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