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About

Ming Yao is a leading researcher at the intersection of robotics, intelligent manufacturing, and energy efficiency. Their work focuses on two critical challenges: optimizing the energy consumption of industrial robots (IRs) and advancing the design of parallel and soft robotic systems. Yao’s major contributions include pioneering data-driven methods for energy evaluation and optimization, such as using ResNet-based deep learning models to predict and reduce the power consumption of industrial robots—a key step toward green manufacturing. Their research on cable-driven parallel robots (CDPRs) and bioinspired kirigami structures for soft robot anchoring demonstrates a talent for applying machine learning to solve complex mechanical problems. With over 37 citations across their most-cited works, Yao’s impact is evident in papers like “Machine Learning Applications in Parallel Robots: A Brief Review” (2025, 12 citations) and “Research on Power Modeling of the Industrial Robot Based on ResNet” (2022, 9 citations). Their notable achievements include comprehensive reviews on energy optimization for discrete manufacturing equipment, establishing a foundation for sustainable industrial practices. Yao’s work is essential reading for anyone interested in the future of energy-efficient, intelligent robotics.