Papers
2
Total Citations
18
H-Index
2
About
Zongli Qi is a pioneering researcher at the intersection of artificial intelligence and manufacturing, whose work focuses on leveraging large language models (LLMs) to revolutionize human-robot collaboration in complex assembly environments. His key contributions lie in developing multi-agent task planning systems that balance operator experience with operational efficiency, particularly in high-stakes aerospace applications. Qi's most cited paper, "LLM-based multi-agent task planning for human-robot collaborative assembly balancing operator experience and efficiency" (2025, 11 citations), introduces a novel framework where LLM-powered agents coordinate human and robotic actions to optimize both productivity and worker well-being. His follow-up work, "LLM based autonomous agent of human-robot collaboration for aerospace wire harnessing assembly" (2025, 7 citations), demonstrates the practical deployment of these agents in real-world aerospace manufacturing, addressing the intricate challenges of wire harnessing—a task requiring dexterity, precision, and adaptability. By integrating natural language understanding with robotic control, Qi is advancing a new paradigm of intelligent, adaptive manufacturing systems. His research not only pushes the boundaries of autonomous agents but also prioritizes human-centric design, making him a notable figure in the emerging field of LLM-driven industrial automation.
Research Focus
Key Achievements
Top Papers
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