Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.

翻译：为在仓库、购物中心或医院等人机共存环境中部署机器人，需使其理解周围智能体与物体间潜在的物理交互。特别是，建模后者间的因果关系有助于预测未观测到的人类行为，并预判特定机器人干预的后果。本文提出将因果发现方法应用于人机空间交互建模，试图从真实传感器数据中理解两种场景下的人类行为：人类与环境交互及人类与障碍物交互。我们讨论了利用当前最先进因果发现算法在复杂人类环境中应用的新方法与实践方案，该算法在众多服务机器人场景中具有潜在应用价值。为验证从真实数据集中获取的因果模型的实用性，我们对比了因果与非因果预测方法。结果表明，因果模型能正确捕捉所考虑场景的底层交互机制，并提升预测准确性。