Click here to download

How Developmental Robotics can give a methodological contribute to the psychology
Journal Title: RICERCHE DI PSICOLOGIA  
Author/s: Daniela Conti, Santo Di Nuovo, Angelo Cangelosi 
Year:  2018 Issue: Language: Italian 
Pages:  19 Pg. 221-239 FullText PDF:  237 KB
DOI:  10.3280/RIP2018-002002
(DOI is like a bar code for intellectual property: to have more infomation:  clicca qui   and here 


The latest developments in Artificial Intelligence and the parallel advances of Developmental Robotics can offer a valid methodological support to the research in psychology and to its applications.This interdisciplinary approach, built on the close collaboration of the disciplines of cognitive robotics and psychology, takes direct inspiration from the developmental principles and mechanisms observed in children, and proposes - through studies of simulation in the laboratory - new hypotheses which can be verified with real children. We will illustrate the utility of this approach by presenting a baby-robot case study of the role of the embodiment during early word learning, as well as an overview of several developmental robotics models of perceptual, social and language psychology. Some limitations and possible correctives of the applications of the Developmental Robotics to the psychological interventions will be underlined.
Keywords: Artificial Intelligence, Developmental Robotics, cognitive learning, psychological applications

  1. Adams, S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., Storrs Hall, J., Samsonovich, A., Scheutz, M., Schlesinger, M., Shapiro, S. C., & Sowa, J.F. (2012). Mapping the landscape of human-level artificial general intelligence. AI Magazine, 33(1), 25-42.
  2. Amso, D., & Johnson, S.P. (2006). Learning by selection: visual search and object perception in young infants. Developmental Psychology, 42(6), 1236., DOI: 10.1037/0012-1649.42.6.1236
  3. Araki, T., Nakamura, T., & Nagai, T. (2013). Long-term learning of concept and word by robots: Interactive learning framework and preliminary results. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference (pp. 2280-2287)., DOI: 10.1109/IROS.2013.6696675
  4. Asada, M., MacDorman, K. F., Ishiguro, H., & Kuniyoshi, Y. (2001). Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robotics and Autonomous Systems, 37(2), 185-193., DOI: 10.1016/S0921-8890(01)00157-9
  5. Barsalou, L.W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617-645.
  6. Belpaeme, T., Baxter, P. E., Read, R., Wood, R., Cuayahuitl, H., Kiefer, B., Racioppa, S., Kruijff-Korbayova´, I., Athanasopoulos, G., Enescu, V., Looije, R., Neerincx, M., Demiris, Y., Ros-Espinoza, R., Beck, A., Canamero, L., Hiolle, A., Lewis, M., Baroni, I., Nalin, M., Cosi, P., Paci, G., Tesser, F., Sommavilla, G., & Humbert, R. (2012). Multimodal child-robot interaction: Building social bonds. Journal of Human-Robot Interaction, 1(2), 33-53., DOI: 10.5898/JHRI.1.2
  7. Belpaeme. Brooks, R.A. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6(1-2), 3-15., DOI: 10.1016/S0921-8890(05)80025-9
  8. Butterworth, G. (1991). The ontogeny and phylogeny of joint visual attention. In A. Whiten (Ed.), Natural theories of mind: Evolution, development and simulation of everyday mindreading. Cambridge, MA: Blackwell.
  9. Cangelosi, A., & Schlesinger, M. (2015). Developmental robotics: From babies to robots. MIT Press.
  10. Cangelosi, A., & Schlesinger, M. (2018). From Babies to Robots: The Developmental Robotics Contribution to Developmental Psychology. Child Development Perspectives, in press.
  11. Cangelosi, A., & Di Nuovo, S. (2016). La mente simulata (e-book). Firenze: Giunti.
  12. Conti, D., Di Nuovo, S., Cangelosi, A., & Di Nuovo, A. (2016). Lateral specialization in unilateral spatial neglect: a cognitive robotics model. Cognitive Processing, 17(3), 321-328.
  13. Conti, D., Di Nuovo, S., Buono, S., Trubia, G., & Di Nuovo, A. (2015). Use of Robotics to Stimulate Imitation in Children with Autism Spectrum Disorder: A Pilot Study in a Clinical Setting. In Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication, ROMAN (pp. 1-6)., DOI: 10.1109/ROMAN.2015.7333589
  14. Conti D., Di Nuovo, A., Trubia G., Buono S., & Di Nuovo, S. (2018). Adapting Robot-Assisted Therapy of Children with Autism and Different Levels of Intellectual Disability: A Preliminary Study. Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 91-92., DOI: 10.1145/3173386.3176962
  15. De la Cruz, V., Di Nuovo, A., & Di Nuovo, S. (2015). Fingers and words to count: A cognitive robot learns sums. Sistemi Intelligenti, 27(1), 7-26., DOI: 10.1109/CCMB.2014.7020688
  16. Demiris, Y., & Khadhouri, B. (2006). Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems, 54(5), 361-369.
  17. Demiris, Y., & Meltzoff, A. (2008). The robot in the crib: A developmental analysis of imitation skills in infants and robots. Infant and Child Development, 17(1), 43-53.
  18. Di Nuovo, S., & Cangelosi, A. (Eds) (2015) Vita naturale, vita artificiale. Milano: FrancoAngeli.
  19. Dominey, P.F., & Warneken, F. (2011). The basis of shared intentions in human and robot cognition. New Ideas in Psychology, 29(3), 260-274.
  20. Forster, F., Nehaniv, C. L., & Saunders, J. (2009). Robots that say “no.” In European Conference on Artificial Life (pp. 158-166). Berlin, Heidelberg: Springer., DOI: 10.1007/978-3-642-21314-4_20
  21. Golosio, B., Cangelosi, A., Gamotina, O., & Masala, G.L. (2015). A cognitive neural architecture able to learn and communicate through natural language. PloS One, 10(11), e0140866.
  22. Karim, M.E., Lemaignan, S., & Mondada, F. (2015). A review: Can robots reshape K-12 STEM education? In Advanced Robotics and its Social Impacts (ARSO), 2015 IEEE International Workshop (pp. 1-8)., DOI: 10.1109/ARSO.2015.7428217
  23. Klahr, D., & Wallace, J.G. (1970). An information processing analysis of some Piagetian experimental tasks. Cognitive Psychology, 1(4), 358-387., DOI: 10.1016/0010-0285(70)90021-6
  24. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69., DOI: 10.1007/BF00337288
  25. Lungarella, M., Metta, G., Pfeifer, R., & Sandini, G. (2003). Developmental robotics: a survey. Connection Science, 15(4), 151-190., DOI: 10.1080/09540090310001655110
  26. Merrick, K. (2017). Value systems for developmental cognitive robotics: A survey. Cognitive Systems Research, 41, 38-55.
  27. Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., von Hofsted, C., Rosander, K., Lopes, M., Santos-Victor, J., Bernardino, A., & Montesano, L. (2010). The iCub humanoid robot: An open-systems platform for research in cognitive development. Neural Networks, 23, 1125-1134.
  28. Morse, A., Belpaeme, T., Cangelosi, A., & Floccia, C. (2011). Modeling U shaped performance curves in ongoing development. In Proceedings of the Cognitive Science Society (Vol. 33).
  29. Morse, A., Benitez, V. L., Belpaeme, T., Cangelosi, A., & Smith, L. B. (2015). Posture affects how robots and infants map words to objects. PloS One, 10(3), e0116012.
  30. Morse, A., & Cangelosi, A. (2017). Why are there developmental stages in language learning? A developmental robotics model of language development. Cognitive Science, 41(S1), 32–51.
  31. Morse, A., De Greeff, J., Belpeame, T., & Cangelosi, A. (2010). Epigenetic robotics architecture (ERA). IEEE Transactions on Autonomous Mental Development, 2(4), 325-339., DOI: 10.1109/TAMD.2010.2087020
  32. Nagai, Y., Asada, M., & Hosoda, K. (2006). Learning for joint attention helped by functional development. Advanced Robotics, 20(10), 1165-1181., DOI: 10.1163/156855306778522497
  33. Nagai, Y., Hosoda, K., Morita, A., & Asada, M. (2003). A constructive model for the development of joint attention. Connection Science, 15(4), 211-229., DOI: 10.1080/09540090310001655101
  34. Norman, D. (2017). Which should be in control: technology or people? Encyclopaedia Britannica, 250th Anniversary (comunicazione personale dell’autore).
  35. Oudeyer, P. (2017). What do we learn about development from baby robots? Wiley Interdisciplinary Reviews: Cognitive Science, 8(1-2).
  36. Olson (ed.), The social foundations of language and thought (pp. 156-186), New York: Norton.
  37. Pessa, E. (2004). Statistica con le reti neurali: un’introduzione. Roma: Di Renzo.
  38. Pezzulo, G., Barsalou, L.W., Cangelosi, A., Fischer, M.H., McRae, K., & Spivey, M. (2013). Computational grounded cognition: a new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3, 612.
  39. Rabbitt, S.M., Kazdin, A.E., & Scassellati, B. (2015). Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use. Clinical Psychology Review, 35, 35-46.
  40. Rumelhart, D.E., & McClelland, J.L. (1986). On learning the past tenses of English verbs. In J.L. McClelland & D.E. Rumelhart (Eds.), Parallel distributed processing. Explorations in the microstructure of cognition (Vol. 2, pp. 216-271). Cambridge, MA: MIT Press.
  41. Samuelson, L.K., Smith, L. B., Perry, L.K., & Spencer, J.P. (2011). Grounding word learning in space. PloS One, 6(12), e28095.
  42. Sarabia, M., & Demiris, Y. (2013). A humanoid robot companion for wheelchair users. In International Conference on Social Robotics (pp. 432-441). Cham: Springer., DOI: 10.1007/978-3-319-02675-6_43
  43. Scassellati, B., Admoni, H., & Matarić, M. (2012). Robots for use in autism research. Annual Review of Biomedical Engineering, 14, 275-294.
  44. Schlesinger, M., Amso, D., & Johnson, S.P. (2007). Simulating infants’ gaze patterns during the development of perceptual completion. In Proceedings of the 7th International Conference on Epigenetic Robotics, 2007 (pp. 157-164).
  45. Shaw, P., Lewkowicz, D., Giagkos, A., Law, J., Kumar, S., De Masson d’Autume, C., Lee, M., & Shen, Q., (2015). Babybot challenge: Motor skills. In Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015 Joint IEEE International Conference (pp. 47-54)., DOI: 10.1109/DEVLRN.2015.7346114
  46. Spitz, R. A. (1957). No and yes: On the genesis of human communication. New York: International Universities Press.
  47. Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
  48. Twomey, K. E., Morse, A., Cangelosi, A., & Horst, J.S. (2016). Children’s referent selection and word learning: insights from a developmental robotic system. Interaction Studies, 17(1), 93-119.
  49. Vygotsky, L.S. (1980). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
  50. Warneken, F., Chen, F., & Tomasello, M. (2006). Cooperative activities in young children and chimpanzees. Child Development, 77(3), 640-663.
  51. Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., & Thelen, E. (2001). Artificial Intelligence. Autonomous mental development by robots and animals. Science, 291(5504), 599-600.
  52. Zlatev, J., & Balkenius, C. (2001). Introduction: Why “Epigenetic Robotics?” In C. Balkenius (ed.) Proceedings of the 1st International Workshop Epigenetic Robotics: Modeling cognitive development in robotic systems, 85, 1-4; Sweden: Lund.

Daniela Conti, Santo Di Nuovo, Angelo Cangelosi, How Developmental Robotics can give a methodological contribute to the psychology in "RICERCHE DI PSICOLOGIA " 2/2018, pp. 221-239, DOI:10.3280/RIP2018-002002

   

FrancoAngeli is a member of Publishers International Linking Association a not for profit orgasnization wich runs the CrossRef service, enabing links to and from online scholarly content