Yuan Guan
Papers
2
Total Citations
51
H-Index
2
About
Yuan Guan is a leading researcher in surgical robotics, specializing in human-robot shared control and sim-to-real adaptation. His most impactful work, "Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation" (2022, 49 citations), pioneers a novel framework that seamlessly integrates human expertise with robotic precision during surgical procedures. By leveraging Learning from Demonstration (LfD) techniques, Guan enables surgical robots to autonomously execute complex subtasks while maintaining intuitive human oversight—a critical advancement for minimally invasive surgery. His context-aware approach bridges the simulation-to-reality gap, allowing robots trained in virtual environments to adapt reliably to real-world surgical scenarios. This work addresses a fundamental challenge in medical robotics: balancing automation with clinician control. Guan's research has been cited over 50 times, reflecting its growing influence in both robotics and surgical communities. His contributions are particularly notable for enhancing surgical efficiency and safety, with potential applications ranging from laparoscopic procedures to microsurgery. By developing shared control architectures that learn from expert demonstrations, Guan is helping to define the next generation of intelligent surgical assistants—systems that augment rather than replace human surgeons.
Research Focus
Key Achievements
Top Papers
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- 2