A Graph Neural Network to Model Disruption in Human-aware Robot Navigation


Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people’s paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 (Manso et al 2020) which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository (https://github.com/gnns4hri/sngnnv2).

Publication DOI: https://doi.org/10.1007/s11042-021-11113-6
Divisions: College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Funding: This work has partly been supported by grant RTI2018099522-BC42, from the Spanish Government, and by grants GR18133 and IB18056, from the Government of Extremadura.
Uncontrolled Keywords: Graph neural networks,Human-robot interaction,Social navigation,Software,Media Technology,Hardware and Architecture,Computer Networks and Communications
Publication ISSN: 1573-7721
Last Modified: 16 Apr 2024 07:31
Date Deposited: 22 Jun 2021 07:19
Full Text Link:
Related URLs: https://link.sp ... 042-021-11113-6 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-06-19
Published Online Date: 2021-06-19
Accepted Date: 2021-05-25
Authors: Bachiller, Pilar
Rodriguez-Criado, Daniel
Jorvekar, Ronit R.
Bustos, Pablo
Faria, Diego (ORCID Profile 0000-0002-2771-1713)
Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)



Version: Published Version

License: Creative Commons Attribution

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