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Generation of Human-Aware Navigation Maps Using Graph Neural Networks

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Artificial Intelligence XXXVIII (SGAI-AI 2021)

Abstract

Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted. Graph Neural Networks can process representations including arbitrarily complex relationships between entities such as human interactions. This is particularly interesting in the context of social navigation, where relational information should be considered. This paper presents a model combining Graph Neural Network (GNN) and Convolutional Neural Network (CNN) layers to produce cost maps for human-aware navigation in real-time. The model leverages the relational inductive bias of GNNs to generate scenario representations that can be efficiently exploited using CNNs. In addition, a framework to bootstrap existing zero-dimensional models to generate cost map datasets is proposed. The model is evaluated against the original zero-dimensional dataset and in simulated navigation tasks. The results outperform similar state-of-the-art-methods considering the accuracy for the dataset and the navigation metrics used. The applications of the proposed framework are not limited to human-aware navigation, it could be applied to other fields where cost map generation is needed.

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Notes

  1. 1.

    https://github.com/gnns4hri/sngnn2d.

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Correspondence to Daniel Rodriguez-Criado .

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Rodriguez-Criado, D., Bachiller, P., Manso, L.J. (2021). Generation of Human-Aware Navigation Maps Using Graph Neural Networks. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-91100-3_2

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