Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
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 interactio...
| Authors | Riwa Karam, Ruoyu Lin, Brooks A. Butler et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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