FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation

Vision-and-Language Navigation in Continuous Environments(VLN-CE) requires agents to follow natural-language instructions in un-seen scenes. While Large Models (LMs) have advanced VLN-CE, theirperformance remains severely degraded by real-world visual corruptions,a critical yet underexplored domain constraint. We introduce Tempo-ral Conditional Flow Decorruptor (FlowDec), a novel image restorationframework tailored for LM-based VLN-CE. FlowDec integrates a hybridtemporal conditioning strategy to align the generative flow path withhistorical context and employs action-centroid guided filtering to dy-namically assess and integrate outputs. Extensive experiments demon-strate that FlowDec outperforms state-of-the-art decorruption methodsin both navigation accuracy and generation latency. Our approach es-tablishes a robust, efficient paradigm for resilient embodied navigationin unpredictable real-world conditions.