Chengjie Yuan
Papers
6
Total Citations
66
H-Index
4
About
Chengjie Yuan is a robotics researcher whose work sits at the intersection of reinforcement learning, sim-to-real transfer, and contact-rich robotic assembly. With a growing body of work accumulating over 60 citations, Yuan has made meaningful contributions to one of robotics' most pressing challenges: bridging the gap between simulation training environments and real-world deployment. His most cited work (2023, 28 citations) introduces a framework combining CycleGAN-based visual domain adaptation with force control, enabling robots trained in simulation to perform complex assembly tasks in the physical world. This line of research, extended across multiple publications, demonstrates a consistent focus on making deep reinforcement learning practical and safe for industrial manipulation settings. Yuan has also advanced the integration of Dynamic Movement Primitives with impedance adaptation via reinforcement learning, allowing robots to handle variable contact geometry robustly. Notably, his research portfolio extends beyond robotics into biomechanics, with work on foot bone kinematics using a cadaveric gait simulator, reflecting an interdisciplinary curiosity. Yuan's contributions are particularly valuable for researchers working on manufacturing automation, human-robot collaboration, and the practical deployment of learning-based robotic systems.
Research Focus
Key Achievements
Top Papers
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