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A lightweight module injects temporal memory into robotic policies, enabling them to distinguish between identical-looking states at different points in a repeated motion.
VLN agents can navigate more effectively by predicting their future states and proactively planning based on forecasted semantic map cues, rather than relying solely on historical context.