  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
