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About

Jianlong Fu is an AI researcher whose work sits at the cutting edge of embodied intelligence, robotics, and vision-language modeling. His research focuses on enabling robots to perform complex, generalizable manipulation tasks by bridging the gap between high-level cognitive reasoning and low-level physical action. Fu's most recognized contribution, "AlphaBlock" (2023), introduces a novel framework for embodied finetuning that empowers robots to reason through multi-step tasks — such as arranging building blocks into meaningful patterns — without relying on large volumes of paired training data, garnering 9 citations since its publication. Building on this foundation, his work on "Transferring Foundation Models for Generalizable Robotic Manipulation" addresses a persistent challenge in the field: achieving real-world generalization without costly large-scale data collection. His 2024 paper "CogACT" further advances the state of Vision-Language-Action models by tightly coupling cognition and action within a unified architecture, improving language-guided task execution. Collectively, Fu's research advances the frontier of intelligent robotic systems, offering scalable, data-efficient solutions that bring general-purpose robots meaningfully closer to practical, real-world deployment.