In emerging multi-robot societies, heterogeneous agents must continually extract and integrate local knowledge from one another through communication, even when their internal models are completely opaque. Existing approaches to continual or collaborative learning for visual place recognition (VPR) largely assume white-box access to model parameters or shared training datasets, which is unrealistic when robots encounter unknown peers in the wild. This paper introduces \emph{Continual Communicative Learning (CCL)}, a data-free multi-robot framework in which a traveler robot (student) continually improves its VPR capability by communicating with black-box teacher models via a constrained query--response channel. We repurpose Membership Inference Attacks (MIA), originally developed as privacy attacks on machine learning models, as a constructive communication primitive to reconstruct pseudo-training sets from black-box VPR teachers without accessing their parameters or raw data. To overcome the intrinsic communication bottleneck caused by the low sampling efficiency of black-box MIA, we propose a prior-based query strategy that leverages the student's own VPR prior to focus queries on informative regions of the embedding space, thereby reducing the knowledge transfer (KT) cost. Experimental results on a standard multi-session VPR benchmark demonstrate that the proposed CCL framework yields substantial performance gains for low-performing robots under modest communication budgets, highlighting CCL as a promising building block for scalable and fault-tolerant multi-robot systems. Furthermore, we propose a Distributed Statistic Integration (DSI) framework that theoretically eliminates catastrophic forgetting by efficiently aggregating sufficient statistics from black-box VPR models while maintaining data privacy and reducing communication overhead to a sample-invariant constant complexity.

翻译：在兴起的多机器人社会中，异构智能体必须通过通信持续地从彼此提取并整合局部知识，即使它们的内部模型完全不透明。现有的视觉位置识别（VPR）持续学习或协作学习方法大多假设可以白盒访问模型参数或共享训练数据集，这在机器人在野外遇到未知同伴时是不现实的。本文介绍了\emph{持续通信学习（CCL）}，一个无数据的多机器人框架，其中旅行者机器人（学生）通过一个受限的查询-响应通道与黑盒教师模型进行通信，持续提升其VPR能力。我们重新利用了原本作为机器学习模型隐私攻击的成员推理攻击（MIA），将其作为一种建设性的通信原语，用于从黑盒VPR教师模型中重建伪训练集，而无需访问其参数或原始数据。为了克服由黑盒MIA低采样效率引起的固有通信瓶颈，我们提出了一种基于先验的查询策略，该策略利用学生自身的VPR先验，将查询集中在嵌入空间的信息丰富区域，从而降低知识转移（KT）成本。在一个标准的多会话VPR基准测试上的实验结果表明，所提出的CCL框架在适度的通信预算下，为性能较低的机器人带来了显著的性能提升，突显了CCL作为可扩展且容错的多机器人系统的一个有前景的构建模块。此外，我们提出了一个分布式统计集成（DSI）框架，该框架通过高效聚合来自黑盒VPR模型的充分统计量，理论上消除了灾难性遗忘，同时保持了数据隐私，并将通信开销降低到与样本无关的常数复杂度。