Junwei Fang

China Jiliang University, Osys Technology

Papers

Total Citations

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About

Junwei Fang is a researcher at the forefront of intelligent robotics and automation, with a focus on industrial robot dynamics, bio-inspired control, and large-scale warehouse automation. His work bridges the gap between theoretical modeling and practical deployment, particularly in force-free control for robot teaching, where his 2023 paper on step-by-step identification of industrial robot dynamics parameters has garnered 5 citations for its practical utility in simplifying human-robot interaction. Fang’s exploration of Central Pattern Generator (CPG)-based control for bionic robots, detailed in his 2025 review, highlights his commitment to bio-inspired methods that offer robust, real-time motion control without reliance on precise models—a promising avenue for agile, adaptive robots. Most recently, his HAC-FRL framework introduces a learning-driven approach to distributed task allocation in warehouse automation, achieving 1 citation for its potential to optimize efficiency in complex logistics. Fang’s contributions are notable for their cross-disciplinary impact, combining dynamics, control theory, and reinforcement learning to advance both industrial and biomimetic robotics. His work is essential reading for researchers seeking scalable, real-world solutions in robot control and automation.