Papers
3
Total Citations
81
H-Index
2
About
Ya-Yu Huang is a leading researcher in the field of robotic dexterous manipulation, with a primary focus on enabling multifinger hands to perform human-like grasping and manipulation. Her work bridges the gap between human dexterity and robotic systems, addressing fundamental challenges in functional grasping, generalization across diverse robot hand kinematics, and teleoperation control. Huang’s most cited paper, "Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning" (2021, 58 citations), provides a comprehensive survey that has become a key reference for researchers exploring deep learning approaches to dexterous grasping. Her 2024 study, "Learning Human-Like Functional Grasping for Multifinger Hands From Few Demonstrations" (21 citations), introduces innovative methods for teaching robots to grasp objects with specific intentions using minimal human demonstrations—a significant step toward practical, adaptable robotic hands. Additionally, her work on teleoperated anthropomorphic hand-arm systems (2023) demonstrates real-time human-like manipulation. With a growing citation impact and a focus on solving real-world robotic challenges, Huang is shaping the future of dexterous robotics for industrial and assistive applications.
Research Focus
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