将生成式智能体引入具身多智能体系统，提升协作灵活性与鲁棒性。
Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
- 构建具身多智能体系统分类框架，涵盖架构与感知模态
- 证明生成式技术可增强感知、规划与通信的适应能力
- 适合关注AI协作、机器人系统与智能体交互的研究者
具身多智能体系统（EMAS）因其在物流、机器人等现实场景中应对复杂问题的潜力而日益受到关注。基础模型的进展使得生成式智能体具备更丰富的沟通能力和自适应求解能力。本文系统综述了生成式能力如何赋能EMAS。提出一种基于系统架构与具身模态的分类体系，强调协作在物理与虚拟环境中的融合。分析感知、规划、通信与反馈四大核心模块，说明生成式技术如何提升系统的鲁棒性与灵活性。通过具体案例展示将基础模型融入具身多智能体框架的变革性效果。最后讨论挑战与未来方向，强调EMAS在重塑人工智能协作格局上的巨大潜力。
原文摘要 · Abstract (English)
Embodied multi-agent systems (EMAS) have attracted growing attention for their potential to address complex, real-world challenges in areas such as logistics and robotics. Recent advances in foundation models pave the way for generative agents capable of richer communication and adaptive problem-solving. This survey provides a systematic examination of how EMAS can benefit from these generative capabilities. We propose a taxonomy that categorizes EMAS by system architectures and embodiment modalities, emphasizing how collaboration spans both physical and virtual contexts. Central building blocks, perception, planning, communication, and feedback, are then analyzed to illustrate how generative techniques bolster system robustness and flexibility. Through concrete examples, we demonstrate the transformative effects of integrating foundation models into embodied, multi-agent frameworks. Finally, we discuss challenges and future directions, underlining the significant promise of EMAS to reshape the landscape of AI-driven collaboration.
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