Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales

2026-07-06

AI Summary

This work addresses the challenge of enabling multi-robot systems to simultaneously adapt to unknown environments, unfamiliar teammates, and dynamically varying team sizes in open-world settings. To this end, it proposes the first hypergraph game–based open-adaptive teaming framework, which models team-level non-pairwise collaborative relationships through hypergraphs to support structural reasoning under dynamic team compositions. The framework incorporates a progressive diversity-aware training mechanism—termed HOLA—that enables zero-shot policy transfer across environments, partners, and team scales without fine-tuning. Experimental results demonstrate that the proposed approach significantly outperforms existing baselines in cooperative pursuit tasks involving multiple drones and legged robots, with policies directly deployable on Crazyflie and Zsibot L1 hardware platforms to achieve robust collaboration in entirely unseen scenarios.

Abstract

Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions, providing a principled foundation for coordination structure inference when team composition changes dynamically within episodes. Unlike graph neural network architectures, this is a game-theoretic construct for modeling strategic interactions and payoff structures among agents. Building on this formulation, we develop the Hypergraphic Open-ended Learning Algorithm (HOLA), which progressively expands partner and environment diversity during training rather than optimizing for fixed configurations. Evaluated on cooperative pursuit with multi-drone and multi-quadruped platforms, HOLA outperforms all baselines across all three adaptability dimensions. Learned policies transfer directly to physical hardware without fine-tuning, with successful deployments on Crazyflie and Zsibot L1 platforms confirming robust real-world coordination in novel environments with unseen teammates.

Problem

• open adaptive teaming
• unseen environments
• unknown partners
• varying team sizes
• multi-robot coordination

Innovation

• open adaptive teaming
• hypergraphic game
• multi-robot coordination
• zero-shot transfer
• dynamic team composition