Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal

翻译：在人类共享环境中部署机器人需要深入理解附近智能体与物体间的交互机制。运用因果推断建模因果关系有助于预测人类行为，并支持机器人干预措施的预判。然而，由于现有因果发现方法缺乏在机器人学标准事实框架ROS生态系统中的实现，导致其难以在实际机器人上有效应用。为弥合这一差距，我们在先前工作中提出了ROS-Causal——一个基于ROS的框架，专为人机空间交互中的机载数据采集与因果发现而设计。本研究通过仿真实验和实验室场景下新型人机空间交互数据集的实测，对ROS-Causal进行系统性实验评估，以检验其性能与有效性。分析结果表明该方法具有显著效能，展示了机器人如何在数据采集过程中直接进行机载因果模型提取。从仿真生成的在线因果模型与实验室实验结果具有一致性。这些发现可通过以下路径帮助研究者提升共享环境中机器人系统的性能：首先在无真人参与的仿真环境中研究变量间因果关系，进而推动机器人在真实人类环境中的实际部署。ROS-Causal项目地址：https://lcastri.github.io/roscausal