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Humans use their knowledge of common house layouts obtained from previousexperiences to predict nearby rooms while navigating in new environments. Thisgreatly helps them navigate previously unseen environments and locate theirtarget room. To provide layout prior knowledge to navigational agents based oncommon human living spaces, we propose WIN (\textit{W}hat \textit{I}s\textit{N}ear), a commonsense learning model for Vision Language Navigation(VLN) tasks. VLN requires an agent to traverse indoor environments based ondescriptive navigational instructions. Unlike existing layout learning works,WIN predicts the local neighborhood map based on prior knowledge of livingspaces and current observation, operating on an imagined global map of theentire environment. The model infers neighborhood regions based on visual cuesof current observations, navigational history, and layout common sense. We showthat local-global planning based on locality knowledge and predicting theindoor layout allows the agent to efficiently select the appropriate action.Specifically, we devised a cross-modal transformer that utilizes this localityprior for decision-making in addition to visual inputs and instructions.Experimental results show that locality learning using WIN provides bettergeneralizability compared to classical VLN agents in unseen environments. Ourmodel performs favorably on standard VLN metrics, with Success Rate 68\% andSuccess weighted by Path Length 63\% in unseen environments.

What Is Near?: Room Locality Learning for Enhanced Robot Vision-Language-Navigation in Indoor Living Environments