Yongkang Qiu
Papers
2
Total Citations
11
H-Index
2
About
Yongkang Qiu is a robotics researcher focused on advancing human-robot interaction and safety through sensorless force detection. His primary research areas include humanoid robotics, neural network-based control systems, and external force estimation without dedicated torque sensors. Qiu’s major contribution lies in developing a method that uses BP (backpropagation) neural networks to detect external forces on robot arms in real time, eliminating the need for costly and bulky joint torque sensors. This innovation enhances the safety and affordability of humanoid robots, enabling them to sense and respond to unexpected contacts during operation—a critical capability for collaborative environments. His most cited work, “Sensorless External Force Detection Method for Robot Arm Based on Error Compensation Using BP Neural Network” (2019), has garnered 8 citations, while a closely related paper from the same year has received 3 citations. Though early in his career, Qiu’s work represents a practical step toward more intuitive and safe robotic systems, with potential applications in manufacturing, healthcare, and assistive robotics. His research continues to explore how machine learning can bridge the gap between low-cost hardware and high-performance robotic sensing.
Research Focus
Key Achievements
Top Papers
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