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 diverse manipulation dynamics. We introduce LAPT-Scale, a scalable latent action pre-training paradigm with two components: LAPT-Scale-LAM learns continuous latent actions by explaining frame-to-frame visual transitions in human videos and uses them as human-derived supervision, while LAPT-Scale-Policy trains a VLA policy through human-video latent-action pre-training, target-robot embodiment align...
| Authors | Zhiyuan Xiang, Peidong Liu, Mingyang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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