Ming Chong Lim

Korea Advanced Institute of Science and Technology, Nanyang Technological University

Papers

Total Citations

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About

Ming Chong Lim is a researcher whose work bridges artificial intelligence, robotics, and early childhood education. His key research areas include deep reinforcement learning for autonomous navigation and AI-enhanced language learning. In his highly cited 2021 paper, “Extendable Navigation Network based Reinforcement Learning for Indoor Robot Exploration” (14 citations), Lim introduced a novel framework that combines navigation networks with deep reinforcement learning, featuring a pattern-cognitive non-myopic exploration strategy. This work enables robots to more efficiently and intuitively explore unknown indoor environments, reflecting universal structural preferences. More recently, Lim has turned his attention to the intersection of AI and early language education. His 2025 scoping review, “AI and early language learning” (6 citations), systematically examines how AI tools are being used to personalize language input for young children, covering common AI applications and theoretical foundations. This work highlights Lim’s versatility and his commitment to applying cutting-edge AI techniques across both technical and pedagogical domains. His contributions are shaping how autonomous systems navigate complex spaces and how AI can support foundational learning in early childhood.