# JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling

arXiv: https://arxiv.org/abs/2608.09381 · HTML: https://mlsanity.com/paper/2608.09381/

## Key facts

- Cited by 15 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 17 of its 22 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)
- Vision-language-action models / [World model-based VLA](https://mlsanity.com/vla/world-model-based-vla/)

## Builds on (most cited first, 17 of 17)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [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)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (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)
- [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models](https://mlsanity.com/paper/2510.13626/) (2025)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model](https://mlsanity.com/paper/2602.10098/) (2026)
- [FLARE: Robot Learning with Implicit World Modeling](https://mlsanity.com/paper/2505.15659/) (2025)
- [StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing](https://mlsanity.com/paper/2604.05014/) (2026)
- [LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies](https://mlsanity.com/paper/2606.15768/) (2026)
- [JEPA-VLA: Video Predictive Embedding is Needed for VLA Models](https://mlsanity.com/paper/2602.11832/) (2026)
- [FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment](https://mlsanity.com/paper/2602.17259/) (2026)
- [Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision](https://mlsanity.com/paper/2606.30552/) (2026)
- [PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance](https://mlsanity.com/paper/2604.20834/) (2026)

## Built on by (most cited first, 15 of 15)

- [World-Action Models for Robot Learning and Control: A Survey](https://mlsanity.com/paper/2609.16074/) (2026)
- [InternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data](https://mlsanity.com/paper/2609.31394/) (2026)
- [PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies](https://mlsanity.com/paper/2608.30378/) (2026)
- [CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models](https://mlsanity.com/paper/2609.18462/) (2026)
- [MoWAM: Explicit Future Motion Prediction for Efficient World Action Models](https://mlsanity.com/paper/2609.20709/) (2026)
- [Copper-Policy: Focus on the Representation for Robust Robot Manipulation](https://mlsanity.com/paper/2609.32779/) (2026)
- [DeltaWAM: Change-Centric Visual Foresight via Delta Tokens for an Efficient World-Action Model](https://mlsanity.com/paper/2609.33177/) (2026)
- [FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models](https://mlsanity.com/paper/2609.34362/) (2026)
- [One from Infinity: Actualizing Futures from Pretrained World Models into Robot Actions](https://mlsanity.com/paper/2609.36413/) (2026)
- [Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks](https://mlsanity.com/paper/2609.36471/) (2026)
- [V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents](https://mlsanity.com/paper/2609.37250/) (2026)
- [Magic-W0: A Structured World-Action Foundation Model for Physical Intelligence](https://mlsanity.com/paper/2609.39870/) (2026)
- [Native Action-Prior Learning from Videos for World Action Models](https://mlsanity.com/paper/2610.03391/) (2026)
- [UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models](https://mlsanity.com/paper/2610.18697/) (2026)
- [What Matters for World Modeling in Vision-Language-Action Models?](https://mlsanity.com/paper/2610.19825/) (2026)

## Abstract (arXiv)

Robust robot control benefits from explicitly modeling state transitions, but video-generation world action models (WAMs) introduce substantial deployment cost. Existing latent WAMs avoid explicit future generation, but often compress predictive representations or separate predictive modeling from the representations used for action generation. We introduce JEPA-WAM, a latent WAM built in a pretrained V-JEPA space, which couples latent transition prediction with continuous action generation through a shared predictor. JEPA-WAM predicts a spatially structured joint current-future target that captures task-shared visual temporal structure between current and future observations, while preserving dense patch-level correspondence. Through the shared predictor, transition supervision directly shapes the backbone, from which dedicated representations are extracted for action prediction. The same design can also be instantiated in pretrained VLA policies while preserving their original perception and action pathways. On LIBERO-Plus, JEPA-WAM achieves 79.2%, the best result without large-scale robot-policy pretraining, while its pretrained π0.5 instantiation reaches 86.3%, achieving the best overall performance. Experiments on RoboTwin 2.0 and real-world bimanual manipulation further demonstrate strong generalization under visual and spatial shifts.