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About

Jiaxu Xing is an emerging robotics and autonomous systems researcher whose work sits at the intersection of machine learning, visual perception, and real-world robot deployment. With a growing citation record totaling over 40 citations, Xing has made notable contributions across several interconnected research threads, most prominently in sim-to-real transfer, failure detection, and agile robotic flight. Xing's most impactful work explores how robots can reliably operate beyond controlled laboratory settings. Their 2024 paper on contrastive learning for scene transfer in vision-based agile flight (17 citations) addresses one of robotics' most persistent challenges: enabling end-to-end policies to generalize across diverse real-world environments. Complementing this, their research on self-failure detection using multi-task visual perception (11 citations) demonstrates a creative approach to robotic self-awareness by leveraging cross-task signals from segmentation, depth, and normal estimation. Xing has also contributed practically to aerial robotics applications, including intelligent drone systems for electrical powerline inspection. More recent work tackles generalization in drone racing and the simulation-to-reality gap — fundamental barriers for deploying learned policies at scale. Together, these contributions position Xing as a researcher genuinely invested in making autonomous robots robust, adaptable, and practically deployable.