Skip to main content
SpringerLink
Log in

bum_ros: Distributed User Modelling for Social Robots Using ROS

  • Chapter
  • First Online:
Robot Operating System (ROS)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 778))

  • 4430 Accesses

Abstract

In this chapter we present the ROS implementation of our Bayesian User Model, BUM. BUM is a distributed user modelling technique that can be easily implemented in several system topologies. It is able to infer the characteristics of multiple users from heterogeneous data gathered by multiple devices, such as social robots, ambient sensors and surveillance cameras. This chapter presents the BUM process and its implementation, emphasizing the essential and advanced ROS concepts used and extended to achieve the modularity and flexibility needed. Instructions on how to achieve our experimental set-ups are also presented, including a discussion on the role of ROS in the experimental success of the system, and illustrations of the results that can be achieved with our technique. This chapter serves as a thorough description and tutorial for the usage of our package, which can now be useful to the scientific community in user modelling and user-adaptive systems.

This work was developed in the context of the GrowMeUp project, funded by the European Union’s Horizon 2020 Research and Innovation Programme - Societal Challenge 1 (DG CONNECT/H) under grant agreement N0 643647.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The bum_ros package can be found at https://github.com/gondsm/bum.

  2. 2.

    Systems of this nature are known as Recommender Systems [12].

  3. 3.

    ProbaYes can be contacted through http://www.probayes.com/en/.

References

  1. M.F. McTear, User modelling for adaptive computer systems: a survey of recent developments. Artif. Intell. Rev. 7(3–4), 157–184 (1993)

    Article  Google Scholar 

  2. A.F. Norcio, J. Stanley, Adaptive human-computer interfaces: a literature survey and perspective. IEEE Trans. Syst. Man Cybern. 19(2), 399–408 (1989)

    Article  Google Scholar 

  3. H.A. Tair, G.S. Martins, L. Santos, J. Dias, \(\alpha \) POMDP : State-based decision making for personalized assistive robots, in Thirty-Second AAAI Conference on Artificial Intelligence, Workshop 3: Artificial Intelligence Applied to Assistive Technologies and Smart Environments, vol. 1 (2018)

    Google Scholar 

  4. N. Abdo, C. Stachniss, L. Spinello, W. Burgard, Robot, organize my shelves! tidying up objects by predicting user preferences, in 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015), pp. 1557–1564

    Google Scholar 

  5. K. Baraka, M. Veloso, Adaptive interaction of persistent robots to user temporal preferences, in International Conference on Social Robotics (Springer International Publishing, Berlin, 2015), pp. 61–71

    Chapter  Google Scholar 

  6. M. Fiore, H. Khambhaita, An Adaptive and Proactive Human-Aware Robot Guide (2015)

    Chapter  Google Scholar 

  7. A. Kobsa, Generic user modeling systems. User Model. User-Adapt. Interact. 11(1–2), 49–63 (2001)

    Article  Google Scholar 

  8. F. Broz, I. Nourbakhsh, R. Simmons, Planning for human-robot interaction in socially situated tasks: the impact of representing time and intention. Int. J. Soc. Robot. 5(2), 193–214 (2013)

    Article  Google Scholar 

  9. A. Tapus, A. Aly, User adaptable robot behavior, in 2011 International Conference on Collaboration Technologies and Systems (CTS) (2011), pp. 165–167

    Google Scholar 

  10. G.S. Martins, L. Santos, J. Dias, BUM : bayesian user model for distributed social robots, in 26th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN (IEEE, 2017)

    Google Scholar 

  11. A. Cufoglu, User profiling-a short review. Int. J. Comput. Appl. 108(3), 1–9 (2014)

    Google Scholar 

  12. J.A. Konstan, J. Riedl, Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1–2), 101–123 (2012)

    Article  Google Scholar 

  13. D. Fischinger, P. Einramhof, K. Papoutsakis, W. Wohlkinger, P. Mayer, P. Panek, S. Hofmann, T. Koertner, A. Weiss, A. Argyros, M. Vincze, Hobbit, a care robot supporting independent living at home: first prototype and lessons learned. Robot. Auton. Syst. 75, 60–78 (2014)

    Article  Google Scholar 

  14. I. Duque, K. Dautenhahn, K.L. Koay, L. Willcock, B. Christianson, A different approach of using personas in human-robot interaction: integrating Personas as computational models to modify robot companions’ behaviour, in Proceedings - IEEE International Workshop on Robot and Human Interactive Communication (2013), pp. 424–429

    Google Scholar 

  15. K. Baraka, A. Paiva, M. Veloso, Expressive lights for revealing mobile service robot state. Adv. Intell. Syst. Comput. 417, 107–119 (2016)

    Google Scholar 

  16. S. Nikolaidis, A. Kuznetsov, D. Hsu, S. Srinivasa, Formalizing human-robot mutual adaptation: a bounded memory model, in Human-Robot, Interaction (2016), pp. 75–82

    Google Scholar 

  17. A.B. Karami, K. Sehaba, B. Encelle, Adaptive artificial companions learning from users feedback. Adapt. Behav. 24(2), 69–86 (2016)

    Article  Google Scholar 

  18. G.S. Martins, P. Ferreira, L. Santos, J. Dias, A context-aware adaptability model for service robots, in IJCAI-2016 Workshop on Autonomous Mobile Service Robots (New York, 2016)

    Google Scholar 

  19. G.H. Lim, S.W. Hong, I. Lee, I.H. Suh, M. Beetz, Robot recommender system using affection-based episode ontology for personalization, in Proceedings - IEEE International Workshop on Robot and Human Interactive Communication (2013), pp. 155–160

    Google Scholar 

  20. A. Sekmen, P. Challa, Assessment of adaptive human-robot interactions. Knowl. Based Syst. 42, 49–59 (2013)

    Article  Google Scholar 

  21. A. Cerekovic, O. Aran, D. Gatica-Perez, Rapport with virtual agents: what do human social cues and personality explain?, in IEEE Transactions on Affective Computing, vol. X(X) (2016), pp. 1–1

    Google Scholar 

  22. A. Tapus, C. Tapus, M. Mataric, User-robot personality matching and robot behavior adaptation for post-stroke rehabilitation therapy. Intell. Serv. Robot. 1(2), 169–183 (2008)

    Article  Google Scholar 

  23. Q. Sajid, Personality-based consistent robot behavior, in Human-Robot Interaction (2016), pp. 635–636

    Google Scholar 

  24. A. Vinciarelli, G. Mohammadi, A survey of personality computing. IEEE Trans. Affect. Comput. 5(3), 273–291 (2014)

    Article  Google Scholar 

  25. F. Carmagnola, F. Cena, C. Gena, User model interoperability: a survey. User Model. User-Adapt. Interact. 21(3), 285–331 (2011)

    Article  Google Scholar 

  26. J. Fink, A. Kobsa, A review and analysis of commercial user modeling servers for personalization on the World Wide Web. User Model. User-Adapt. Interact. 10, 209–249 (2000)

    Article  Google Scholar 

  27. C.C. Evans, The official YAML web site (2001)

    Google Scholar 

  28. Python Software Foundation. Welcome to Python.org

    Google Scholar 

  29. Open Source Robotics Foundation. rospy

    Google Scholar 

  30. The Matplotlib Development Team. Matplotlib: Python Plotting

    Google Scholar 

  31. Open Source Robotics Foundation. Documentation - ROS Wiki

    Google Scholar 

  32. C.M. Bishop, Pattern Recognition and Machine Learning (2006)

    Google Scholar 

  33. J.F. Ferreira, J. Dias, Probabilistic Approaches for Robotic Perception (Springer International Publishing, Berlin, 2014)

    Google Scholar 

  34. C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27(1), 379–423 (1948)

    Article  MathSciNet  Google Scholar 

  35. G.S. Martins, L. Santos, J. Dias, The GrowMeUp project and the applicability of action recognition techniques, in Third Workshop on Recognition and Action for Scene Understanding (REACTS), ed. by J. Dias, F. Escolano, G. Ezzopardi, R. Marfil (Ruiz de Aloza, 2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Systems and Robotics, University of Coimbra, 3030-790, Coimbra, Portugal

    Gonçalo S. Martins, Luís Santos & Jorge Dias

Authors
  1. Gonçalo S. Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luís Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gonçalo S. Martins .

Editor information

Editors and Affiliations

  1. College of Computer Science and Information Systems, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Martins, G.S., Santos, L., Dias, J. (2019). bum_ros: Distributed User Modelling for Social Robots Using ROS. In: Koubaa, A. (eds) Robot Operating System (ROS). Studies in Computational Intelligence, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-91590-6_15

Download citation

Publish with us

Policies and ethics

Search

Navigation