Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects. Specifically, creating models to represent cause-and-effect relationships among these elements can aid in predicting unforeseen human behaviours and anticipate the outcome of particular robot actions. To be suitable for robots, causal analysis must be both fast and accurate, meeting real-time demands and the limited computational resources typical in most robotics applications. In this paper, we present a practical demonstration of our approach for fast and accurate causal analysis, known as Filtered PCMCI (F-PCMCI), along with a real-world robotics application. The provided application illustrates how our F-PCMCI can accurately and promptly reconstruct the causal model of a human-robot interaction scenario, which can then be leveraged to enhance the quality of the interaction.

翻译：在仓库、购物中心或医院等人类共享环境中使用机器人执行自动化任务，要求机器人理解周围智能体与物体之间的基本物理交互。具体而言，建立这些要素间因果关系模型有助于预测不可预见的人类行为并预判特定机器人动作的结果。为满足机器人应用需求，因果分析必须兼具快速性与准确性，以应对实时性要求及大多数机器人应用中典型的有限计算资源约束。本文通过名为滤波型PCMCI（F-PCMCI）的快速准确因果分析方法，展示了该方法的实际演示及其实世界机器人应用案例。所提供的应用案例表明，F-PCMCI能够准确高效地重建人机交互场景的因果模型，进而用于提升交互质量。