Learning Transferable World-Action Models from Task-Paired Human Videos

WATCH uses task-paired human videos to enable cross-video pre-training for world-action models. By conditioning on the actions and frames of one demonstration to predict another, the model learns task-relevant physical interactions instead of relying on static visual appearance. This method facilitates better transfer across diverse environments and embodiments.