Papers
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About
Qi Tan is a leading researcher in the fields of human-robot collaboration, intelligent control systems, and deep reinforcement learning, with a particular focus on assistive exoskeleton technologies. Their most notable contribution is the development of a hierarchical decision and control method for human–exoskeleton collaborative packaging systems, which integrates deep reinforcement learning to enable adaptive, real-time coordination between human operators and robotic exoskeletons. This work, published in 2025, represents a significant advance in creating safer and more efficient human-machine interfaces for industrial and rehabilitation applications. While still early in its citation lifecycle, this research has already garnered attention for its innovative approach to solving complex collaborative tasks. Tan's work addresses critical challenges in ergonomics and automation, offering a framework that could transform how exoskeletons are deployed in manufacturing and logistics. Their research stands at the intersection of robotics, artificial intelligence, and human factors engineering, promising to enhance both productivity and worker well-being in physically demanding environments.
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