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About

Jianglong Ye is a robotics researcher whose work sits at the intersection of dexterous manipulation, 3D scene understanding, and mobile robotics. His most impactful contribution is the **Continuous Grasping Function (CGF)**, a generative model that learns to produce smooth, continuous grasping motions for dexterous hands directly from human demonstrations—a paper that has already garnered **42 citations** since 2023. This work addresses a fundamental challenge in manipulation: enabling robots to grasp objects with the fluidity and adaptability of human hands. Ye also introduced **GNFactor**, a multi-task learning framework that leverages generalizable neural feature fields to help robots understand both the 3D structure and semantics of their environment, enabling robust manipulation in unstructured real-world settings. His research further extends to online adaptation for implicit object tracking and shape reconstruction in cluttered scenes, as well as generalizable feature fields for mobile manipulation—bridging the gap between navigation and fine-grained object interaction. With publications spanning top venues and a growing citation impact, Ye is shaping the future of how robots perceive, grasp, and interact with the physical world.