Fangjun Li

University of Leeds, Beijing University of Chemical Technology

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Total Citations

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About

Fangjun Li is a pioneering researcher at the intersection of autonomous robotics, natural language acquisition, and soft actuator design. His work addresses two fundamental challenges in robotics: how machines can autonomously learn language through perceptual grounding, and how they can interact safely with the world through novel soft materials. In his highly cited 2021 paper (17 citations), Li introduced OLAV, a groundbreaking system that, for the first time, enables autonomous robots to bootstrap knowledge by simultaneously learning grammar and grounding words to visual perception—a critical step toward truly intelligent, self-learning machines. Complementing this cognitive work, Li’s 2022 study (10 citations) on lightweight dual-mode soft actuators, fabricated from bellows and foam, represents a significant advance in soft robotics. By designing a negative-pressure-driven foam actuator, he dramatically improved durability and compressibility, opening new possibilities for safe, lightweight robotic systems. Li’s dual expertise in cognitive robotics and physical hardware design marks him as a versatile innovator, whose contributions are shaping the future of autonomous, physically embodied AI.