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Social navigation datasets are necessary to assess social navigationalgorithms and train machine learning algorithms. Most of the currentlyavailable datasets target pedestrians' movements as a pattern to be replicatedby robots. It can be argued that one of the main reasons for this to happen isthat compiling datasets where real robots are manually controlled, as theywould be expected to behave when moving, is a very resource-intensive task.Another aspect that is often missing in datasets is symbolic information thatcould be relevant, such as human activities, relationships or interactions.Unfortunately, the available datasets targeting robots and supporting symbolicinformation are restricted to static scenes. This paper argues that simulationcan be used to gather social navigation data in an effective and cost-efficientway and presents a toolkit for this purpose. A use case studying theapplication of graph neural networks to create learned control policies usingsupervised learning is presented as an example of how it can be used.

A Toolkit to Generate Social Navigation Datasets