RAC-WAM: Region-Wise Adversarial Consistency for Visual Robustness in World-Action Models

World action models (WAMs) jointly predict robot actions and future visual states, with future prediction providing supervision beyond action labels. Yet their performance still degrades notably under visual distribution shifts such as changes in camera viewpoint or scene appearance. Adversarial consistency training offers an effective way to improve robustness, but existing methods typically use action-only consistency and perform global perturbation search over the full observation. These choices are not fully aligned with WAMs: action-only consistency may miss vulnerabilities exposed by the future-video branch, while global search may repeatedly exploit the most sensitive evidence, leaving other semantic regions underexplored. To address these issues, we propose RAC-WAM, a region-wise adversarial consistency framework for robust WAMs. RAC-WAM jointly leverages action and future-video discrepancies to guide adversarial search. To broaden perturbation coverage across camera views and semantic regions, RAC-WAM performs hierarchical region-wise search by sampling a camera scope and constructing separate perturbations for robot, object, and background regions. To account for regional sensitivity differences under a conserved perturbation budget, perturbation magnitudes are allocated across regions according to local sensitivity. Experiments on LIBERO-Plus, RoboTwin 2.0, and a Franka robot demonstrate the effectiveness of RAC-WAM: it improves zero-shot success on LIBERO-Plus from 72.2% to 82.8% over a same-backbone baseline while preserving in-distribution performance, and delivers consistent robustness gains in bimanual and real-world manipulation.