Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

[2605.21723] Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

[Submitted on 20 May 2026]

Title:Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

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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.

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:2605.21723v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.21723

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Riwa Karam [view email] [v1] Wed, 20 May 2026 20:30:58 UTC (7,954 KB)

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