Learning Transferable Dynamics Priors from Action to World Modeling

We study action-conditioned world modeling as a scalableway to learn transferable dynamics priors for robot learning. By pre-training a model to predict how actions drive visual scene evolution,the resulting world model captures reusable interaction dynamics be-yond appearance-level video generation. Concretely, we pretrain a multi-view interactive base diffusion world model, A2World, on large-scalerobot manipulation data with real action annotations. We validate thelearned dynamics priors from two complementary perspectives. First,we adapt A2World into a task- or scene-specialized real-world simula-tor, A2World-sim, whose long-horizon rollouts support simulator-basedpolicy evaluation and scalable what-if analysis by replacing real-robotrollouts with world model rollouts. Second, starting from the same pre-trained weights, we adapt A2World into a video-action joint predictionmodel, A2World-policy, that predicts actions under visual and instruc-tion conditioning. Experiments across simulation benchmarks and real-robot settings demonstrate that action-conditioned world model pre-training yields transferable dynamics priors that benefit both simulator-centric and policy-centric robot learning.