Xiaolan Yao

Papers

Total Citations

H-Index

About

Xiaolan Yao is a researcher whose work lies at the intersection of mobile robotics and humanoid manipulation, with a particular focus on control systems and learning-based approaches. Yao’s key contributions include advancing speed control for mobile robot platforms through the application of second-order Linear Active Disturbance Rejection Control (LADRC), a method designed to ensure stable and accurate velocity tracking even in the presence of external disturbances. This foundational work, published in 2016, has garnered 6 citations and addresses a critical challenge in real-world robotic deployment. Expanding into humanoid robotics, Yao explored object grasping and manipulation through learning by demonstration—a paradigm that enables robots to acquire complex skills by observing human actions rather than requiring explicit programming. This 2017 study, with 2 citations, reflects Yao’s commitment to making robotics more accessible and adaptable for applications ranging from household assistance to hazardous environment operations. By bridging classical control theory with modern imitation learning, Yao’s research contributes to the development of more intuitive and resilient robotic systems, offering valuable insights for students and researchers working at the intersection of control, learning, and autonomous manipulation.