SeqWM: Empowering Multi-Robot Cooperation via Sequential World Models

To address the difficulty of applying model-based reinforcement learning (MBRL) to multi-robot systems, we propose the Sequential World Model (SeqWM). This framework decomposes complex joint dynamics by using independent, sequentially-structured models for each agent. Planning and decision-making occur via sequential communication, where each agent bases its actions on the predictions of its predecessors. This design enables explicit intention sharing, boosts cooperative performance, and reduces communication complexity. Results show SeqWM outperforms state-of-the-art methods in simulations and real-world deployments, achieving advanced behaviors like predictive adaptation and role division.