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LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and dive
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