Interplay between Natural and Artificial Intelligence in Training Autistic Children with Robots

  • Emilia Barakova
  • Tino Lourens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)


The need to understand and model human-like behavior and intelligence has been embraced by a multidisciplinary community for several decades. The success so far has been shown in solutions for a concrete task or a competence, and these solutions are seldom a truly multidisciplinary effort. In this paper we analyze the needs and the opportunities for combining artificial intelligence and bio-inspired computation within an application domain that provides a cluster of solutions instead of searching for a solution to a single task. We analyze applications of training children with autism spectrum disorder (ASD) with a humanoid robot, because it must include multidisciplinary effort and at the same time there is a clear need for better models of human-like behavior which will be tested in real life scenarios through these robots. We designed, implemented, and carried out three applied behavior analysis (ABA) based robot interventions. All interventions aim to promote self initiated social behavior in children with ASD. We found out that the standardization of the robot training scenarios and using unified robot platforms can be an enabler for integrating multiple intelligent and bio-inspired algorithms for creation of tailored, but domain specific robot skills and competencies. This approach might set a new trend to how artificial and bio-inspired robot applications develop. We suggest that social computing techniques are a pragmatic solution to creation of standardized training scenarios and therefore enable the replacement of perceivably intelligent robot behaviors with truly intelligent ones.


Artificial intelligence machine learning training children with autism with robots ABA therapy with humanoid robots bio-inspired computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gillesen, J.C.C., Barakova, E.I., Huskens, B.E.B.M., Feijs, L.M.G.: From training to robot behavior: Towards custom scenarios for robotics in training programs for asd. In: IEEE Int. Conf. on Rehabilitation Robotics (ICORR), pp. 1–7 (2011)Google Scholar
  2. 2.
    Goodrich, M.A., Colton, M., et al.: Incorporating a robot into an autism therapy team. IEEE, Intelligent Systems 27(2), 52–59 (2012)CrossRefGoogle Scholar
  3. 3.
    Feil-Seifer, D., Matarić, M.J.: Toward socially assistive robotics for augmenting interventions for children with autism spectrum disorders. In: Khatib, O., Kumar, V., Pappas, G.J. (eds.) Experi. Robotics: The 11th Intern. Sympo., STAR, vol. 54, pp. 201–210. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Scassellati, B., Admoni, H., Matarić, M.J.: Robots for use in autism research. Annual Review of Biomedical Engineering 14, 275–294 (2012)CrossRefGoogle Scholar
  5. 5.
    Barakova, E.I., Gillesen, J.C.C., Huskens, B.E.B.M., Lourens, T.: End-user programming architecture facilitates the uptake of robots in social therapies. Robotics and Autonomous Systems, doi:10.1016/j.robot.2012.08.001Google Scholar
  6. 6.
    Diehl, J.J., Schmitt, L.M., Villano, M., Crowell, C.R.: The clinical use of robots for individuals with autism spectrum disorders: a critical review. Res. Autism Spectr. Disord. 6, 249 (2012)CrossRefGoogle Scholar
  7. 7.
    Huskens, B., Verschuur, R., Gillesen, J., Didden, R., Barakova, E.: Promoting question-asking in school-aged children with autism spectrum disorders: Effectiveness of a robot intervention compared to a human-trainer intervention. Journal of Developmental Neurorehabilitation (in press 2013)Google Scholar
  8. 8.
    Wainer, J., Dautenhahn, K., Robins, B., Amirabdollahian, F.: Collaborating with kaspar: Using an autonomous humanoid robot to foster cooperative dyadic play among children with autism. In: 10th IEEE-RAS Humanoids, pp. 631–638 (2010)Google Scholar
  9. 9.
    Goodrich, M.A., Crandall, J.W., Barakova, E.I.: Teleoperation and beyond for assistive humanoid robots. HF Reviews 9 (in press 2013)Google Scholar
  10. 10.
    Kim., E., Paul., R., Shic., F., Scassellati., B.: Bridging the research gap: making hri useful to individuals with autism. J. Hum.-Robot Interact. 1Google Scholar
  11. 11.
    Smith, T., McAdam, D., Napolitano: Autism and applied behavior analysis. In: Sturmey, P., Fitzer (eds.) Autism Spectrum Disorders Applied Behavior Analysis evidence and practice, vol. 13, pp. 1–29. Pro-Ed, Inc. (2007)Google Scholar
  12. 12.
    Koegel, R.L., Koegel, L.K.: Pivotal response treatments for autism: communication, social, and academic development. P.H. Brookes, Baltimore (2006)Google Scholar
  13. 13.
    Barakova, E., Spaanenburg, L.: Windowed active sampling for reliable neural learning. Journal of Systems Architecture 44(8), 635–650 (1998)CrossRefGoogle Scholar
  14. 14.
    Boujarwah, F., Abowd, G., Arriaga, R.: Socially computed scripts to support social problem solving skills. In: CHI 2012, pp. 1987–1996. ACM (2012)Google Scholar
  15. 15.
    Lourens, T.: Tivipe –tino’s visual programming environment. In: The 28th Annual International Computer Software & Applications Conference, IEEE COMPSAC 2004, pp. 10–15 (2004)Google Scholar
  16. 16.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  17. 17.
    Chai, D., Ngan, K.N.: Face segmentation using skin-color map in videophone applications. IEEE Trans. on Circuits and Systems for Video Techn. 9(4), 551–564 (1999)CrossRefGoogle Scholar
  18. 18.
    Lourens, T., van Berkel, R., Barakova, E.I.: Communicating emotions and mental states to robots in a real time parallel framework using laban movement analysis. Robotics and Autonomous Systems 58(12), 1256–1265 (2010)CrossRefGoogle Scholar
  19. 19.
    Heisele, B., Serre, T., Pontil, M., Vetter, T., Poggio, T.: Categorization by learning and combining object parts. In: Neural Information Processing Systems (NIPS), Vancouver, pp. 1239–1245 (2001)Google Scholar
  20. 20.
    Heisele, B., Riskov, I., Morgenstern, C.: Components for Object Detection and Identification. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 225–237. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591 (1959)Google Scholar
  22. 22.
    Würtz, R.P., Lourens, T.: Corner detection in color images by multiscale combination of end-stopped cortical cells. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 901–906. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  23. 23.
    Lourens, T., Barakova, E.I., Okuno, H.G., Tsujino, H.: A computational model of monkey cortical grating cells. Biological Cybernetics 92(1), 61–70 (2005)zbMATHCrossRefGoogle Scholar
  24. 24.
    Lourens, T., Barakova, E.I.: Orientation contrast sensitive cells in primate v1 –a computational model. Natural Computing 6(3), 241–252 (2007)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emilia Barakova
    • 1
  • Tino Lourens
    • 2
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.TiViPEHelmondThe Netherlands

Personalised recommendations