  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.
