Zhuoran Li

City University Malaysia, Fuzhou University

Papers

Total Citations

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About

Zhuoran Li is at the forefront of intelligent robotics and autonomous systems, with a research focus on reinforcement learning, human-robot interaction, and scene understanding for smart manufacturing. Their most impactful work includes the development of an advanced 3D navigation system for Automated Guided Vehicles (AGVs) in complex smart factory environments, which has garnered 15 citations for addressing the limitations of traditional 2D planning in Industry 4.0 settings. Li also pioneered a novel deep reinforcement learning framework for variable impedance control in robotic massage, a contact-rich manipulation task that traditionally relies on rigid environmental models—this work, with 14 citations, demonstrates how robots can adaptively learn compliant behaviors. More recently, Li introduced RoboEXP, an interactive exploration system that generates action-conditioned scene graphs for robotic manipulation, enabling robots to autonomously map both geometric and semantic features of their surroundings. This work, with 5 citations, represents a significant step toward truly autonomous robots capable of understanding and acting within unstructured environments. Li’s contributions are shaping the next generation of adaptive, intelligent robotic systems for both industrial and service applications.