跨未知环境、伙伴和规模的多机器人开放式自适应协作
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
AI总结：
研究多机器人在未知环境、伙伴及规模下的协作，提出超图形式博弈公式，开发HOLA算法，能在训练中扩展多样性，在多平台实验中性能优于基线，策略可直接部署到硬件。
摘要：
在现实世界中部署机器人团队需要同时适应未知环境、未知伙伴和不同的团队规模。我们将此形式化为开放式自适应多机器人协作，并提出一种超图形式的博弈公式，为情节内团队组成动态变化时的协调结构推理提供了原则基础。在此基础上，我们开发了超图开放式学习算法（HOLA），在多无人机和多四足机器人平台的合作追踪评估中，HOLA在所有三个适应性维度上均优于所有基线。学习到的策略无需微调即可直接转移到物理硬件上，在Crazyflie和Zsibot L1平台上的成功部署证实了在具有未知队友的新环境中强大的现实世界协调能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
