Look Ahead Together, Act Locally: Learning a Shared Predictive Brain for Multi-Robot Manipulation

Multi-robot manipulation requires robots to coordinate their actions while reliably performing their individual operations. Existing approaches often face a trade-off between decentralized policies that preserve per-robot controllers but lack global coordination context, and centralized policies that model team interactions but tightly couple coordination with joint action generation. In this paper, we introduce a multi-robot manipulation framework built on the principle of Look Ahead Together, Act Locally. Our method separates coordination from control through a shared predictive Brain and individual Action Experts. Given all robots' observations and a language instruction, the Brain learns a shared prediction of the collaborative future through alignment with future multi-view 3D features, and derives corresponding guidance for each Action Expert. The frozen Brain then guides independently adapted single-robot policies, enabling coordinated action while preserving their modularity. Experiments on RoboFactory demonstrate an 85.5% average success rate across six tasks, consistently outperforming decentralized and centralized baselines, while preserving a modular interface that enables individual experts to be replaced without retraining the Brain or other controllers.