Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation

General-purpose vision-language-action (VLA) models leverage large-scale vision-language priors to understand scenes and instructions, but primarily generate actions directly from the current observation. World-action models (WAMs) further model future scene states, offering a broader view of how the environment may evolve. However, for robotic manipulation, predicting the entire future scene can introduce information beyond what is necessary for action generation; more importantly, effective actions require knowing not only what the scene may become, but also where and how relevant changes unfold. To bridge action generation with these action-relevant aspects of the world, we present Bridge-WA, a general world-action framework that learns complementary representations of future states, spatial changes, and local motion. Specifically, Bridge-WA consists of a Latent World Dynamics Module (LWDM) and WorldBridge. LWDM predicts future states, spatial changes, and local motion from VLM outputs, supervised by corresponding world targets. The WorldBridge are embedded into the action transformer and inject layer-specific combinations of these world priors through multi-source attention, spatiotemporal biases, and reliability-gated feature modulation. This design grounds action generation in action-relevant future dynamics while adaptively regulating world guidance, enabling robust generalization to visual disturbances and viewpoint shifts. We evaluate on LIBERO-Plus, LIBERO-Dynamic, RoboTwin 2.0, VLABench, and real-world robots, where it achieves maximum success-rate improvements of 11.1%, 42.0%, 3.7%, 23.4%, and 11.1%, respectively, and achieves state-of-the-art average success rates on LIBERO-Dynamic, RoboTwin 2.0 and real-world. In particular, Bridge-WA demonstrates strong generalization to visual variations and viewpoint shifts in both simulation and real-world settings.