DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
On the map: Vision-language-action models → World model-based VLA
Abstract (arXiv)
Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited to challenging image-based forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information. To address these limitations, we propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling, thereby establishing a perception-prediction-action loop for manipulation tasks. Specifically, DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning. This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting. To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action representations from shared latent features. Extensive experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.
Key facts. Cited by 203 papers in the map (data through 7 Oct 2026). Placed in World model-based VLA, one of 13 directions in Vision-language-action models. Builds on 132 of its 151 references that are in the map. First posted on arXiv 6 Jul 2025.
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Other references · 131
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- 2024CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulationcited by 411
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50 most cited of 131 All 131 (JSON)
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Citing papers per year: 2025: 37; 2026: 166.
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- 2026Fast-WAM: Do World Action Models Need Test-time Future Imagination?cited by 311
- 2025VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Modelcited by 140
- 2026GigaWorld-Policy: An Efficient Action-Centered World--Action Modelcited by 129
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38 more
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- 2026LaST0: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Modelcited by 28
- 2026ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queriescited by 28
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- 2025Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Surveycited by 25
- 2026BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generationcited by 25
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- 2026Towards Generalizable Robotic Manipulation in Dynamic Environmentscited by 17
- 2026VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Modelscited by 17
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50 most cited of 203 All 203 (JSON)
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- 2026Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model30 Sep 2026
- 2026Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts30 Sep 2026
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- 2026MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies30 Sep 2026
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- 2026Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning29 Sep 2026
- 2026Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation28 Sep 2026
- 2026FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models28 Sep 2026
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50 newest of 203 All 203 (JSON)
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- 2026ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queriescited by 28
- 2026When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAscited by 27
- 2026BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generationcited by 25
- 2025Mixture of Horizons in Action Chunkingcited by 20
- 2026PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulationcited by 20
- 2025QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Modelscited by 19
- 2026Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulationcited by 19
- 2026Towards Generalizable Robotic Manipulation in Dynamic Environmentscited by 17
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12 most cited of 101 All 101 (JSON)
- 2026ATI-VLA: Action-Centric Predictive Vision-Language-Action Models via Actionable Alignment Then Adaptive Injection1 Oct 2026
- 2026Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model30 Sep 2026
- 2026Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts30 Sep 2026
- 2026Spike-driven Vision-Language-Action Model30 Sep 2026
- 2026Beyond Token Importance: Preserving Spatial Scaffolds for Efficient Vision-Language-Action Inference29 Sep 2026
- 2026Where Predictive Supervision Goes Shapes What VLA Policies Learn29 Sep 2026
- 2026Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning29 Sep 2026
- 2026Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation28 Sep 2026
- 2026SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models27 Sep 2026
- 2026SCULPT-VLA: Learning Structured Control through Staged Action Grounding20 Sep 2026
- 2026UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling10 Sep 2026
- 2026GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation7 Sep 2026
12 newest of 101 All 101 (JSON)
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- 2025Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Modelcited by 33
- 2026ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?cited by 33
- 2025AstraNav-World: World Model for Foresight Control and Consistencycited by 17
- 2026Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulationcited by 17
- 2025A Step Toward World Models: A Survey on Robotic Manipulationcited by 15
- 2025NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Predictioncited by 15
12 most cited of 49 All 49 (JSON)
- 2026MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies30 Sep 2026
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- 2026FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models28 Sep 2026
- 2026ReSync: Re-Aligning the Two Clocks of Asynchronous World-Action Models27 Sep 2026
- 2026Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models6 Sep 2026
- 2026Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models3 Sep 2026
- 2026Keep the Future, Drop the Rollout: RIFT for World Action Models12 Aug 2026
- 2026World Tokens: Enhancing Embodied Policies with Training-Time World Modeling10 Aug 2026
- 2026SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models9 Aug 2026
- 2026Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models7 Aug 2026
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12 newest of 49 All 49 (JSON)
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12 most cited of 19 All 19 (JSON)
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12 newest of 19 All 19 (JSON)
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