Visual Representation and History Modeling for Navigation World Models

arXiv:2609.29555 · cs.CV, cs.RO · Submitted 2026-08-26 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Transformer — 19× in this paper · explained in 2 episodes
• World Model — 18× in this paper · explained in 7 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Decode — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Balanced Gated Delta Network — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
• RAE-NWM: Navigation World Model in Dense Visual Representation Space
• DINOv2: Learning Robust Visual Features without Supervision
• AR Forcing: Towards Long-Horizon Robot Navigation World Model
• Drift-Resistant Navigation World Model with Anchored Epipolar Guidance
• Latent World Models with Monotone Planning Costs for Image-Goal Navigation
• Uncertainty-Aware World Model for Aerial Image-Goal Navigation
• UniNav: A Unified World-Action Diffusion Model for Visual Navigation
• NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation
• SSR: Similarity-Shift Refinement for Training-Free Object-Centric Masks
• What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
• SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer
• DeltaFlow: Noise-Adaptive Bidirectional Gated Delta Networks for Embedded Language Flows
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
• Rapid Exploration for Open-World Navigation with Latent Goal Models
• DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models