Advertisement

Immersive Telepresence Framework for Remote Educational Scenarios

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12206)

Abstract

Social robots have an enormous potential for educational applications, allowing cognitive outcomes similar to those with human involvement. Enabling instructors and learners to directly control a social robot and immersively interact with their students and peers opens up new possibilities for effective lesson delivery and better participation in the classroom.

This paper proposes the use of immersive technologies to promote engagement in remote educational settings involving robots. In particular, this research introduces a telepresence framework for the location-independent operation of a social robot using a virtual reality headset and controllers. Using the QTrobot as a platform, the framework supports the direct and immersive control via different interaction modes including motion, emotion and voice output. Initial tests involving a large audience of educators and students validate the acceptability and applicability to interactive classroom scenarios.

Keywords

Social robotics Education Immersive telepresence Teleoperation Virtual reality Human-robot interaction UI design 

Notes

Acknowledgements

The authors would like to thank Thomas Sauvage and Julien Sanchez from the University of Toulouse III - Paul Sabatier, who assisted in this research in the context of an internship at the University of Luxembourg.

References

  1. 1.
    Adamides, G., Christou, G., Katsanos, C., Xenos, M., Hadzilacos, T.: Usability guidelines for the design of robot teleoperation: a taxonomy. IEEE Trans. Hum.-Mach. Syst. 45(2), 256–262 (2015)CrossRefGoogle Scholar
  2. 2.
    Baker, M., Casey, R., Keyes, B., Yanco, H.A.: Improved interfaces for human-robot interaction in urban search and rescue. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), pp. 2960–2965 (2004)Google Scholar
  3. 3.
    de Barros, P.G., Linderman, R.W.: A survey of user interfaces for robot teleoperation. Technical report, Worcester Polytechnic Institute (2009). http://digitalcommons.wpi.edu/computerscience-pubs/21
  4. 4.
    Bartneck, C., Soucy, M., Fleuret, K., Sandoval, E.B.: The robot engine - making the unity 3D game engine work for HRI. In: Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2015), pp. 431–437 (2015)Google Scholar
  5. 5.
    Belpaeme, T., et al.: Child-robot interaction: perspectives and challenges. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.) ICSR 2013. LNCS (LNAI), vol. 8239, pp. 452–459. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-02675-6_45CrossRefGoogle Scholar
  6. 6.
    Belpaeme, T., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21) (2018)Google Scholar
  7. 7.
    Benyon, D.: Designing Interactive Systems: A Comprehensive Guide to HCI, UX and Interaction Design. Pearson Edinburgh (2014)Google Scholar
  8. 8.
    Cha, E., Chen, S., Matarić, M.J.: Designing telepresence robots for K-12 education. In: 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2017), pp. 683–688 (2017)Google Scholar
  9. 9.
    Clabaugh, C., Matarić, M.: Escaping Oz: autonomy in socially assistive robotics. Ann. Rev. Control Robot. Auton. Syst. 2, 33–61 (2019)CrossRefGoogle Scholar
  10. 10.
    Codd-Downey, R., Forooshani, P.M., Speers, A., Wang, H., Jenkin, M.R.M.: From ROS to unity: leveraging robot and virtual environment middleware for immersive teleoperation. In: Proceedings of the 11th IEEE International Conference on Information and Automation (ICIA 2014), pp. 932–936 (2014)Google Scholar
  11. 11.
    Crooks, T.J.: The impact of classroom evaluation practices on students. Rev. Educ. Res. 58(4), 438–481 (1988)CrossRefGoogle Scholar
  12. 12.
    Draper, J.V., Kaber, D.B., Usher, J.M.: Telepresence. Hum. Factors 40(3), 354–375 (1998)CrossRefGoogle Scholar
  13. 13.
    Fong, T., Thorpe, C., Baur, C.: Collaboration, dialogue, human-robot interaction. In: Jarvis, R.A., Zelinsky, A. (eds.) Proceedings of the 10th International Symposium on Robotics Research (ISRR 2003), pp. 255–266 (2003)Google Scholar
  14. 14.
    Gallon, L., Abenia, A., Dubergey, F., Négui, M.: Using a telepresence robot in an educational context. In: Proceedings of the 10th International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS 2019), pp. 16–22 (2019)Google Scholar
  15. 15.
    Jecker, J.D., Maccoby, N., Breitrose, H.: Improving accuracy in interpreting non-verbal cues of comprehension. Psychol. Sch. 2(3), 239–244 (1965)CrossRefGoogle Scholar
  16. 16.
    Kilteni, K., Groten, R., Slater, M.: The sense of embodiment in virtual reality. Presence: Teleoperators Virtual Environ. 21(4), 373–387 (2012)Google Scholar
  17. 17.
    Meng, W., Hu, Y., Lin, J., Lin, F., Teo, R.: ROS+Unity: an efficient high-fidelity 3D multi-UAV navigation and control simulator in GPS-denied environments. In: 41st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015), pp. 2562–2567 (2015)Google Scholar
  18. 18.
    Miller, D.P., Nourbakhsh, I.: Robotics for education. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 2115–2134. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1_79CrossRefGoogle Scholar
  19. 19.
    Mubin, O., Stevens, C.J., Shahid, S., Al Mahmud, A., Dong, J.: A review of the applicability of robots in education. Technol. Educ. Learn. 1, 1–7 (2013)CrossRefGoogle Scholar
  20. 20.
    Okon, J.: Role of non-verbal communication in education. Mediterranean J. Soc. Sci. 2(5), 35–40 (2011)Google Scholar
  21. 21.
    Pasternak, E., Fenichel, R., Marshall, A.N.: Tips for creating a block language with blockly. In: Proceedings of the IEEE Blocks and Beyond Workshop (B&B 2017), pp. 21–24 (2017)Google Scholar
  22. 22.
    Rodríguez-Lera, F.J., Matellán-Olivera, V., Conde-González, M.Á., Martín-Rico, F.: HiMoP: a three-component architecture to create more human-acceptable social-assistive robots. Cogn. Process. 19(2), 233–244 (2018)CrossRefGoogle Scholar
  23. 23.
    Roldán, J.J., Peña-Tapia, E., Garzón-Ramos, D., de León, J., Garzón, M., del Cerro, J., Barrientos, A.: Multi-robot systems, virtual reality and ROS: developing a new generation of operator interfaces. In: Koubaa, A. (ed.) Robot Operating System (ROS). SCI, vol. 778, pp. 29–64. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-91590-6_2CrossRefGoogle Scholar
  24. 24.
    Schuemie, M.J., van der Straaten, P., Krijn, M., van der Mast, C.A.: Research on presence in virtual reality: a survey. CyberPsychol. Behav. 4(2), 183–201 (2001)CrossRefGoogle Scholar
  25. 25.
    Sita, E., Horváth, C.M., Thomessen, T., Korondi, P., Pipe, A.G.: ROS-Unity3D based system for monitoring of an industrial robotic process. In: Proceedings of the 10th IEEE/SICE International Symposium on System Integration (SII 2017), pp. 1047–1052 (2017)Google Scholar
  26. 26.
    Steuer, J.: Defining virtual reality: dimensions determining telepresence. J. Commun. 42(4), 73–93 (1992)CrossRefGoogle Scholar
  27. 27.
    Toh, L.P.E., Causo, A., Tzuo, P.W., Chen, I.M., Yeo, S.H.: A review on the use of robots in education and young children. J. Educ. Technol. Soc. 19(2), 148–163 (2016)Google Scholar
  28. 28.
    Tromp, N., Hekkert, P., Verbeek, P.P.: Design for socially responsible behavior: a classification of influence based on intended user experience. Des. Issues 27(3), 3–19 (2011)CrossRefGoogle Scholar
  29. 29.
    Tsui, K.M., Desai, M., Yanco, H.A.: Considering the bystander’s perspective for indirect human-robot interaction. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI 2010), pp. 129–130 (2010)Google Scholar
  30. 30.
    Tunstall, P., Gipps, C.: Teacher feedback to young children in formative assessment: a typology. Br. Educ. Res. J. 22(4), 389–404 (1996)CrossRefGoogle Scholar
  31. 31.
    Weiss, A., Bernhaupt, R., Lankes, M., Tscheligi, M.: The USUS evaluation framework for human-robot interaction. In: Proceedings of the Symposium on New Frontiers in Human-Robot Interaction at the Adaptive and Emergent Behaviour and Complex Systems Convention (AISB 2009), pp. 11–26 (2009)Google Scholar
  32. 32.
    Whitney, D., Rosen, E., Phillips, E., Konidaris, G., Tellex, S.: Comparing robot grasping teleoperation across desktop and virtual reality with ROS reality. In: Amato, N.M., Hager, G., Thomas, S., Torres-Torriti, M. (eds.) Robotics Research. SPAR, vol. 10, pp. 335–350. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-28619-4_28CrossRefGoogle Scholar
  33. 33.
    Whitney, J.P., Chen, T., Mars, J., Hodgins, J.K.: A hybrid hydrostatic transmission and human-safe haptic telepresence robot. In: 22nd IEEE International Conference on Robotics and Automation (ICRA 2016), pp. 690–695 (2016)Google Scholar
  34. 34.
    Yanco, H.A., Drury, J.: Classifying human-robot interaction: an updated taxonomy. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (ICSMC 2004), vol. 3, pp. 2841–2846 (2004)Google Scholar
  35. 35.
    Zhang, M., Duan, P., Zhang, Z., Esche, S.: Development of telepresence teaching robots with social capabilities. In: Proceedings of the ASME International Mechanical Engineering Congress and Exposition (IMECE 2018), pp. 1–11 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of LuxembourgEsch-sur-AlzetteLuxembourg
  2. 2.University of LeónLeónSpain

Personalised recommendations