  Adapting to social conventions is an unavoidable requirement for the
acceptance of assistive and social robots. While the scientific community
broadly accepts that assistive robots and social robot companions are unlikely
to have widespread use in the near future, their presence in health-care and
other medium-sized institutions is becoming a reality. These robots will have a
beneficial impact in industry and other fields such as health care. The growing
number of research contributions to social navigation is also indicative of the
importance of the topic. To foster the future prevalence of these robots, they
must be useful, but also socially accepted. The first step to be able to
actively ask for collaboration or permission is to estimate whether the robot
would make people feel uncomfortable otherwise, and that is precisely the goal
of algorithms evaluating social navigation compliance. Some approaches provide
analytic models, whereas others use machine learning techniques such as neural
networks. This data report presents and describes SocNav1, a dataset for social
navigation conventions. The aims of SocNav1 are two-fold: a) enabling
comparison of the algorithms that robots use to assess the convenience of their
presence in a particular position when navigating; b) providing a sufficient
amount of data so that modern machine learning algorithms such as deep neural
networks can be used. Because of the structured nature of the data, SocNav1 is
particularly well-suited to be used to benchmark non-Euclidean machine learning
algorithms such as Graph Neural Networks (see [1]). The dataset has been made
available in a public repository.
