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HuRo: Robotizing Human Videos for Scalable VLA Pretraining

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Human video datasets have emerged as a compelling alternative to expensive real-robot data, offering rich diversity at scale. To bridge the human-to-robot embodiment gap, existing approaches either robotize videos in task-matched settings or address observation and action alignment separately at scale. In this work, we systematically examine whether robotized human videos can provide effective and scalable supervision for pretraining vision-language-action (VLA) policies. To this end, we develop a robotization pipeline that converts heterogeneous human videos into robot-aligned observations an

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