Papers
3
Total Citations
67
H-Index
3
About
Qingwei Nie is an emerging researcher at the intersection of human-robot interaction, augmented reality (AR), and artificial intelligence, with a particular focus on developing intuitive and intelligent collaborative systems for industrial applications. His work addresses a critical challenge in modern manufacturing and maintenance: enabling seamless, user-friendly cooperation between humans and robots through cutting-edge perceptual and learning technologies. Nie's most influential contribution, accumulating 42 citations since 2023, introduced a mixed perception-driven framework that combines AR with online deep reinforcement learning to enhance human-robot collaborative maintenance — a significant step toward adaptive, real-time industrial workflows. Building on this foundation, he extended these principles to robot teaching environments, proposing cloud-edge orchestrated AR interaction systems that lower the technical barriers for non-expert users, earning 21 citations. His most recent work integrates visual language models with deep reinforcement learning for AR-assisted assembly planning, reflecting his commitment to pushing the boundaries of multimodal AI in collaborative robotics. Collectively, Nie's research demonstrates a consistent vision: making human-robot collaboration more intelligent, accessible, and practically deployable — a contribution of growing relevance as smart manufacturing continues to evolve rapidly.
Research Focus
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Top Papers
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