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

arXiv cs.CV / 3/11/2026

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Key Points

  • The paper introduces RAE-NWM, a navigation world model that operates within a dense visual representation space instead of the traditional compressed latent space used by Variational Autoencoders.
  • The authors identify that dense DINOv2 features provide stronger linear predictability for action-conditioned state transitions, which improves the modeling of navigation dynamics.
  • RAE-NWM employs a Conditional Diffusion Transformer with a Decoupled Diffusion Transformer head, as well as a time-driven gating module to regulate action injection during transition generation.
  • Extensive evaluations demonstrate that using dense visual features enhances structural stability and action accuracy, which benefits downstream tasks in planning and navigation.
  • This approach addresses limitations in fine-grained spatial information loss typical in prior models, enabling more precise control in visual navigation tasks.

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.09241 (cs)
[Submitted on 10 Mar 2026]

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

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Abstract:Visual navigation requires agents to reach goals in complex environments through perception and planning. World models address this task by simulating action-conditioned state transitions to predict future observations. Current navigation world models typically learn state evolution under actions within the compressed latent space of a Variational Autoencoder, where spatial compression often discards fine-grained structural information and hinders precise control. To better understand the propagation characteristics of different representations, we conduct a linear dynamics probe and observe that dense DINOv2 features exhibit stronger linear predictability for action-conditioned transitions. Motivated by this observation, we propose the Representation Autoencoder-based Navigation World Model (RAE-NWM), which models navigation dynamics in a dense visual representation space. We employ a Conditional Diffusion Transformer with Decoupled Diffusion Transformer head (CDiT-DH) to model continuous transitions, and introduce a separate time-driven gating module for dynamics conditioning to regulate action injection strength during generation. Extensive evaluations show that modeling sequential rollouts in this space improves structural stability and action accuracy, benefiting downstream planning and navigation.
Comments:
Subjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
ACM classes: I.2.9; I.2.10
Cite as: arXiv:2603.09241 [cs.CV]
  (or arXiv:2603.09241v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.09241
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arXiv-issued DOI via DataCite

Submission history

From: Mingkun Zhang [view email]
[v1] Tue, 10 Mar 2026 06:16:23 UTC (1,607 KB)
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