# Bridge-WA: Predicting Where and How the World Changes for Robotic Action arXiv: https://arxiv.org/abs/2607.02195 · HTML: https://mlsanity.com/paper/2607.02195/ ## Key facts - Cited by 2 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 41 of its 85 references that are in the map. - First posted on arXiv 2 Jul 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, 30 of 41) - [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) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [World Models](https://mlsanity.com/paper/1803.10122/) (2018) - [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024) - [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025) - [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (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) - [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (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) - [CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation](https://mlsanity.com/paper/2411.19650/) (2024) - [UniVLA: Learning to Act Anywhere with Task-centric Latent Actions](https://mlsanity.com/paper/2505.06111/) (2025) - [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025) - [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) - [LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models](https://mlsanity.com/paper/2510.13626/) (2025) - [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026) - [Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control](https://mlsanity.com/paper/1812.00568/) (2018) - [Interactive Post-Training for Vision-Language-Action Models](https://mlsanity.com/paper/2505.17016/) (2025) - [NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks](https://mlsanity.com/paper/2504.19854/) (2025) - [StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing](https://mlsanity.com/paper/2604.05014/) (2026) - [GR-3 Technical Report](https://mlsanity.com/paper/2507.15493/) (2025) - [A Pragmatic VLA Foundation Model](https://mlsanity.com/paper/2601.18692/) (2026) - [ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning](https://mlsanity.com/paper/2602.11236/) (2026) - [FitVid: Overfitting in Pixel-Level Video Prediction](https://mlsanity.com/paper/2106.13195/) (2021) - [Gemini Robotics: Bringing AI into the Physical World](https://mlsanity.com/paper/2503.20020/) (2025) - [Being-H0.7: A Latent World-Action Model from Egocentric Videos](https://mlsanity.com/paper/2605.00078/) (2026) ## Built on by (most cited first, 2 of 2) - [PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution](https://mlsanity.com/paper/2607.16636/) (2026) - [SCULPT-VLA: Learning Structured Control through Staged Action Grounding](https://mlsanity.com/paper/2609.23275/) (2026) ## Abstract (arXiv) General-purpose VLA models leverage large-scale vision-language priors to understand scenes and instructions, but primarily generate actions directly from the current observation. WAMs further model future scene states, offering a broader view of how the environment may evolve. However, for robotic manipulation, predicting the entire future scene can introduce information beyond what is necessary for action generation; more importantly, effective actions require knowing not only what the scene may become, but also where and how relevant changes unfold. To bridge action generation with these action-relevant aspects of the world, we present Bridge-WA, a general world-action framework that learns complementary representations of future states, spatial changes, and local motion. Specifically, Bridge-WA consists of a Latent World Dynamics Module (LWDM) and WorldBridge. LWDM predicts future states, spatial changes, and local motion from VLM outputs, supervised by corresponding world targets. The WorldBridge are embedded into the action transformer and inject layer-specific combinations of these world priors through multi-source attention, spatiotemporal biases, and reliability-gated feature modulation. This design grounds action generation in action-relevant future dynamics while adaptively regulating world guidance, enabling robust generalization to visual disturbances and viewpoint shifts. We evaluate Bridge-WA on four simulation benchmarks and the real robots, where it achieves maximum success-rate improvements of 11.1%, 42.0%, 3.7%, 23.4%, and 11.1%, respectively, and achieves state-of-the-art average success rates on LIBERO-Dynamic, RoboTwin 2.0 and real-world. In particular, Bridge-WA demonstrates strong generalization to visual variations and viewpoint shifts in both simulation and real-world settings. Code and visualizations are available at: https://hcplab-sysu.github.io/BRIDGE-WA.