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User-Adaptive Interaction in Social Robots: A Survey Focusing on Non-physical Interaction

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Abstract

This work presents a survey on the usage of user-adaptive techniques for human interface with Social Robots, with focus on non-physical interaction. The work is based on an analysis of a number of recent scientific works, and aims to uncover existing scientific and technological gaps which can serve as basis for future research and development work. User-adaptive systems consist of autonomous agents that are able to use some manner of information on their user in order to adapt to them. Through their adaptive nature, these systems have been shown to be easier to accept by end-users, and to lead to improvements in a myriad of objective and subjective performance measurements. Thus, in the context of a growing domestic Social Robot industry, it becomes of key importance to study the scientific and technological frontiers of this field. In order to uncover potential lines of future research, we propose a taxonomy for the classification of works, which we use to analyse the works under survey, exposing the current scientific frontiers of the area. Aiming to establish the overall readiness of the field, we also analyse the maturity of the works under survey, exposing the current technological level of the techniques at hand and discussing a number of technological challenges.

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References

  1. Abdo N, Stachniss C, Spinello L, Burgard W (2015) Robot, organize my shelves! Tidying up objects by predicting user preferences. In: 2015 IEEE international conference on robotics and automation (ICRA), pp 1557–1564

  2. Aldebaran. Pepper, the humanoid robot from Aldebaran, a genuine companion. https://www.ald.softbankrobotics.com/en/cool-robots/pepper

  3. Aly A (2014) Towards an interactive human-robot relationship: developing a customized robot’s behavior to human’s profile, p 181

  4. Aly A, Tapus A (2013) A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction. In: ACM/IEEE international conference on human–robot interaction, pp 325–332

  5. Amirabdollahian F, Op Den Akker R, Bedaf S, Bormann R, Draper H, Evers V, Gelderblom GJ, Ruiz CG, Hewson D, Hu N, Iacono I, Koay KL, Krose B, Marti P, Michel H, Prevot-Huille H, Reiser U, Saunders J, Sorell T, Dautenhahn K (2013) Accompany: acceptable robotics companions for ageing years - multidimensional aspects of human–system interactions. In: 2013 6th international conference on human system interactions, HSI 2013, pp 570–577

  6. Amoia M, Gardent C, Perez-beltrachini L (2015) Learning a second language with a socially assistive robot. Test 5

  7. Aylett R, Kappas A, Castellano G, Bull S, Barendregt W, Paiva A, Hall L (2015) I know how that feels—an empathic robot tutor. eChallenges e-2015 Conference, pp 1–9

  8. Emmanuel Senft B (2015) Paul Baxter, James Kennedy, and Tony Belpaeme. Supervised progressively autonomous robot competencies, SPARC

    Google Scholar 

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

    Google Scholar 

  10. Baraka K, Veloso M (2015) Adaptive interaction of persistent robots to user temporal preferences. International conference on social robotics. Springer, Berlin, pp 61–71

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  12. Burr Settles (2012) Active Learning

  13. Chiang HH, Chen YL, Lin CT (2013) Human–robot interactive assistance of a robotic walking support system in a home environment. In: Proceedings of the international symposium on consumer electronics, ISCE, pp 263–264

  14. Cross N (1987) Computers and people, vol 8

  15. de Graaf MMA (2015) Living with robots: investigating the user acceptance of social robots in domestic environments. Ph,D, thesis

  16. Devin S, Alami R (2016) An implemented theory of mind to improve human–robot shared plans execution. In: International conference on human–robot interaction, pp 319–326

  17. Duque I, Dautenhahn K, Koay KL, Willcock L, Christianson B (2013) 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, pp 424–429

  18. Eckert M, López M, Lázaro C, Meneses J, Ortega JFM (2015) MoKey—a motion based keyboard interpreter, pp 4–5

  19. Efthimiou E, Fotinea S, Goulas T, Dimou A, Koutsombogera M, Pitsikalis V, Maragos P, Tzafestas C (2016) In: The MOBOT platform—showcasing multimodality in human–assistive robot interaction, vol 1, pp 382–391

  20. Ferreira JF, Dias J (2014) Probabilistic approaches for robotic perception. Springer, Berlin

    Book  Google Scholar 

  21. Fiore M, Khambhaita H (2015) An adaptive and proactive human–aware robot guide

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

    Article  Google Scholar 

  23. Fisher R (2008) CVonline: the evolving, distributed, non-proprietary, on-line compendium of computer vision

  24. Gao Y, Chang HJ, Demiris Y (2015) User modelling for personalised dressing assistance by humanoid robots. In: 2015 IEEE/RSJ international conference on intelligent robots and systems, pp 1840–1845

  25. Ghallab M, Nau D, Traverso P (2016) Automated planning and acting. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  26. Gordon G, Spaulding S, Westlund JK, Lee JJ, Plummer L, Martinez M, Das M, Breazeal C (2016) Affective personalization of a social robot tutor for children’s second language skills. In: Proceedings of the 30th conference on artificial intelligence (AAAI 2016), pp 3951–3957

  27. Gordon G, Breazeal C (2015) Bayesian active learning-based robot tutor for children’ s word-reading skills. Aaai, pp 1343–1349

  28. Gordon G, Spaulding S, Kory J, Joo J (2012) Affective personalization of a social robot tutor for children’ s second language skills

  29. Grosinger J, Pecora F, Saffiotti A (2016) Making robots proactive through equilibrium maintenance. In: Ijcai, pp 3375–3381

  30. Huang C-M, Mutlu B (2016) Anticipatory robot control for efficient human–robot collaboration. In: Human–robot interaction (section V)

  31. Blue Frog Robotics Inc. (2016) Buddy: your companion robot. http://www.bluefrogrobotics.com/en/buddy-your-companion-robot/

  32. Kanda T, Sato R, Saiwaki N, Ishiguro H (2007) A two-month field trial in an elementary school for long-term human-robot interaction. IEEE Trans Robot 23(5):962–971

    Article  Google Scholar 

  33. Kanda T, Hirano T, Eaton D, Ishiguro H (2004) Interactive robots as social partners and peer tutors for children: a field trial. Human Comput Interact 19:61–84

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Karami AB, Mouaddib A (2011) A decision model of adaptive interaction selection for a robot companion. In: Proceedings of the 5th european conference on mobile robots, ECMR’11, pp 83–88

  36. Karami AB, Sehaba K, Encelle B (2013) Towards adaptive robots based on interaction traces: a user study. In: 2013 16th international conference on advanced robotics (ICAR), pp 1–6

  37. Karami AB, Sehaba K, Encelle B (2014) Learn to adapt based on users’ feedback. In: The 23rd IEEE international symposium on robot and human interactive communication. IEEE

  38. Karami AB, Sehaba K, Encelle B (2013) Adaptive and personalised robots—learning from users’ feedback. In: Proceedings—international conference on tools with artificial intelligence, ICTAI, pp 626–632

  39. Kim HG, Yang JY, Kwon DS (2015) Experience based domestic environment and user adaptive cleaning algorithm of a robot cleaner. In: 2014 11th international conference on ubiquitous robots and ambient intelligence, URAI 2014 (Urai), pp 176–178

  40. Kim HJ, Choi YS (2013) Eating activity recognition for health and wellness: a case study on Asian eating style. In: Digest of technical papers—IEEE international conference on consumer electronics, vol 1, pp 446–447

  41. Klee SD, Ferreira BQ, Silva R, Costeira P, Melo FS, Veloso M (2015) Personalized assistance for dressing users. In: 7th international conference on social robotics (ICSR ). Springer, Berlin

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  44. Lakiotaki K, Matsatsinis NF, Tsoukiàs A (2011) Multicriteria user modeling in recommender systems. IEEE Intell Syst 26(2):64–76

    Article  Google Scholar 

  45. Lam C, Yang AY, Driggs-campbell K, Bajcsy R, Sastry SS (2015) Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems

  46. Li Y, Tee KP, Yan R, Chan WL, Wu Y, Limbu DK (2015) Adaptive optimal control for coordination in physical human–robot interaction. In: IEEE international conference on intelligent robots and systems (IROS). IEEE

  47. Lim GH, Hong SW, Lee I, IH Suh, Beetz M (2013) Robot recommender system using affection-based episode ontology for personalization. In: Proceedings—IEEE international workshop on robot and human interactive communication, pp 155–160

  48. Nichola L, Erin W, Heather P (2016) Effects of voice-adaptation and social dialogue on perceptions of a robotic learning companion. In: Human-robot, interaction, pp 255–262

  49. Madureira A, Cunha B, Pereira JP, Gomes S, Pereira I, Santos JM, Abraham A (2014) Using personas for supporting user modeling on scheduling systems. In: 14th international conference on hybrid intelligent systems (HIS), pp 279–284. IEEE

  50. Martins GS, Ferreira P, Santos L, Dias J (2016) A context-aware adaptability model for service robots. In: IJCAI-2016 workshop on autonomous mobile service robots, New York

  51. Martins GS, Santos L, Dias J (2015) The GrowMeUp project and the applicability of action recognition techniques. In: Dias J, Escolano F, Ezzopardi G, Marfil R (eds) Third workshop on recognition and action for scene understanding (REACTS). Ruiz de Aloza

  52. Matsubara T, Miro JV, Tanaka D, Poon J, Sugimoto K (2015) Sequential intention estimation of a mobility aid user for intelligent navigational assistance. In: 24th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 444–449. IEEE

  53. Mavridis N (2015) A review of verbal and non-verbal human-robot interactive communication. Robot Autonom Syst 63(P1):22–35

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  55. Mead R, Matarić M (2015) Toward robot adaptation of human speech and gesture parameters in a unified framework of proxemics and multimodal communication. In: 2015 IEEE international conference on robotics and automation workshop on machine learning for social robots

  56. Miñón R, Moreno L, Martínez P, Abascal J (2014) An approach to the integration of accessibility requirements into a user interface development method. Sci Comput Program 86:58–73

    Article  Google Scholar 

  57. Moustris G, Geravand M, Tzafestas C, Peer A (2016) User-adaptive shared control in a mobility assistance robot based on human-centered intention reading and decision making schemes, pp 1–4

  58. Müller S, Sprenger S, Gross H-M (2014) OfficeMate: a study of an online learning dialog system for mobile assistive robots. In: ADAPTIVE 2014, the sixth international conference on adaptive and self-adaptive systems and applications (Adaptive), pp 104–110

  59. Muller S, Sprenger S, Gross HM (2014) Online adaptation of dialog strategies based on probabilistic planning. In: Proceedings—IEEE international workshop on robot and human interactive communication, 2014-Octob(October), vol 692–697

  60. Stefanos N, Anton K, David H, Siddhartha S (2016) Formalizing human–robot mutual adaptation: a bounded memory model. In: Human–robot, interaction, pp 75–82

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

    Article  Google Scholar 

  62. Papageorgiou XS, Chalvatzaki G, Dometios A, Tzafestas CS, Maragos P (2017) Intelligent assistive robotic systems for the elderly: two real-life use cases. In: PETRA ’17 proceedings of the 10th international conference on pervasive technologies related to assistive environments, pp 360–365. ACM

  63. Papageorgiou XS, Moustris GP, Pitsikalis V, Chalvatzaki G, Dometios A, Kardaris N, Tzafestas CS, Maragos P (2015) User-oriented cognitive interaction and control for an intelligent robotic walker

  64. ParoRobots. The paro robot. http://www.parorobots.com/

  65. Piller F, Ihl C, Steiner F (2010) Embedded toolkits for user co-design: a technology acceptance study of product adaptability in the usage stage. In: Proceedings of the annual Hawaii international conference on system sciences, pp 1–10

  66. Revelle W, Scherer KR (2005) Personality and emotion. In: Handbook of personality and affective science, pp 304–305

  67. Roboticstrends. The jibo robot. http://www.roboticstrends.com/company/jibo

  68. Ros R, Baroni I, Demiris Y (2014) Adaptive human-robot interaction in sensorimotor task instruction: from human to robot dance tutors. Robot Autonom Syst 62(6):707–720

    Article  Google Scholar 

  69. Rossi S, Ferland F, Tapus A (2017) User profiling and behavioral adaptation for HRI: a survey. Pattern Recognit Lett 99:3–12

    Article  Google Scholar 

  70. Santos L, Khoshhal K, Dias J, Khoshhala K, Dias J (2015) Trajectory-based human action segmentation. Pattern Recognit 48(2):568–579

    Article  Google Scholar 

  71. Sanz I, Museros L, Falomir Z, Gonzalez-Abril L (2015) Customizing a qualitative colour description for adaptability and usability. Pattern Recognit Lett 67:2–10

    Article  Google Scholar 

  72. Sarabia M, Lee K, Demiris Y (2015) Towards a synchronised grammars framework for adaptive musical human–robot collaboration. In: Proceedings of the IEEE international symposium on robot and human interactive communication (to appear)

  73. Schadenberg BR, Neerincx MA, Cnossen F, Looije R (2017) Personalising game difficulty to keep children motivated to play with a social robot: a Bayesian approach. Cognit Syst Res 43:222–231

    Article  Google Scholar 

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

    Article  Google Scholar 

  75. Sequeira P, Melo FS, Paiva A (2014) Learning by appraising: an emotion-based approach to intrinsic reward design. Adapt Behav 22(5):330–349

    Article  Google Scholar 

  76. Shen Q, Dautenhahn K, Saunders J, Kose H (2015) Can real-time, adaptive human-robot motor coordination improve humans’ overall perception of a robot? IEEE Trans Autonom Mental Dev 7(1):52–64

    Article  Google Scholar 

  77. Smith JS, Chao C, Thomaz AL (2015) Real-time changes to social dynamics in human–robot turn-taking, pp 3024–3029

  78. Taha T, Mir JV (2011) A POMDP framework for modelling human interaction with assistive robots

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

    Article  Google Scholar 

  80. Triebel R, Arras K, Alami R, Beyer L, Breuers S, Chatila R, Chetouani M, Cremers D, Evers V, Joosse M, Khambhaita H, Kucner T, Leibe B, Lilienthal AJ, Linder T, Lohse M, Magnusson M, Okal B, Palmieri L, Rafi U, Van Rooij M, Zhang L (2016) SPENCER: a socially aware service robot for passenger guidance and help in busy airports, pp 1–14

  81. Van Den Broeck J, Rossi G, Dierckx E, De Clercq B (2012) Age-neutrality of the NEO-PI-R: potential differential item functioning in older versus younger adults. J Psychopathol Behav Assess 34:361–369

    Article  Google Scholar 

  82. Vinciarelli A (2014) More personality in personality computing. IEEE Trans Affect Comput 5(3):297–300

    Article  Google Scholar 

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

    Article  Google Scholar 

  84. Wada K, Shibata T (2006) Robot therapy in a care house—its sociopsychological and physiological effects on the residents. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006 (May), pp 3966–3971

  85. Zhang H, Zhou W, Reardon C, Parker LE (2014) Simplex-based 3D spatio-temporal feature description for action recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)

  86. Zhang H, Ping W, Beck A, Zhang Z, Gao X (2016) Adaptive incremental learning of image semantics with application to social robot. Neurocomputing 173:93–101

    Article  Google Scholar 

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Funding

This work was funded by the European Union’s Horizon 2020 research and innovation programme - Societal Challenge 1 (DG CONNECT/H) under grant agreement No 643647 (Project GrowMeUp).

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Authors and Affiliations

  1. Institute of Systems and Robotics, University of Coimbra, 3030-290, Coimbra, Portugal

    Gonçalo S. Martins, Luís Santos & Jorge Dias

  2. Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi, UAE

    Jorge Dias

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  1. Gonçalo S. Martins
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Correspondence to Jorge Dias.

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Martins, G.S., Santos, L. & Dias, J. User-Adaptive Interaction in Social Robots: A Survey Focusing on Non-physical Interaction. Int J of Soc Robotics 11, 185–205 (2019). https://doi.org/10.1007/s12369-018-0485-4

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