RAE-NWM: Navigation World Model in Dense Visual Representation Space

Abstract

Visual navigation requires agents to reach goals in complexenvironments through perception and planning. World models addressthis task by simulating action-conditioned state transitions to predictfuture observations. Current navigation world models typically learnstate evolution under actions within the compressed latent space of aVariational Autoencoder, where spatial compression often discards fine-grained structural information and makes precise action-conditioned pre-diction more difficult. To better understand the propagation character-istics of different representations, we conduct a linear dynamics probeand observe that dense DINOv2 features exhibit stronger linear pre-dictability for action-conditioned transitions. Motivated by this obser-vation, we propose the Representation Autoencoder-based NavigationWorld Model (RAE-NWM), which generatively models navigation dy-namics in a dense visual representation space. We employ a Condi-tional Diffusion Transformer with a Decoupled Diffusion Transformerhead (CDiT-DH) to model continuous transitions, and introduce a sep-arate time-driven gating module for dynamics conditioning to regulateaction injection strength during generation. Extensive evaluations showthat modeling sequential rollouts in this space improves structural stabil-ity and action accuracy, benefiting downstream planning and navigation.Code is available at https://github.com/20robo/raenwm.