Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

2026-05-20

AI Summary

This work addresses the dynamic robot resource allocation problem in heterogeneous multi-team systems by proposing an altruistic collaboration mechanism grounded in Hamilton’s rule from evolutionary ecology—a principle introduced here for the first time to multi-team resource allocation. The approach employs a graph neural network policy trained centrally but executed in a decentralized manner, jointly modeling heterogeneity in robot capabilities, transition costs, and interdependencies in team contributions to yield scalable, near-optimal allocation decisions. Evaluation in a fire-rescue simulation environment demonstrates that the method achieves near-optimal performance while effectively enabling efficient collaboration in large-scale, heterogeneous multi-agent systems.

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.

Problem

• heterogeneous multi-team systems
• altruistic collaboration
• dynamic robot allocation
• resource allocation
• Hamilton's rule

Innovation

• Hamilton's rule
• heterogeneous multi-team systems
• graph neural network
• resource allocation
• altruistic collaboration