Papers
1
Total Citations
12
H-Index
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About
Zining Yang is a researcher at the intersection of robotics, machine learning, and dynamical systems, with a primary focus on learning stable and robust motion representations from human demonstrations. In their highly cited 2015 work, “Neural learning of stable dynamical systems based on extreme learning machine,” Yang introduced a novel approach that leverages the extreme learning machine (ELM) framework to model human motions as autonomous dynamical systems. A key contribution of this paper is the rigorous theoretical derivation of sufficient conditions to guarantee global stability at the target point—a critical challenge in imitation learning for robotics. By ensuring that learned motions converge reliably, Yang’s method enables safer and more predictable robot behavior in real-world tasks. This work has garnered 12 citations, reflecting its influence on subsequent research in stable motion generation and neural network-based control. Yang’s contributions are particularly notable for bridging theoretical guarantees with practical learning efficiency, offering a foundation for future advances in human-robot interaction and adaptive automation.
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