM algorithm, which integrates Q-learning with extreme learning machines to address the high-dimensional, slow-training challenges of BP neural networks in mobile robot path planning. This innovation, cited 4 times, offers a faster, more efficient alternative for real-time decision-making. Through these contributions, Huo has demonstrated a sustained commitment to bridging computational learning theory and practical robotics, producing algorithms that are both biologically inspired and computationally lean. Their work continues to influence researchers exploring lightweight, adaptive control systems for autonomous robots.