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About

Yifei Yang is a pioneering researcher at the intersection of robotics and computer vision, whose work is shaping how machines perceive and interact with unstructured environments. His primary research areas span humanoid robotics, open-set object detection, and instance segmentation—critical domains for enabling robots to operate safely and autonomously in the real world. Yang’s most impactful contribution is his comprehensive review of humanoid robots (2025, 26 citations), which synthesizes progress, challenges, and future directions, serving as a foundational resource for the field. He has also advanced open-set object detection (2023, 21 citations) by introducing a classification-free object proposal method combined with instance-level contrastive learning, allowing robots to detect both known and unknown objects—a fundamental skill for manipulation in cluttered settings. More recently, Yang tackled open-set instance segmentation (2024, 3 citations), developing a class semantics modulation approach that segments known and unknown objects without prior training, enhancing robot safety in real-world deployment. His work bridges theory and application, offering scalable solutions for perception in dynamic environments. With a growing citation footprint and a focus on open-world challenges, Yifei Yang is a rising voice in robotics, driving innovation toward more adaptable and intelligent autonomous systems.