Abstract
We present STORM (Search-Guided Generative World Models), a novel framework for spatio-temporal reasoning in robotic manipulation that unifies diffusion-based action generation, conditional video prediction, and search-based planning. Unlike prior Vision-Language-Action (VLA) models that rely on abstract latent dynamics or delegate reasoning to language components, STORM grounds planning in explicit visual rollouts, enabling interpretable and foresight-driven decision-making. A diffusion-based VLA policy proposes diverse candidate actions, a generative video world model simulates their visual and reward outcomes, and Monte Carlo Tree Search (MCTS) selectively refines plans through lookahead evaluation. Experiments on the SimplerEnv manipulation benchmark demonstrate that STORM achieves a new state-of-the-art average success rate of 51.0 percent, outperforming strong baselines such as CogACT. Reward-augmented video prediction substantially improves spatio-temporal fidelity and task relevance, reducing Frechet Video Distance by over 75 percent. Moreover, STORM exhibits robust re-planning and failure recovery behavior, highlighting the advantages of search-guided generative world models for long-horizon robotic manipulation.
Abstract (translated)
我们提出了一种名为STORM（基于搜索引导的生成式世界模型）的新框架，该框架用于机器人操作中的时空推理。STORM将扩散动作生成、条件视频预测和基于搜索的规划统一起来。与之前的视觉-语言-行动（VLA）模型不同，这些模型依赖于抽象潜在动力学或将推理委托给语言组件，STORM通过显式的视觉展开来实现计划制定，从而支持可解释且具有前瞻性驱动决策能力的操作。 具体来说，一个基于扩散的VLA策略提出多样化的候选动作，生成式视频世界模型模拟了它们的视觉和奖励结果，而蒙特卡洛树搜索（MCTS）则通过前瞻评估选择性地优化计划。在SimplerEnv操作基准测试中进行的实验表明，STORM达到了新的最先进平均成功率51.0%，优于诸如CogACT等强大的基线模型。 奖励增强型视频预测显著提高了时空准确性和任务相关性，将Frechet视频距离减少了超过75%。此外，STORM展示了稳健的重新规划和故障恢复行为，强调了搜索引导生成式世界模型在长时域机器人操作中的优势。
URL
https://arxiv.org/abs/2512.18477
