Efficient Image-Goal Navigation with Representative Latent World Model

TL;DR

This paper introduces ReL-NWM, a novel latent space world model for efficient image-goal navigation that bypasses pixel-level reconstruction, enabling fast planning and successful real-world deployment.

Contribution

The paper presents ReL-NWM, a high-level semantic latent space model that improves navigation efficiency and performance over traditional pixel-based world models.

Findings

Achieves state-of-the-art trajectory prediction accuracy.

Demonstrates effective image-goal navigation in benchmarks.

Successfully deployed on a real humanoid robot.

Abstract

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.