# LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

arXiv: https://arxiv.org/abs/2606.15768 · HTML: https://mlsanity.com/paper/2606.15768/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 46 of its 47 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- Vision-language-action models / [World model-based VLA](https://mlsanity.com/vla/world-model-based-vla/)

## Builds on (most cited first, 30 of 46)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025)
- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [UniVLA: Learning to Act Anywhere with Task-centric Latent Actions](https://mlsanity.com/paper/2505.06111/) (2025)
- [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Latent Action Pretraining from Videos](https://mlsanity.com/paper/2410.11758/) (2024)
- [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)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video](https://mlsanity.com/paper/2505.11709/) (2025)
- [Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better](https://mlsanity.com/paper/2505.23705/) (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)
- [π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities](https://mlsanity.com/paper/2604.15483/) (2026)
- [DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos](https://mlsanity.com/paper/2602.06949/) (2026)
- [Learning to Act without Actions](https://mlsanity.com/paper/2312.10812/) (2023)
- [VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model](https://mlsanity.com/paper/2602.10098/) (2026)

## Built on by (most cited first, 30 of 52)

- [From Foundation to Application: Improving VLA Models in Practice](https://mlsanity.com/paper/2607.06403/) (2026)
- [JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling](https://mlsanity.com/paper/2608.09381/) (2026)
- [GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch](https://mlsanity.com/paper/2607.13960/) (2026)
- [ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts](https://mlsanity.com/paper/2607.28993/) (2026)
- [LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2608.03701/) (2026)
- [Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning](https://mlsanity.com/paper/2607.14183/) (2026)
- [World-Action Models for Robot Learning and Control: A Survey](https://mlsanity.com/paper/2609.16074/) (2026)
- [QQWorld: Quantile-Quantile Matching for World Model Regularization](https://mlsanity.com/paper/2607.28415/) (2026)
- [SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space](https://mlsanity.com/paper/2608.01397/) (2026)
- [Rethink Before You Execute: Adaptive Execution for World Action Models](https://mlsanity.com/paper/2608.09492/) (2026)
- [Foresight Without Seeing: Latent Futures for World Action Models](https://mlsanity.com/paper/2608.11605/) (2026)
- [Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models](https://mlsanity.com/paper/2608.05903/) (2026)
- [StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation](https://mlsanity.com/paper/2608.10780/) (2026)
- [LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models](https://mlsanity.com/paper/2608.22403/) (2026)
- [GlanceWAM: Sparse Test-Time Imagination for World-Action Models](https://mlsanity.com/paper/2608.23927/) (2026)
- [Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots](https://mlsanity.com/paper/2607.02501/) (2026)
- [Uncertainty-Aware World Model for Aerial Image-Goal Navigation](https://mlsanity.com/paper/2608.05597/) (2026)
- [World Models for Embodied Intelligence: From Plausible to Controllable to Actionable](https://mlsanity.com/paper/2609.16697/) (2026)
- [LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models](https://mlsanity.com/paper/2609.24350/) (2026)
- [MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence](https://mlsanity.com/paper/2609.25627/) (2026)
- [Streaming-WAM: Action-Conditioned World-Action Model for Asynchronous Robot Manipulation](https://mlsanity.com/paper/2609.28927/) (2026)
- [Rethinking Representations for World-Action Modeling](https://mlsanity.com/paper/2609.38163/) (2026)
- [Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models](https://mlsanity.com/paper/2608.06994/) (2026)
- [SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation](https://mlsanity.com/paper/2608.09771/) (2026)
- [Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning](https://mlsanity.com/paper/2608.09876/) (2026)
- [Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models](https://mlsanity.com/paper/2608.27259/) (2026)
- [AcrossWAM1.0:A Modular Latent World-Action Stack for Compact Robot Policies](https://mlsanity.com/paper/2608.29937/) (2026)
- [MoWAM: Explicit Future Motion Prediction for Efficient World Action Models](https://mlsanity.com/paper/2609.20709/) (2026)
- [SkelWAM: A Skeleton-Guided World-Action Model for Zero-Shot Cross-Embodiment Manipulation](https://mlsanity.com/paper/2609.21983/) (2026)
- [The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models](https://mlsanity.com/paper/2609.23369/) (2026)

## Abstract (arXiv)

Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoals to enable dynamics-aware robot control. LaWAM achieves state-of-the-art or competitive success rates (SRs) across LIBERO (98.6% SR), RoboTwin (91.22% SR), and real-world manipulation tasks while retaining low-latency inference. LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.