Yongkui Liu
Papers
12
Total Citations
247
H-Index
7
About
Yongkui Liu is a leading researcher at the intersection of intelligent manufacturing, robotics, and artificial intelligence, with a focus on deep reinforcement learning (DRL), digital twin technology, and cloud manufacturing. His work addresses fundamental challenges in enabling industrial robots to autonomously acquire complex manipulation skills, bridging the gap between simulated training environments and real-world deployment. His most cited work, "A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping" (2022, 124 citations), exemplifies his signature approach: leveraging digital twins to dramatically improve the efficiency and reliability of robot learning pipelines. Liu has made significant contributions to cloud manufacturing, developing DRL-based scheduling frameworks that optimize decentralized robot services across distributed industrial networks. His research spans multiple scales, from enterprise-level service scheduling to low-level robotic assembly policies, demonstrating both theoretical depth and practical applicability. With over 240 cumulative citations, his body of work has meaningfully shaped how smart factories integrate AI-driven robotics, making him an important voice in the ongoing evolution toward fully autonomous, intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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- 4Industrial Internet for Manufacturing9 citations · 2021
- 5A phased robotic assembly policy based on a PL-LSTM-SAC algorithm8 citations · 2024
- 6Smart robotics for manufacturing8 citations · 2023
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