Yibiao Zhang

Papers

Total Citations

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About

Yibiao Zhang is a robotics researcher whose work focuses on advancing robotic manipulation through functional and dexterous grasping. His primary research areas include robot grasping, hand-object interaction, and semantic representation for task-oriented manipulation. Zhang’s major contribution lies in challenging the conventional emphasis on stable grasping, instead pioneering functional grasp approaches that enable robots to perform post-grasp manipulation tasks more akin to human behavior. His notable work, "FunctionalGrasp: Learning Functional Grasp for Robots via Semantic Hand-Object Representation" (2023, 23 citations), introduces a novel framework that embeds semantic understanding of hand-object relationships to generate grasps that are not merely stable but task-appropriate. This work has been recognized for addressing a critical gap in robotic manipulation, moving beyond simple pick-and-place to more complex, goal-directed interactions. Additionally, his paper "Hand-object information embedded dexterous grasping generation" (2023, 7 citations) further explores how integrating hand-object context can enhance dexterous grasp planning. Zhang’s research is highly relevant for students and researchers interested in human-robot interaction, artificial intelligence, and the future of autonomous robotic systems capable of nuanced, real-world tasks.