arXiv cs.MA· Riwa Karam, Ruoyu Lin, Brooks A. Butler, Magnus Egerstedt·· 13 小时前AI 评分29
基于 Hamilton 规则学习异构多团队系统中的利他协作
Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
AI 导读
研究提出一种异构多团队协作资源分配框架，将机器人视为可转移资源，借用生态学 Hamilton 规则作为利他决策机制，并证明该异构资源分配问题为 NP-hard。为此作者开发了集中训练、分散执行的图神经网络策略，在团队交互图上预测机器人级转移决策与后续分配。灭火场景中，15 台机器人的五组物理测试台试验显示，GNN 五次均达到设定的灭火阈值，静态分配仅两次。
正文 · 原文
Abstract:This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where the robots themselves 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 general heterogeneous resource assignment 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 subsequent robot-to-team assignments. The proposed approach is evaluated in a fire-fighting scenario against exact optimization and multi-step simulations, quantifying allocation quality, decision computation, and fire suppression. Five paired physical testbed trials with 15 robots compare learned reallocation with fixed team membership; the GNN reaches the modeled fire stopping threshold in all five trials, compared with two for static allocation.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.21723 [cs.RO] |
| (or arXiv:2605.21723v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21723 arXiv-issued DOI via DataCite |
Submission history
From: Riwa Karam [view email]
[v1]
Wed, 20 May 2026 20:30:58 UTC (7,954 KB)
[v2]
Wed, 21 Oct 2026 08:52:16 UTC (8,445 KB)
来源：arXiv cs.MA · arxiv.org