Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

Riwa Karam; Ruoyu Lin; Brooks A. Butler; Magnus Egerstedt

cs.ROMay 20, 2026

Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

Authors: Riwa Karam, Ruoyu Lin, Brooks A. Butler, Magnus Egerstedt

Organizations: Samueli School of Engineering, University of California, Irvine, Irvine, CA, 92697, USA · University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

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.

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Jun 18, 2026 · cs.ROJ/K move · Enter open · S save

Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination

Esmaeil Seraj, Rohan Paleja, Luis Pimentel, Kin Man Lee+6

High-performing human-human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the different roles of heterogeneous team members and adapt their communication protocols accordingly. Multi-Agent Reinforcement Learning (MARL) has attempted to develop computational methods for synthesizing such joint coordination-communication strategies, but emulating heterogeneous communication patterns across agents with different state, action, and observation spaces has remained a challenge. Without properly modeling agent heterogeneity, as in prior MARL work that leverages homogeneous graph networks, communication becomes less helpful and can even deteriorate the team's performance. In the past, we proposed Heterogeneous Policy Networks (HetNet) to learn efficient and diverse communication models for coordinating cooperative heterogeneous teams. In this extended work, we extend Heterogeneous Policy Networks (HetNet) to support scaling heterogeneous robot teams. Building on heterogeneous graph-attention networks, we show that HetNet not only facilitates learning heterogeneous collaborative policies but also enables end-to-end training for learning highly efficient binarized messaging. Our empirical evaluation shows that HetNet sets a new state of the art in learning coordination and communication strategies for heterogeneous multi-agent teams by achieving an 5.84% to 707.65% performance improvement over the next-best baseline across multiple domains while simultaneously achieving a 200x reduction in the required communication bandwidth.

Multi-Agent Reinforcement Learning Multi-Robot Systems Global Coordination Coordination Communication Heterogeneity

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Abstract

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