用分步预测让多机器人自动协作，提升效率与默契。
Empowering Multi-Robot Cooperation via Sequential World Models
- 每台机器人按顺序预测前序动作，自主规划自身行为
- 在双机械手和多四足机器人上超越现有方法，样本效率更高
- 适合需要实时协作的物理机器人系统，支持真实部署
基于模型的强化学习（MBRL）因采样高效和具备规划能力，在机器人领域取得显著进展。然而，将MBRL扩展到物理多机器人协作仍面临联合动态建模复杂的问题。为此，我们提出序列世界模型（SeqWM），将序列化范式引入多机器人MBRL。SeqWM采用独立的自回归代理级世界模型来表示联合动态，每个代理基于前序代理的预测生成自身未来轨迹并规划行动。该设计降低建模复杂度，通过显式的意图共享催生高级协作行为。在Bi-DexHands和Multi-Quadruped上的实验表明，SeqWM在整体性能和样本效率上均优于现有最先进模型基和无模型基方法，展现出预测适应、时间对齐和角色分工等先进协作行为。此外，该方法已在真实四足机器人上成功部署，验证了其在真实多机器人系统中的有效性。演示与代码见：https://github.com/zhaozijie2022/seqwm
原文摘要 · Abstract (English)
Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical multi-robot cooperation remains challenging due to the complexity of joint dynamics. To address this challenge, we propose the Sequential World Model (SeqWM), a novel framework that integrates the sequential paradigm into multi-robot MBRL. SeqWM employs independent, autoregressive agent-wise world models to represent joint dynamics, where each agent generates its future trajectory and plans its actions based on the predictions of its predecessors. This design lowers modeling complexity and enables the emergence of advanced cooperative behaviors through explicit intention sharing. Experiments on Bi-DexHands and Multi-Quadruped demonstrate that SeqWM outperforms existing state-of-the-art model-based and model-free baselines in both overall performance and sample efficiency, while exhibiting advanced cooperative behaviors such as predictive adaptation, temporal alignment, and role division. Furthermore, SeqWM has been successfully deployed on physical quadruped robots, validating its effectiveness in real-world multi-robot systems. Demos and code are available at: https://github.com/zhaozijie2022/seqwm
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