Design and Validation of MOMDP Models for Child–Robot Interaction Within Tasks of Robot-Assisted ASD Diagnostic Protocol

Abstract

The existing procedures for autism spectrum disorder diagnosis are time-consuming and challenging both for evaluators and children being evaluated. Occurrence of low agreement rates between different clinicians when evaluating a child suggests that there exists a need for a more objective approach to diagnostics. To that end, we developed a robot-assisted ASD diagnostic protocol. In this work the focus is on robot reasoning for tasks of the protocol. We propose the mixed observability Markov decision process models for tasks which infer information about the state of a child based on observations of child’s behavior. In order to formulate observation probabilities of task models, ASD experts are surveyed and their knowledge is encoded in the observation probabilities of task models. Expert knowledge also allowed for implementation of child behavioral models which are used to validate and tune developed models. Following the successful validation through simulations of child–robot interaction using child behavioral models, task models are validated through experimental sessions with six typically developing children and eight children with ASD. Results obtained through experiments show that the robot is capable of correctly identifying the behavior of the child within the diagnostic tasks.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Notes

  1. 1.

    Value \(p_s=0.5\) results in uniform distribution of observation probabilities while value \(p_s=1.0\) leaves probabilities unchanged. Values \(p_s>1\) increase the difference in probabilities increasing the amount of information the observation brings into the task. Values \(p_s<0.5\) result in probabilities that update belief in the wrong direction.

References

  1. 1.

    Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, Kurzius-Spencer M, Zahorodny W, Rosenberg CR, White T, Durkin MS, Imm P, Nikolaou L, Yeargin-Allsopp M, Lee L-C, Harrington R, Lopez M, Fitzgerald RT, Hewitt A, Pettygrove S, Constantino JN, Vehorn A, Shenouda J, Hall-Lande J, Van Naarden Braun K, Dowling NF (2018) Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR. Surveill Summ 67(6):1–23. https://doi.org/10.15585/mmwr.ss6706a1

    Article  Google Scholar 

  2. 2.

    Roelfsema MT, Hoekstra RA, Allison C, Wheelwright S, Brayne C, Matthews FE, Baron-Cohen S (2011) Are autism spectrum conditions more prevalent in an information-technology region? A school-based study of three regions in the Netherlands. J Autism Dev Disord 42(5):734–739. https://doi.org/10.1007/s10803-011-1302-1

    Article  Google Scholar 

  3. 3.

    Kim YS, Leventhal BL, Koh YJ, Fombonne E, Laska E, Lim EC, Cheon KA, Kim SJ, Kim YK, Lee H, Song DH, Grinker RR (2011) Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry 168(9):904–912. https://doi.org/10.1176/appi.ajp.2011.10101532

    Article  Google Scholar 

  4. 4.

    Klin A, Lang J, Chicchetti V, Volkmar F (2000) Interrater reliability of clinical diagnosis and DSM-IV criteria for autistic disorder: results of the DSM-IV autism field trial. J Autism Dev Disord 30(2):163–167

    Article  Google Scholar 

  5. 5.

    Crane L, Chester JW, Goddard L, Henry LA, Hill E (2015) Experiences of autism diagnosis: a survey of over 1000 parents in the united kingdom. Autism 20(2):153–162. https://doi.org/10.1177/1362361315573636

    Article  Google Scholar 

  6. 6.

    Lavelle TA, Weinstein MC, Newhouse JP, Munir K, Kuhlthau KA, Prosser LA (2014) Economic burden of childhood autism spectrum disorders. Pediatrics 133(3):e520–e529. https://doi.org/10.1542/peds.2013-0763

    Article  Google Scholar 

  7. 7.

    Daniels AM, Como A, Hergüner S, Kostadinova K, Stosic J, Shih A (2017) Autism in southeast Europe: a survey of caregivers of children with autism spectrum disorders. J Autism Dev Disord 47(8):2314–2325. https://doi.org/10.1007/s10803-017-3145-x

    Article  Google Scholar 

  8. 8.

    Scassellati B (2005) Quantitative metrics of social response for autism diagnosis. In: IEEE international workshop on robot and human interactive communication, 2005 (ROMAN 2005), pp 585–590

  9. 9.

    Aresti-Bartolome N, Garcia-Zapirain B (2014) Technologies as support tools for persons with autistic spectrum disorder: a systematic review. Int J Environ Res Public Health 11(8):7767–7802. https://doi.org/10.3390/ijerph110807767

    Article  Google Scholar 

  10. 10.

    Pennisi P, Tonacci A, Tartarisco G, Billeci L, Ruta L, Gangemi S, Pioggia G (2015) Autism and social robotics: a systematic review. Autism Res 9(2):165–183. https://doi.org/10.1002/aur.1527

    Article  Google Scholar 

  11. 11.

    Petric F, Hrvatinic K, Babic A, Malovan L, Miklic D, Kovacic Z, Cepanec M, Stosic J, Simlesa S (2014) Four tasks of a robot-assisted autism spectrum disorder diagnostic protocol: first clinical tests. In: 2014 IEEE global humanitarian technology conference (GHTC), pp 510–517. https://doi.org/10.1109/GHTC.2014.6970331

  12. 12.

    Boccanfuso L, Scarborough S, Abramson RK, Hall AV, Wright HH, O’Kane JM (2016) A low-cost socially assistive robot and robot-assisted intervention for children with autism spectrum disorder: field trials and lessons learned. Auton Robots. https://doi.org/10.1007/s10514-016-9554-4

  13. 13.

    Huijnen CAGJ, Lexis MAS, de Witte LP (2016) Matching robot KASPAR to autism spectrum disorder (ASD) therapy and educational goals. Int J Soc Robot 8(4):445–455. https://doi.org/10.1007/s12369-016-0369-4

    Article  Google Scholar 

  14. 14.

    Taheri A, Meghdari A, Alemi M, Pouretemad H (2017) Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates. Int J Soc Robot 10(1):93–113. https://doi.org/10.1007/s12369-017-0433-8

    Article  Google Scholar 

  15. 15.

    David DO, Costescu CA, Matu S, Szentagotai A, Dobrean A (2018) Developing joint attention for children with autism in robot-enhanced therapy. Int J Soc Robot 10(5):595–605. https://doi.org/10.1007/s12369-017-0457-0

    Article  Google Scholar 

  16. 16.

    Ismail LI, Verhoeven T, Dambre J, Wyffels F (2018) Leveraging robotics research for children with autism: a review. Int J Soc Robot. https://doi.org/10.1007/s12369-018-0508-1

  17. 17.

    d Souza PEU, Chanel CPC, Dehais F (2015) Momdp-based target search mission taking into account the human operator’s cognitive state. In: 2015 IEEE 27th international conference on tools with artificial intelligence (ICTAI), pp 729–736. https://doi.org/10.1109/ICTAI.2015.109

  18. 18.

    Nikolaidis S, Zhu YX, Hsu D, Srinivasa S (2017) Human–robot mutual adaptation in shared autonomy. In: Proceedings of the 2017 ACM/IEEE international conference on human–robot interaction (HRI’17). ACM, New York, pp 294–302. https://doi.org/10.1145/2909824.3020252

  19. 19.

    Anderson B, Moore A (2005) Active learning for hidden Markov models: Objective functions and algorithms. In: Proceedings of the 22nd international conference on machine learning (ICML’05). ACM, New York, pp 9–16. https://doi.org/10.1145/1102351.1102353

  20. 20.

    Atrash A, Pineau J (2009) A Bayesian reinforcement learning approach for customizing human–robot interfaces. In: Proceedings of the 14th international conference on intelligent user interfaces (IUI ’09). ACM, New York, pp 355–360. https://doi.org/10.1145/1502650.1502700

  21. 21.

    Doshi-Velez F, Pineau J, Roy N (2012) Reinforcement learning with limited reinforcement: using bayes risk for active learning in POMDPs. Artif Intell 187–188:115–132. https://doi.org/10.1016/j.artint.2012.04.006

    MathSciNet  Article  MATH  Google Scholar 

  22. 22.

    Petric F, Tolić D, Miklić D, Kovačić Z, Cepanec M, Šimleša S (2015) Towards a robot-assisted autism diagnostic protocol: modelling and assessment with POMDP. In: Intelligent robotics and applications. Springer, Berlin, pp 82–94. https://doi.org/10.1007/978-3-319-22876-1_8

  23. 23.

    Petric F, Miklić D, Kovačić Z (2018) POMDP-based coding of child–robot interaction within a robot-assisted ASD diagnostic protocol. Int J Hum Robot 15(02):1850011. https://doi.org/10.1142/s0219843618500111

    Article  Google Scholar 

  24. 24.

    Lord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, Schopler E (1989) Austism diagnostic observation schedule: a standardized observation of communicative and social behavior. J Autism Dev Disord 19(2):185–212. https://doi.org/10.1007/bf02211841

    Article  Google Scholar 

  25. 25.

    Petric F, Miklić D, Kovačić Z (2016) Probabilistic eye contact detection for the robot-assisted ASD diagnostic protocol. In: Proceedings of the fifth Croatian computer vision workshop (CCVW 2016), pp 3–8

  26. 26.

    Petric F, Miklic D, Cepanec M, Cvitanovic P, Kovacic Z (2017) Functional imitation task in the context of robot-assisted autism spectrum disorder diagnostics: preliminary investigations. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE.https://doi.org/10.1109/roman.2017.8172498

  27. 27.

    Kokot M, Petric F, Cepanec M, Miklić D, Bejić I, Kovačić Z (2018) Classification of child vocal behavior for a robot-assisted autism diagnostic protocol. In: 2018 26th Mediterranean conference on control and automation (MED), pp 1–6. https://doi.org/10.1109/MED.2018.8443030

  28. 28.

    Dawson G, Toth K, Abbott R, Osterling J, Munson J, Estes A, Liaw J (2004) Early social attention impairments in autism: social orienting, joint attention, and attention to distress. Dev Psychol 40(2):271–283

    Article  Google Scholar 

  29. 29.

    Jones W, Carr K, Klin A (2008) Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Arch Gen Psychiatry 65(8):946–954

    Article  Google Scholar 

  30. 30.

    Hauck M, Fein D, Waterhouse L, Feinstein C (1995) Social initiations by autistic children to adults and other children. J Autism Dev Disord 25(6):579–595. https://doi.org/10.1007/bf02178189

    Article  Google Scholar 

  31. 31.

    Stone WL, Caro-Martinez LM (1990) Naturalistic observations of spontaneous communication in autistic children. J Autism Dev Disord 20(4):437–453. https://doi.org/10.1007/bf02216051

    Article  Google Scholar 

  32. 32.

    Carpenter M, Nagell K, Tomasello M, Butterworth G, Moore C (1998) Social cognition, joint attention, and communicative competence from 9 to 15 months of age. Monographs of the society for research in child development, p i-174

  33. 33.

    Charman T, Swettenham J, Baron-Cohen S, Cox A, Baird G, Drew A (1997) Infants with autism: an investigation of empathy, pretend play, joint attention, and imitation. Dev Psychol 33(5):781

    Article  Google Scholar 

  34. 34.

    Ong SCW, Png SW, Hsu D, Lee WS (2010) Planning under uncertainty for robotic tasks with mixed observability. Int J Robot Res 29(8):1053–1068. https://doi.org/10.1177/0278364910369861

    Article  Google Scholar 

  35. 35.

    Brock O, Trinkle J, Ramos F (2009) SARSOP: efficient point-based POMDP planning by approximating optimally reachable belief spaces. MIT Press, Cambridge, pp 65–72

    Google Scholar 

  36. 36.

    Azenkot S, Feng C, Cakmak M (2016) Enabling building service robots to guide blind people a participatory design approach. In: 2016 11th ACM/IEEE international conference on human–robot interaction (HRI), pp 3–10. https://doi.org/10.1109/HRI.2016.7451727

  37. 37.

    Sequeira P, Alves-Oliveira P, Ribeiro T, Di Tullio E, Petisca S, Melo FS, Castellano G, Paiva A (2016) Discovering social interaction strategies for robots from restricted-perception wizard-of-oz studies. In: The eleventh ACM/IEEE international conference on human robot interaction (HRI’16). IEEE Press, Piscataway, pp 197–204. http://dl.acm.org/citation.cfm?id=2906831.2906866

  38. 38.

    (2015) Nao Software Documentation. Aldebaran Robotics, v2.1 edn. https://community.aldebaran-robotics.com/doc

  39. 39.

    Hoey J, Poupart P (2005) Solving pomdps with continuous or large discrete observation spaces. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI’05). Morgan Kaufmann, San Francisco, pp 1332–1338. http://dl.acm.org/citation.cfm?id=1642293.1642505

  40. 40.

    Hellinger E (1909) Neue begründung der theorie quadratischer formen von unendlichvielen veränderlichen. Journal für die reine und angewandte Mathematik (Crelle’s Journal) 1909(136). https://doi.org/10.1515/crll.1909.136.210

  41. 41.

    Nikulin M (2001) Hellinger distance—encyclopedia of mathematics. European Mathematical Society/Springer, Berlin

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the children and their parents for volunteering in experimental sessions and Damjan, Maja, Jasmina, Sanja, Petra, Dina and Omar for their immeasurable contribution.

Funding

This work has been fully supported by the Croatian Science Foundation through the ADORE project (HRZZ-93743-2014).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Frano Petric.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work has been fully supported by the Croatian Science Foundation through the ADORE Project (HRZZ-93743-2014).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 47 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Petric, F., Kovacic, Z. Design and Validation of MOMDP Models for Child–Robot Interaction Within Tasks of Robot-Assisted ASD Diagnostic Protocol. Int J of Soc Robotics 12, 371–388 (2020). https://doi.org/10.1007/s12369-019-00577-0

Download citation

Keywords

  • Robotics
  • Autism spectrum disorder
  • Diagnostics
  • Mixed observability Markov decision processes