让机器人自主组队完成任务，靠本地信息实时调整合作策略。
Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation
- 用空间动作图+意图共享，让机器人局部决策组队
- 仿真验证可处理大规模机器人和多类型任务
- 适合需要动态协作的多机器人系统场景
我们提出一种去中心化的学习框架，用于多机器人任务分配中的动态联盟形成。该方法在MAPPO基础上引入空间动作图、机器人运动规划、意图共享和任务分配修订机制，实现高效自适应的联盟组建。大量仿真实验表明，每个机器人仅依赖本地信息即可学习及时更新任务选择，并与其他机器人协同完成任务。结果还表明该框架具备处理大规模机器人群体及多样化任务场景的能力。
原文摘要 · Abstract (English)
We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends MAPPO by integrating spatial action maps, robot motion planning, intention sharing, and task allocation revision to enable effective and adaptive coalition formation. Extensive simulation studies confirm the effectiveness of our model, enabling each robot to rely solely on local information to learn timely revisions of task selections and form coalitions with other robots to complete collaborative tasks. The results also highlight the proposed framework's ability to handle large robot populations and adapt to scenarios with diverse task sets.
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