Lianqing Yu

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About

Lianqing Yu is an emerging researcher specializing in robotic manipulation, garment handling, and reinforcement learning for deformable object interaction. Their work sits at the intersection of computer vision, motion planning, and autonomous manipulation, addressing one of robotics' most challenging problems: enabling robots to handle flexible, unpredictable materials like clothing with human-like dexterity. Yu's most notable contribution, "Learning to Unfold Garment Effectively Into Oriented Direction" (2023), introduces a novel policy framework that intelligently selects between dynamic fling and quasi-static pick-and-place actions to unfold garments into specific planar orientations. This work is particularly impactful because it directly addresses the practical pipeline of garment manipulation — recognizing that precise unfolding is a critical prerequisite for downstream tasks such as automated folding. The study has already garnered 13 citations since its publication, reflecting growing interest from the robotics and AI communities in dexterous manipulation of deformable objects. Yu's research contributes meaningfully to the broader goal of deploying robots in domestic and industrial settings where handling soft goods is routine, pushing the boundaries of what autonomous systems can achieve with complex, real-world objects.