Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

Engineering notes

The proposed approach is applicable in scenarios requiring efficient resource allocation in multi-team robotic systems, such as disaster response and firefighting. It emphasizes learnability from past interactions, enhancing real-time decision-making capabilities.

Chinese explanation / 中文解读

本文研究异构多团队协作中的动态机器人分配，提出了一种将机器人视为可转移资源的框架。基于生态学中的汉密尔顿法则，提出了一种多团队协作资源分配框架，考虑到异构能力、转移成本和能力相关的贡献。通过图神经网络政策，预计在消防场景中的机器人转移决策，实现高效的分配。

Original abstract

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.

Links and sources

• Official / arXiv page
• PDF from original source

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