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Generation of Human-Aware Navigation Maps Using Graph Neural Networks

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Artificial Intelligence XXXVIII (SGAI-AI 2021)


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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  1. 1.


  1. Bachiller, P., Rodriguez-Criado, D., Jorvekar, R.R., Bustos, P., Faria, D.R., Manso, L.J.: A graph neural network to model disruption in human-aware robot navigation. Multimedia Tools Appl. (2021).

  2. Baghel, R., Kapoor, A., Bachiller, P., Jorvekar, R.R., Rodriguez-Criado, D., Manso, L.J.: A toolkit to generate social navigation datasets (2020). arXiv preprint arXiv:2009.05345

  3. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks, pp. 1–40 (2018). arXiv: 1806.01261

  4. Charalampous, K., Kostavelis, I., Gasteratos, A.: Recent trends in social aware robot navigation: a survey. Rob. Auton. Syst. 93, 85–104 (2017)

    Article  Google Scholar 

  5. Chen, C., Hu, S., Nikdel, P., Mori, G., Savva, M.: Relational graph learning for crowd navigation (2019). arXiv preprint arXiv:1909.13165

  6. Chen, T.L., et al.: Robots for humanity: using assistive robotics to empower people with disabilities. IEEE Rob. Autom. Mag. 20(1), 30–39 (2013)

    Article  Google Scholar 

  7. Chen, Y., Liu, C., Shi, B.E., Liu, M.: Robot navigation in crowds by graph convolutional networks with attention learned from human gaze. IEEE Rob. Autom. Lett 5(2), 2754–2761 (2020)

    Article  Google Scholar 

  8. Chen, Y.F., Everett, M., Liu, M., How, J.P.: Socially aware motion planning with deep reinforcement learning. In: IEEE International Conference on Intelligent Robots and Systems 2017-September, pp. 1343–1350 (2017)

    Google Scholar 

  9. Ferrer, G., Sanfeliu, A.: Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1730–1735 (2014)

    Google Scholar 

  10. Gross, H.M., Scheidig, A., Müller, S., Schütz, B., Fricke, C., Meyer, S.: Living with a mobile companion robot in your own apartment - final implementation and results of a 20-weeks field study with 20 seniors. In: Proceedings - IEEE International Conference on Robotics and Automation 2019-May, pp. 2253–2259 (2019)

    Google Scholar 

  11. Hall, E.T.: The hidden Dimension: Man’s Use of Space in Public and Private. The Bodley Head Ltd., London (1966)

    Google Scholar 

  12. van der Heiden, T., Weiss, C., Shankar, N.N., van Hoof, H.: Social navigation with human empowerment driven reinforcement learning (2020). arXiv preprint arXiv:2003.08158

  13. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)

    Article  Google Scholar 

  14. Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Rob. Auton. Syst. 61(12), 1726–1743 (2013)

    Article  Google Scholar 

  15. LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: Progress and prospects. Algorithmic Comput. Rob. Direct. 5, 293–308 (2001)

    MATH  Google Scholar 

  16. Manso, L.J., Jorvekar, R.R., Faria, D.R., Bustos, P., Bachiller, P.: Graph Neural Networks for Human-aware Social Navigation, pp. 1–6 (2019). arXiv preprint arXiv:1909.09003

  17. Manso, L.J., Nuñez, P., Calderita, L.V., Faria, D.R., Bachiller, P.: Socnav1: a dataset to benchmark and learn social navigation conventions. Data 5(1) (2020).

  18. Ng, A.Y., Russell, S.J., et al.: Algorithms for inverse reinforcement learning. In: ICML, vol. 1, p. 2 (2000)

    Google Scholar 

  19. Nilsson, N.J.: Principles of Artificial Intelligence. Morgan Kaufmann, Burlington (2014)

    MATH  Google Scholar 

  20. Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y.: Learning proxemics for personalized human-robot social interaction. Int. J. Soc. Rob. 12, 267–280 (2019)

    Article  Google Scholar 

  21. Pérez-Higueras, N., Caballero, F., Merino, L.: Learning human-aware path planning with fully convolutional networks. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 5897–5902 (2018)

    Google Scholar 

  22. Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware navigation: a survey. Int. J. Soc. Rob. 7(2), 137–153 (2014).

    Article  Google Scholar 

  23. Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M., et al.: Modeling relational data with graph convolutional networks. In: Gangemi, A. (ed.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018).

    Chapter  Google Scholar 

  24. Sun, S., Zhao, X., Li, Q., Tan, M.: Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. Adv. Rob. 34(13), 888–901 (2020)

    Article  Google Scholar 

  25. Truong, X., Ngo, T.D.: Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Trans. Autom. Sci. Eng. 14(4), 1743–1760 (2017)

    Article  Google Scholar 

  26. Vasquez, D., Okal, B., Arras, K.O.: Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1341–1346 (2014)

    Google Scholar 

  27. Vega, A., Manso, L.J., Macharet, D.G., Bustos, P., Núñez, P.: Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances. Pattern Recogn. Lett. 118, 72–84 (2019)

    Article  Google Scholar 

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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.

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  • Print ISBN: 978-3-030-91099-0

  • Online ISBN: 978-3-030-91100-3

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