# UniNav: A Unified World-Action Diffusion Model for Visual Navigation

arXiv: https://arxiv.org/abs/2608.03244 · HTML: https://mlsanity.com/paper/2608.03244/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 32 of its 52 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)
- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 30 of 32)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets](https://mlsanity.com/paper/2504.02792/) (2025)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [Unified Video Action Model](https://mlsanity.com/paper/2503.00200/) (2025)
- [mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs](https://mlsanity.com/paper/2512.15692/) (2025)
- [GigaWorld-Policy: An Efficient Action-Centered World--Action Model](https://mlsanity.com/paper/2603.17240/) (2026)
- [Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation](https://mlsanity.com/paper/2508.05635/) (2025)
- [Rapid Exploration for Open-World Navigation with Latent Goal Models](https://mlsanity.com/paper/2104.05859/) (2021)
- [Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising](https://mlsanity.com/paper/2604.26694/) (2026)
- [NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance](https://mlsanity.com/paper/2505.08712/) (2025)
- [VGGT-Omega](https://mlsanity.com/paper/2605.15195/) (2026)
- [Light-WAM: Efficient World Action Models with State-Fusion Action Decoding](https://mlsanity.com/paper/2606.08242/) (2026)
- [Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight](https://mlsanity.com/paper/2510.08713/) (2025)
- [AIM: Intent-Aware Unified world action Modeling with Spatial Value Maps](https://mlsanity.com/paper/2604.11135/) (2026)
- [Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation](https://mlsanity.com/paper/2606.15869/) (2026)
- [NavDreamer: Video Models as Zero-Shot 3D Navigators](https://mlsanity.com/paper/2602.09765/) (2026)
- [RAE-NWM: Navigation World Model in Dense Visual Representation Space](https://mlsanity.com/paper/2603.09241/) (2026)
- [SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy](https://mlsanity.com/paper/2604.03181/) (2026)
- [WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation](https://mlsanity.com/paper/2606.04907/) (2026)
- [MWM: Mobile World Models for Action-Conditioned Consistent Prediction](https://mlsanity.com/paper/2603.07799/) (2026)
- [Efficient Image-Goal Navigation with Representative Latent World Model](https://mlsanity.com/paper/2511.11011/) (2025)
- [LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry](https://mlsanity.com/paper/2512.19629/) (2025)

## Built on by (most cited first, 2 of 2)

- [Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models](https://mlsanity.com/paper/2609.26007/) (2026)
- [Visual Representation and History Modeling for Navigation World Models](https://mlsanity.com/paper/2609.29555/) (2026)

## Abstract (arXiv)

Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts. We present UniNav, a unified world-action model that generates future visual observations and continuous waypoint trajectories through a single diffusion process. Given history frames and a goal image, UniNav jointly denoises visual and waypoint tokens within a single transformer, unifying future prediction and action generation in a shared framework. To improve spatial grounding, we incorporate geometry-aware camera tokens. We also train on both trajectory-labeled navigation data and video-only data, enabling the model to benefit from diverse videos without waypoint annotations. Based on this unified framework, we introduce two variants: UniNav-Full jointly predicts interpretable future observations and their corresponding trajectories, while UniNav-Fast removes future-image tokens at inference for efficient trajectory prediction. Experiments on navigation benchmarks show that UniNav outperforms the strongest baseline in ATE across all datasets. With one-step inference, UniNav-Fast achieves a latency of 0.1s without a substantial accuracy drop. Code will be released.