This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting allocation problem is combinatorial and is shown to be NP-hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot-level transfer decisions and next robot-to-team assignments. The proposed approach is validated in a firefighting scenario through simulations and experiments, demonstrating that the learned policy achieves near-optimal performance while scaling to larger systems.

翻译：本文通过动态机器人分配研究异构多团队协作，其中机器人被视为可转移资源。利用生态学中的汉密尔顿法则作为利他决策机制，我们提出了一种具有异构能力、转移成本以及依赖能力的贡献的多团队协作资源分配框架。由此产生的分配问题具有组合特性，并被证明是NP-难问题。为应对可扩展性挑战，我们开发了一种基于集中式训练与分散式执行的图神经网络策略，该策略根据汉密尔顿法则近似利他分配。该模型在团队交互图上运行，预测机器人级别的转移决策及后续机器人与团队的分配。通过消防场景的仿真与实验验证，所提方法表明，学习到的策略在扩展到更大系统时仍能实现接近最优的性能。