  Autonomous navigation is a key skill for assistive and service robots. To be
successful, robots have to navigate avoiding going through the personal spaces
of the people surrounding them. Complying with social rules such as not getting
in the middle of human-to-human and human-to-object interactions is also
important. This paper suggests using Graph Neural Networks to model how
inconvenient the presence of a robot would be in a particular scenario
according to learned human conventions so that it can be used by path planning
algorithms. To do so, we propose two ways of modelling social interactions
using graphs and benchmark them with different Graph Neural Networks using the
SocNav1 dataset. We achieve close-to-human performance in the dataset and argue
that, in addition to promising results, the main advantage of the approach is
its scalability in terms of the number of social factors that can be considered
and easily embedded in code, in comparison with model-based approaches. The
code used to train and test the resulting graph neural network is available in
a public repository.
