F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
On the map: Vision-language-action models → World model-based VLA
Abstract (arXiv)
Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modules for perception, foresight generation, and control, thereby bridging understanding, generation, and actions. At its core, F1 employs a next-scale prediction mechanism to synthesize goal-conditioned visual foresight as explicit planning targets. By forecasting plausible future visual states, F1 reformulates action generation as a foresight-guided inverse dynamics problem, enabling actions that implicitly achieve visual goals. To endow F1 with robust and generalizable capabilities, we propose a three-stage training recipe on an extensive dataset comprising over 330k trajectories across 136 diverse tasks. This training scheme enhances modular reasoning and equips the model with transferable visual foresight, which is critical for complex and dynamic environments. Extensive evaluations on real-world tasks and simulation benchmarks demonstrate F1 consistently outperforms existing approaches, achieving substantial gains in both task success rate and generalization ability.
Key facts. Cited by 85 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 41 of its 53 references that are in the map. First posted on arXiv 8 Sep 2025.
Builds on
41 of 53 references.
Closest ancestors · 1
Same direction, newest first
Other references · 40
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Citing papers per year: 2025: 12; 2026: 73.
85 papers.
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12 most cited of 36 All 36 (JSON)
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- 2026Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model11 Sep 2026
- 2026DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation6 Aug 2026
- 2026In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use6 Aug 2026
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12 newest of 36 All 36 (JSON)
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12 most cited of 28 All 28 (JSON)
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- 2026Flex-π: A Multi-Stream World-Action Model with Compute Flexibility11 Aug 2026
- 2026Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models7 Aug 2026
- 2026Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control29 Jul 2026
- 2026RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation21 Jul 2026
- 2026From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence13 Jul 2026
- 2026ABot-M0.5: Unified Mobility-and-Manipulation World Action Model1 Jul 2026
- 2026World Action Models: A Survey18 Jun 2026
- 2026ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?17 Jun 2026
12 newest of 28 All 28 (JSON)
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- 2025ManualVLA: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulationcited by 13
- 2026HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoningcited by 13
- 2025Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-trainingcited by 7
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- 2026HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning24 Feb 2026
- 2026Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models1 Feb 2026
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- 2025Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training30 Dec 2025
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- 2025Rethinking Intermediate Representation for VLM-based Robot Manipulation24 Nov 2025
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- 2026GEM: Generative Supervision Helps Embodied Intelligencecited by 6
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- 2026GEM: Generative Supervision Helps Embodied Intelligence27 May 2026
- 2026VLANeXt: Recipes for Building Strong VLA Models20 Feb 2026
- 2026ST4VLA: Spatially Guided Training for Vision-Language-Action Models10 Feb 2026
- 2026RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation10 Feb 2026
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- 2026Rewiring Semantics, Dynamics, and Control: A Simple yet Effective Action-Centric Tri-Stream Transformer8 Oct 2026
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- 2026Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation1 Jul 2026
- 2026UAM: A Dual-Stream Perspective on Forgetting in VLA Training15 May 2026
- 2026ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning11 Feb 2026
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