Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation

Existing world model-based planners for visual navigationtypically follow a verification-centric paradigm, decoupling goal intentfrom trajectory synthesis. This approach suffers from candidate depen-dence, heavy computational overhead, and inconsistencies between sam-pled actions and predicted visuals. To address these issues, we proposeSWAM (Spatial-perceiving World Action Model), a task-centric jointobservation-action generation framework. Given start and goal RGB ob-servations, SWAM performs single-pass inference to simultaneously gen-erate intermediate RGB-D sequences and corresponding action trajecto-ries, promoting goal-consistent trajectory generation and improved spa-tial feasibility. SWAM leverages depth pseudo-labels during training tointernalize spatial priors, but it requires only monocular RGB input atinference time by using depth estimation model. We further introducea visual-guided action refinement module and a trajectory-scale regular-ization loss to enforce fine-grained alignment between motion and visualcues while stabilizing predictions across varying distances. Extensive ex-periments show that SWAM significantly outperforms state-of-the-arttwo-stage planners in success rate, trajectory accuracy, and inference ef-ficiency, while demonstrating robust zero-shot generalization to unseenenvironments.