TLDR
潜变量世界模型在预测未来状态和在真实世界中规划方面表现出色。然而在实践中，我们缺乏一种原则性的方法来估计其能力如何随模型规模、数据和算力扩展，这一开放问题减缓了该领域进展。本工作提出 RoboJEPA，一种基于联合嵌入预测架构 (JEPA) 的世界模型，在涵盖 12 种机器人形态的大规模数据集上训练。我们表明 RoboJEPA 的想象误差——其潜变量推演的误差——遵循关于算力的二阶幂律，使我们能够预先Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a world model based on the Joint Embedding Predictive Architecture (JEPA) and trained on a large-scale dataset spanning 12 robotic embodiments. We show that RoboJEPA's imagination error, the error of its latent rollouts, follows a second-order power law in compute, allowing us to pre