World4Omni: A Zero-Shot Framework from Image Generation World Model to Robotic Manipulation
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Improving data efficiency and generalization in robotic manipulation remains
a core challenge. We propose a novel framework that leverages a pre-trained
multimodal image-generation model as a world model to guide policy learning. By
exploiting its rich visual-semantic representations and strong generalization
across diverse scenes, the model generates open-ended future state predictions
that inform downstream manipulation. Coupled with zero-shot low-level control
modules, our approach enables general-purpose robotic manipulation without
task-specific training. Experiments in both simulation and real-world
environments demonstrate that our method achieves effective performance across
a wide range of manipulation tasks with no additional data collection or
fine-tuning. Supplementary materials are available on our website:
https://world4omni.github.io/.
