Yanlong Huang

Papers

Total Citations

H-Index

About

Yanlong Huang is a robotics and machine learning researcher whose work sits at the intersection of robot learning, motion planning, and human-robot interaction. His research focuses on enabling robots to acquire and refine motor skills through data-driven approaches, with a particular emphasis on locally weighted regression (LWR) and active learning methodologies. One of his most recognized contributions is the integration of active learning with LWR for ping-pong playing robots, a challenging real-world testbed that demands precise, dynamic motor control. In this work, Huang demonstrated how robots could learn and adaptively refine racket control parameters to accurately return balls to desired target positions — a problem requiring both speed and accuracy under uncertainty. Published in 2012 and accumulating 26 citations, this research exemplifies his broader commitment to making robot learning more sample-efficient and practically deployable. By combining classical regression techniques with active learning strategies, Huang's work helps bridge the gap between theoretical machine learning and real-world robotic applications, making his contributions particularly valuable to researchers working on skill acquisition, adaptive control, and intelligent autonomous systems.