Xiaojuan Ban

University of Science and Technology Beijing

Papers

Total Citations

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About

Xiaojuan Ban is a leading researcher in embodied artificial intelligence and mobile robotics, with a focus on semantic navigation and active perception. Her work bridges the gap between computer vision and autonomous systems, enabling robots to intelligently locate and navigate toward specific objects in unknown environments. Ban’s most-cited paper, “An Efficient Object Navigation Strategy for Mobile Robots Based on Semantic Information” (2022, 6 citations), introduces a visual SLAM-based framework that enhances robot localization and map-building through semantic cues. Her more recent contribution, “An Object-Driven Navigation Strategy Based on Active Perception and Semantic Association” (2024, 4 citations), tackles the challenge of efficient, interpretable navigation in embodied AI by combining active perception with semantic reasoning. This work addresses key limitations of end-to-end learning and modular approaches, offering improved generalizability and efficiency. Ban’s research is particularly impactful for advancing autonomous robots in real-world settings, where understanding and interacting with objects is critical. Her innovative strategies are shaping the next generation of intelligent navigation systems, making her a notable figure in the field of robotics and AI.