  Deploying robots in human-shared spaces requires understanding interactions
among nearby agents and objects. Modelling cause-and-effect relations through
causal inference aids in predicting human behaviours and anticipating robot
interventions. However, a critical challenge arises as existing causal
discovery methods currently lack an implementation inside the ROS ecosystem,
the standard de facto in robotics, hindering effective utilisation in robotics.
To address this gap, this paper introduces ROS-Causal, a ROS-based framework
for onboard data collection and causal discovery in human-robot spatial
interactions. An ad-hoc simulator, integrated with ROS, illustrates the
approach's effectiveness, showcasing the robot onboard generation of causal
models during data collection. ROS-Causal is available on GitHub:
https://github.com/lcastri/roscausal.git.
