Yangsheng Xu

Carnegie Mellon University, Chinese University of Hong Kong

Papers

Total Citations

404

H-Index

About

Yangsheng Xu is a pioneering robotics researcher whose work spans human-robot interaction, robot learning, manipulator control, and space robotics. Best known for his foundational 1997 paper on human action learning via Hidden Markov Models — now cited over 240 times — Xu developed a groundbreaking framework enabling robots to recognize, characterize, and emulate human skills, laying essential groundwork for cooperative human-robot systems. His contributions to manipulator control are equally significant: his 2002 work on dual neural networks for bi-criteria kinematic control of redundant manipulators, cited nearly 150 times, introduced an elegant solution for managing physical joint constraints while minimizing discontinuities in motion planning. Xu has also advanced the understanding of underactuated manipulators, exploring dexterity trade-offs relevant to fault-tolerant and energy-efficient robotic design. His work on the Self-Mobile Space Manipulator (SM²) at Carnegie Mellon University's Robotics Institute further demonstrates his reach into space robotics, contributing to NASA's vision for autonomous exterior maintenance of the Space Station. Across these domains, Xu's research reflects a consistent commitment to making robots more adaptable, intelligent, and capable of meaningful collaboration with humans.