F1: A Vision-Language-Action Model Bridging Understanding and Generation
  to Actions
Authors: name: Qi Lv, name: Weijie Kong, name: Hao Li, name: Jia Zeng, name: Zherui Qiu, name: Delin Qu, name: Haoming Song, name: Qizhi Chen, name: Xiang Deng, name: Jiangmiao Pang

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.