Fanlong Zeng

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About

Fanlong Zeng is a leading researcher at the intersection of artificial intelligence and robotics, with a primary focus on leveraging large language models (LLMs) to advance robotic manipulation and dexterity. His most-cited work, the 2023 survey "Large Language Models for Robotics: A Survey" (36 citations), provides a comprehensive roadmap for integrating LLMs into robotic systems, highlighting how multi-modality feedback can unlock what he terms "dexterity intelligence"—the ability to learn, generalize, and control complex manipulation tasks. This foundational contribution has helped shape a rapidly growing field, bridging the gap between high-level language understanding and low-level robotic control. Zeng's research explores how robots can interpret human instructions, adapt to dynamic environments, and perform intricate tasks through language-guided learning. By systematically categorizing emerging methods and challenges, his survey has become an essential reference for researchers and students alike, offering a clear vision for the future of intelligent robotics. His work underscores the transformative potential of LLMs in enabling more capable, adaptable, and human-interactive robotic systems.