让无人机在没合作过的情况下，自动协同抓捕多个逃逸目标。
HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
- 用超图建模多机协作，动态调整学习目标以适应未知伙伴。
- 在两种未知队友池中测试，均显著优于基线方法。
- 首次实现真实无人机系统的零样本协同抓捕，可落地应用。
零样本协调（ZSC）是多智能体协作中的关键挑战，旨在训练出能与未曾接触过的伙伴协同的智能体。现有先进方法主要聚焦于双人视频游戏如OverCooked!2和Hanabi。本文将ZSC研究拓展至多无人机协同追捕场景，探索如何构建能与多个未知队友协作捕获多个逃逸目标的无人机智能体。提出一种新型超图式开放持续学习算法（HOLA-Drone），基于超图形式的游戏建模，持续调整学习目标以提升与多未知队友的协作能力。为验证有效性，构建两个不同的未知无人机队友池，评估其与多种未知伙伴的协作表现。实验结果表明，HOLA-Drone在与未知队友协作方面显著优于基线方法。此外，真实世界实验验证了HOLA-Drone在物理系统中的可行性。视频可在项目主页查看：https://sites.google.com/view/hola-drone。
原文摘要 · Abstract (English)
Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered before. Recent cutting-edge ZSC methods have primarily focused on two-player video games such as OverCooked!2 and Hanabi. In this paper, we extend the scope of ZSC research to the multi-drone cooperative pursuit scenario, exploring how to construct a drone agent capable of coordinating with multiple unseen partners to capture multiple evaders. We propose a novel Hypergraphic Open-ended Learning Algorithm (HOLA-Drone) that continuously adapts the learning objective based on our hypergraphic-form game modeling, aiming to improve cooperative abilities with multiple unknown drone teammates. To empirically verify the effectiveness of HOLA-Drone, we build two different unseen drone teammate pools to evaluate their performance in coordination with various unseen partners. The experimental results demonstrate that HOLA-Drone outperforms the baseline methods in coordination with unseen drone teammates. Furthermore, real-world experiments validate the feasibility of HOLA-Drone in physical systems. Videos can be found on the project homepage~\url{https://sites.google.com/view/hola-drone}.
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