# Efficient Image-Goal Navigation with Representative Latent World Model

arXiv: https://arxiv.org/abs/2511.11011 · HTML: https://mlsanity.com/paper/2511.11011/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 7 of 7)

- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [Pathdreamer: A World Model for Indoor Navigation](https://mlsanity.com/paper/2105.08756/) (2021)
- [WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation](https://mlsanity.com/paper/2503.02247/) (2025)
- [X-MOBILITY: End-To-End Generalizable Navigation via World Modeling](https://mlsanity.com/paper/2410.17491/) (2024)

## Built on by (most cited first, 6 of 6)

- [RAE-NWM: Navigation World Model in Dense Visual Representation Space](https://mlsanity.com/paper/2603.09241/) (2026)
- [WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling](https://mlsanity.com/paper/2605.06407/) (2026)
- [UniNav: A Unified World-Action Diffusion Model for Visual Navigation](https://mlsanity.com/paper/2608.03244/) (2026)
- [Uncertainty-Aware World Model for Aerial Image-Goal Navigation](https://mlsanity.com/paper/2608.05597/) (2026)
- [Feeling Terrain Before Crossing: World Models for Off-Road Navigation](https://mlsanity.com/paper/2609.19863/) (2026)
- [G2WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation](https://mlsanity.com/paper/2610.16558/) (2026)

## Abstract (arXiv)

World models enable robots to conduct counterfactual reasoning in physical environments by predicting future world states. While conventional approaches often prioritize pixel-level reconstruction of future scenes, such detailed rendering is computationally intensive and unnecessary for planning tasks like navigation. We therefore propose that prediction and planning can be efficiently performed directly within a latent space of high-level semantic representations. To realize this, we introduce the Representative Latent space Navigation World Model (ReL-NWM). Rather than relying on reconstructionoriented latent embeddings, our method leverages a pre-trained representation encoder, DINOv3, and incorporates specialized mechanisms to effectively integrate action signals and historical context within this representation space. By operating entirely in the latent domain, our model bypasses expensive explicit reconstruction and achieves highly efficient navigation planning. Experiments show state-of-the-art trajectory prediction and image-goal navigation performance on multiple benchmarks. Additionally, we demonstrate real-world applicability by deploying the system on a Unitree G1 humanoid robot, confirming its efficiency and robustness in practical navigation scenarios.