Abstract
We introduce a method for decentralized person re-identification in robot swarms that leverages natural language as the primary representational modality. Unlike traditional approaches that rely on opaque visual embeddings -- high-dimensional feature vectors extracted from images -- the proposed method uses human-readable language to represent observations. Each robot locally detects and describes individuals using a vision-language model (VLM), producing textual descriptions of appearance instead of feature vectors. These descriptions are compared and clustered across the swarm without centralized coordination, allowing robots to collaboratively group observations of the same individual. Each cluster is distilled into a representative description by a language model, providing an interpretable, concise summary of the swarm's collective perception. This approach enables natural-language querying, enhances transparency, and supports explainable swarm behavior. Preliminary experiments demonstrate competitive performance in identity consistency and interpretability compared to embedding-based methods, despite current limitations in text similarity and computational load. Ongoing work explores refined similarity metrics, semantic navigation, and the extension of language-based perception to environmental elements. This work prioritizes decentralized perception and communication, while active navigation remains an open direction for future study.
Abstract (translated)
我们提出了一种在机器人集群中进行去中心化人员再识别的方法，该方法利用自然语言作为主要的表现形式。与传统的依赖于不透明的视觉嵌入（从图像提取的高度维特征向量）的方法不同，所提出的方法使用人类可读的语言来表示观察结果。每个机器人本地检测并描述个体，并使用视觉-语言模型（VLM）生成外观的文字描述，而不是特征向量。这些描述在没有集中协调的情况下在整个集群中进行比较和聚类，使机器人能够协作地对同一个人的观察结果进行分组。每个聚类由一个语言模型提炼成一个代表性的描述，提供了一个可解释且简洁的群体感知总结。这种方法支持自然语言查询、增强透明度并支持可解释群集行为。初步实验表明，在身份一致性以及与基于嵌入的方法相比在可解释性方面表现出了竞争力，尽管目前文本相似性和计算负荷方面存在限制。正在进行的工作探索了更精细的相似度指标、语义导航以及将基于语言的感知扩展到环境元素的可能性。这项工作优先考虑去中心化感知和通信，而主动导航仍然是未来研究的一个开放方向。
URL
https://arxiv.org/abs/2601.12479
