This study identifies a gap in data-driven approaches to robot-centric pedestrian interactions and proposes a corresponding pipeline. The pipeline utilizes unsupervised learning techniques to identify patterns in interaction data of urban environments, specifically focusing on conflict scenarios. Analyzed features include the robot's and pedestrian's speed and contextual parameters such as proximity to intersections. They are extracted and reduced in dimensionality using Principal Component Analysis (PCA). Finally, K-means clustering is employed to uncover underlying patterns in the interaction data. A use case application of the pipeline is presented, utilizing real-world robot mission data from a mid-sized German city. The results indicate the need for enriching interaction representations with contextual information to enable fine-grained analysis and reasoning. Nevertheless, they also highlight the need for expanding the data set and incorporating additional contextual factors to enhance the robots situational awareness and interaction quality.

翻译：本研究识别了数据驱动方法在机器人中心的行人交互中的空白，并提出相应的处理流程。该流程利用无监督学习技术来识别城市环境交互数据中的模式，特别关注冲突场景。分析的特征包括机器人和行人的速度以及上下文参数（如与交叉口的距离）。这些特征通过主成分分析（PCA）进行提取和降维。最后，采用K-means聚类来揭示交互数据中的潜在模式。本文展示了该流程的实际应用案例，使用了来自德国中型城市真实机器人任务数据。结果表明，需要利用上下文信息丰富交互表征，以实现细粒度分析和推理。然而，研究也强调需要扩展数据集并纳入更多上下文因素，以提升机器人的情境感知能力和交互质量。