Graph Neural Networks for Human-aware Social Navigation

Luis J. Manso*, Ronit R. Jorvekar, Diego Faria, Pablo Bustos, Pilar Bachiller

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to comply with social rules, such as avoiding the personal spaces of the people surrounding them, or not getting in the way of human-to-human and human-to-object interactions. 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 automated scenario-to-graph transformations 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 its 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.
Original languageEnglish
Title of host publicationAdvances in Physical Agents II - Proceedings of the 21st International Workshop of Physical Agents WAF 2020
EditorsLuis M. Bergasa, Manuel Ocaña, Rafael Barea, Elena López-Guillén, Pedro Revenga
Number of pages13
ISBN (Electronic)978-3-030-62579-5
ISBN (Print)978-3-030-62578-8
Publication statusPublished - 3 Nov 2020
Event21st International Workshop of Physical Agents (WAF2020) - Madrid, Spain
Duration: 19 Nov 202020 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference21st International Workshop of Physical Agents (WAF2020)

Bibliographical note

© 2020 The Authors


  • Graph Neural Networks
  • Human-robot interaction
  • Social navigation


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  • Best Paper Award

    Gilliland, David (Recipient) & Mooi, Erik A (Recipient), 2008

    Prize: Prize (including medals and awards)

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