Zequn Zhang

Nanjing University of Aeronautics and Astronautics

Papers

Total Citations

H-Index

About

Zequn Zhang is an emerging researcher at the forefront of intelligent manufacturing and human-robot collaboration (HRC), with a focus on developing adaptive, generalizable systems that bridge the gap between artificial intelligence and real-world industrial applications. His most notable work, "Foundation Models Assist in Human–Robot Collaboration Assembly" (2024), has already garnered 8 citations within its first year of publication — a promising indicator of its impact within the rapidly evolving robotics and manufacturing communities. In this work, Zhang addresses a critical limitation in existing HRC systems: their inability to transfer knowledge and generalize across diverse environments. By harnessing the power of foundation models, he proposes a framework that enhances perception and decision-making capabilities in collaborative assembly tasks, enabling robots to work more flexibly and efficiently alongside human counterparts. His research sits at a compelling intersection of computer vision, large-scale AI models, and advanced manufacturing, tackling real-world challenges such as customization and adaptability. For students and researchers interested in the future of smart manufacturing, human-robot teaming, and the integration of foundation models into physical systems, Zhang's work represents a timely and forward-thinking contribution to the field.