Abstract
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 [1]. 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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), 7 (2020). https://www.mdpi.com/2306-5729/5/1/7
Pacchierotti, E., Christensen, H.I., Jensfelt, P.: Human-robot embodied interaction in hallway settings: a pilot user study. In: IEEE International Workshop on Robot and Human Interactive Communication, vol. 2005, pp. 164–171. IEEE (2005). https://doi.org/10.1109/ROMAN.2005.1513774
Hansen, S.T., Svenstrup, M., Andersen, H.J., Bak, T.: Adaptive human aware navigation based on motion pattern analysis. In: Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 927–932 (2009). https://doi.org/10.1109/ROMAN.2009.5326212
Cosley, D., Baxter, J., Lee, S., Alson, B., Nomura, S., Adams, P., Sarabu, C., Gay, G.: A tag in the hand: supporting semantic, social, and spatial navigation in museums. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI 2009), pp. 1953–1962 (2009). https://doi.org/10.1145/1518701.1518999
Bhatt, M., Dylla, F.: A qualitative model of dynamic scene analysis and interpretation in ambient intelligence systems. Int. J. Robot. Autom. 24(3), 1–18 (2010). https://doi.org/10.2316/journal.206.2009.3.206-3274
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). https://doi.org/10.1016/j.patrec.2018.07.015
Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware navigation: a survey. Int. J. Soc. Robot. 7(2), 137–153 (2015). https://doi.org/10.1007/s12369-014-0251-1
Charalampous, K., Kostavelis, I., Gasteratos, A.: Recent trends in social aware robot navigation: a survey. Robot. Auton. Syst. 93, 85–104 (2017). https://doi.org/10.1016/j.robot.2017.03.002
Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995). https://doi.org/10.1103/PhysRevE.51.4282
Ferrer, G., Garrell, A., Sanfeliu, A.: Social-aware robot navigation in urban environments. In: 2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings, pp. 331–336 (2013). https://doi.org/10.1109/ECMR.2013.6698863
Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y.: Learning proxemics for personalized human-robot social interaction. Int. J. Soc. Robot. (2019). https://doi.org/10.1007/s12369-019-00560-9
Ramon-Vigo, R., Perez-Higueras, N., Caballero, F., Merino, L.: Transferring human navigation behaviors into a robot local planner. In: IEEE RO-MAN 2014 - 23rd IEEE International Symposium on Robot and Human Interactive Communication: Human-Robot Co-Existence: Adaptive Interfaces and Systems for Daily Life, Therapy, Assistance and Socially Engaging Interactions, pp. 774–779 (2014). https://doi.org/10.1109/ROMAN.2014.6926347
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 (IROS), pp. 1341–1346 (2014). https://doi.org/10.1109/IROS.2014.6942731
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, September 2017, pp. 1343–1350 (2017). https://doi.org/10.1109/IROS.2017.8202312
Chen, C., Liu, Y., Kreiss, S., Alahi, A.: Crowd-robot interaction: crowd-aware robot navigation with attention-based deep reinforcement learning. In: International Conference on Robotics and Automation (ICRA), pp. 6015–6022. IEEE (2019). http://arxiv.org/abs/1809.08835
Martins, G.S., Rocha, R.P., Pais, F.J., Menezes, P.: Clusternav: learning-based robust navigation operating in cluttered environments. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9624–9630. IEEE (2019)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Battaglia, P.W., Hamrick, J.B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., Pascanu, R.: Relational inductive biases, deep learning, and graph networks, pp. 1–40 (2018). https://doi.org/10.1017/S0031182005008516, http://arxiv.org/abs/1806.01261
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4800–4810 (2018)
Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Trans. Neural Netw. 8(3), 1–22 (1997)
Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 729–734 (2005). https://doi.org/10.1109/IJCNN.2005.1555942
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009). https://doi.org/10.1109/TNN.2008.2005605
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks, no. 1, pp. 1–20 (2015). arXiv: 1511.05493
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks, pp. 1–14 (2016). arXiv:1609.02907
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 10843, no. 1, pp. 593–607 (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: Proceedings of the International Conference on Learning Representations 2018, 2015, pp. 1–11 (2018). http://arxiv.org/abs/1710.10903
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, M., Yu, L., Zheng, D., Gan, Q., Gai, Y., Ye, Z., Li, M., Zhou, J., Huang, Q., Ma, C., Huang, Z., Guo, Q., Zhang, H., Lin, H., Zhao, J., Li, J., Smola, A., Zhang, Z.: Deep graph library: towards efficient and scalable deep learning on graphs. In: ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds (RLGM), pp. 1–7 (2019)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Kirby, R., Simmons, R., Forlizzi, J.: Companion: a constraint-optimizing method for person-acceptable navigation. In: RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 607–612. IEEE (2009)
Cruz-maya, A.: Enabling socially competent navigation through incorporating HRI, pp. 9–12. arXiv: 1904.09116v1 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Manso, L.J., Jorvekar, R.R., Faria, D.R., Bustos, P., Bachiller, P. (2021). Graph Neural Networks for Human-Aware Social Navigation. In: Bergasa, L.M., Ocaña, M., Barea, R., López-Guillén, E., Revenga, P. (eds) Advances in Physical Agents II. WAF 2020. Advances in Intelligent Systems and Computing, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-62579-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-62579-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62578-8
Online ISBN: 978-3-030-62579-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)