Papers
5
Total Citations
78
H-Index
5
About
Haonan Luo is a researcher specializing in embodied AI, robot navigation, and multimodal learning, with a particular focus on enabling intelligent agents to perceive, reason, and act within real-world environments. His work sits at the intersection of computer vision, natural language processing, and robotics, addressing some of the most challenging problems in human-robot interaction. Luo's most influential contribution, "SegEQA" (2019, 30 citations), pioneered the application of video segmentation and visual attention mechanisms to Embodied Question Answering (EQA), a field where autonomous agents must explore environments and answer natural language queries — with direct applications in autonomous driving and home robotics. He has consistently advanced this domain, developing robust learning frameworks to handle noisy real-world training labels ("Robust-EQA," 2023, 17 citations) and leveraging transformer-based vision-language alignment for navigation and question answering tasks (2024, 19 citations). More recently, Luo has expanded into multi-robot coordination, exploring how semantic and multimodal knowledge can be collaboratively shared across robot teams for efficient environment exploration. Collectively accumulating nearly 80 citations, his body of work reflects a coherent and growing research agenda pushing embodied AI toward practical, scalable deployment in complex real-world scenarios.
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