Qinling Xu
Papers
2
Total Citations
29
H-Index
2
About
Qinling Xu is at the forefront of advancing autonomous robotic systems for healthcare, with a primary focus on robotic massage—a challenging contact-rich manipulation domain. Her research bridges interactive trajectory planning, variable impedance control, and deep reinforcement learning to enable robots to perform complex physical interactions with human subjects safely and effectively. In her 2024 work "Toward automatic robotic massage based on interactive trajectory planning and control" (15 citations), Xu introduced a vision-based framework that allows robots to dynamically adapt their movements in real-time, addressing the inherent unpredictability of human-robot contact. She further advanced the field with "Learning Variable Impedance Control for Robotic Massage With Deep Reinforcement Learning" (14 citations), where she developed a novel learning framework that enables robots to autonomously modulate their stiffness and damping during physical interaction—overcoming the limitations of traditional model-based compliance control. By tackling the dual challenges of environmental uncertainty and task complexity, Xu’s research is paving the way for practical, autonomous robotic systems that could significantly reduce the workload of healthcare professionals while improving patient care quality.
Research Focus
Key Achievements
Top Papers
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- 2