Multi-agent collaboration enhances the perception capabilities of individual agents through information sharing. However, in real-world applications, differences in sensors and models across heterogeneous agents inevitably lead to domain gaps during collaboration. Existing approaches based on adaptation and reconstruction fail to support pragmatic heterogeneous collaboration due to two key limitations: (1) Intrusive retraining of the encoder or core modules disrupts the established semantic consistency among agents; and (2) accommodating new agents incurs high computational costs, limiting scalability. To address these challenges, we present a novel Generative Communication mechanism (GenComm) that facilitates seamless perception across heterogeneous multi-agent systems through feature generation, without altering the original network, and employs lightweight numerical alignment of spatial information to efficiently integrate new agents at minimal cost. Specifically, a tailored Deformable Message Extractor is designed to extract spatial message for each collaborator, which is then transmitted in place of intermediate features. The Spatial-Aware Feature Generator, utilizing a conditional diffusion model, generates features aligned with the ego agent's semantic space while preserving the spatial information of the collaborators. These generated features are further refined by a Channel Enhancer before fusion. Experiments conducted on the OPV2V-H, DAIR-V2X and V2X-Real datasets demonstrate that GenComm outperforms existing state-of-the-art methods, achieving an 81% reduction in both computational cost and parameter count when incorporating new agents. Our code is available at https://github.com/jeffreychou777/GenComm.

翻译：多智能体协作通过信息共享提升个体智能体的感知能力。然而，在实际应用中，异构智能体间传感器与模型的差异不可避免地导致协作过程中的领域鸿沟。现有基于适应与重构的方法因两个关键局限而无法支持实用的异构协作：(1) 对编码器或核心模块的侵入式再训练破坏了智能体间已建立的语义一致性；(2) 接纳新智能体需付出高昂计算成本，限制了可扩展性。为解决这些挑战，我们提出一种新颖的生成式通信机制（GenComm），通过特征生成促进异构多智能体系统的无缝感知，无需修改原始网络，并采用轻量级空间信息数值对齐以极低成本高效集成新智能体。具体而言，我们设计了定制的可变形消息提取器，为每个协作者提取空间消息，并以此替代中间特征进行传输。空间感知特征生成器利用条件扩散模型，生成与自身智能体语义空间对齐的特征，同时保留协作者的空间信息。这些生成的特征在融合前通过通道增强器进一步优化。在OPV2V-H、DAIR-V2X和V2X-Real数据集上的实验表明，GenComm优于现有最先进方法，在集成新智能体时实现了计算成本与参数数量81%的同步降低。我们的代码公开于https://github.com/jeffreychou777/GenComm。