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Adapting approaching proxemics of a service robot based on physical user behavior and user feedback

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Abstract

Service robots with social interactive features are developed to cater to the demand in various application domains. These robots often need to approach toward users to accomplish typical day-to-day services. Thereby, the approaching behavior of a service robot is a crucial factor in developing social interactivity between users and the robot. In this regard, a robot should be capable of maintaining proper proxemics at the termination position of an approach that improves the comfort of users. Proxemics preferences of humans depend on physical user behavior as well as personal factors. Therefore, this paper proposes a novel method to adapt the termination position of an approach based on physical user behavior and user feedback. Physical behavior of a user is perceived by the robot through analyzing skeletal joint movements of the user. These parameters are taken as inputs for a fuzzy neural network that determines the appropriate interpersonal distance. The preference of a user is learnt by modifying the internal parameters of the fuzzy neural network based on user feedback. A user study has been conducted to compare and contrast behavior of the proposed system over the existing approaches. The outcomes of the user study confirm a significant improvement in user satisfaction due to the adaptation toward users based on feedback.

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References

  • Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15(12), 31314–31338 (2015)

    Article  Google Scholar 

  • Ball, A., Silvera-Tawil, D., Rye, D., Velonaki, M.: Group comfortability when a robot approaches. In: International Conference on Social Robotics, pp. 44–53. Springer (2014)

  • Bartneck, C., Forlizzi, J.: A design-centred framework for social human–robot interaction. In: 13th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN), pp. 591–594. IEEE (2004)

  • Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)

    Article  Google Scholar 

  • Bethel, C.L., Murphy, R.R.: Review of human studies methods in HRI and recommendations. Int. J. Soc. Robot. 2(4), 347–359 (2010)

    Article  Google Scholar 

  • Bhavnani, C.V., Rolf, M.: Attitudes towards a handheld robot that learns proxemics. In: 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 1–2. IEEE (2020)

  • Bocardus, E.: Social distance and its origins. J. Appl. Sociol. 9, 216–226 (1925)

    Google Scholar 

  • De Graaf, M.M., Allouch, S.B.: Exploring influencing variables for the acceptance of social robots. Robot. Auton. Syst. 61(12), 1476–1486 (2013)

    Article  Google Scholar 

  • Edwards, C., Edwards, A., Omilion-Hodges, L.: Receiving medical treatment plans from a robot: evaluations of presence, credibility, and attraction. In: Companion of the 2018 ACM/IEEE International Conference on Human–Robot Interaction, pp. 101–102. ACM (2018)

  • Ellis, P.D.: The Essential Guide to Effect Sizes: Statistical Power, Meta-analysis, and the Interpretation of Research Results. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  • Firestone, I.J.: Reconciling verbal and nonverbal models of dyadic communication. Environ. Psychol. Nonverbal Behav. 2(1), 30–44 (1977)

    Article  Google Scholar 

  • Gaglio, S., Re, G.L., Morana, M.: Human activity recognition process using 3-d posture data. IEEE Trans. Hum.-Mach. Syst. 45(5), 586–597 (2015)

    Article  Google Scholar 

  • Gao, Y., Wallkötter, S., Obaid, M., Castellano, G.: Investigating deep learning approaches for human–robot proxemics. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1093–1098. IEEE (2018)

  • Gómez, J.V., Mavridis, N., Garrido, S.: Social path planning: generic human–robot interaction framework for robotic navigation tasks. In: 2nd International Workshop Cognitive Robotics Systems: Replicating Human Actions and Activities (2013)

  • Hall, E.T.: The Hidden Dimension. Doubleday & Company Inc., Garden City (1966)

    Google Scholar 

  • Henkel, Z., Bethel, C.L., Murphy, R.R., Srinivasan, V.: Evaluation of proxemic scaling functions for social robotics. IEEE Trans. Hum.-Mach. Syst. 44(3), 374–385 (2014)

    Article  Google Scholar 

  • Ibarra, L., Webb, C.: Advantages of fuzzy control while dealing with complex/unknown model dynamics: a quadcopter example. New Appl. Artif. Intell. 31, 93–121 (2016)

    Google Scholar 

  • Jalal, A., Kim, Y., Kamal, S., Farooq, A., Kim, D.: Human daily activity recognition with joints plus body features representation using kinect sensor. In: 2015 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–6. IEEE (2015)

  • Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing—a computational approach to learning and machine intelligence [book review]. IEEE Trans. Autom. Control 42(10), 1482–1484 (1997)

    Article  Google Scholar 

  • Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N.: An affective guide robot in a shopping mall. In: 2009 4th ACM/IEEE International Conference on Human–Robot Interaction (HRI), pp. 173–180. IEEE (2009)

  • Kaplan, K.J., Firestone, I.J., Klein, K.W., Sodikoff, C.: Distancing in dyads: a comparison of four models. Soc. Psychol. Q. 46, 108–115 (1983)

    Article  Google Scholar 

  • Kaptein, M.C., Nass, C., Markopoulos, P.: Powerful and consistent analysis of likert-type rating scales. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2391–2394 (2010)

  • Karreman, D., Utama, L., Joosse, M., Lohse, M., van Dijk, B., Evers, V.: Robot etiquette: how to approach a pair of people? In: Proceedings ACM/IEEE International Conference Human–Robot Interaction, pp. 196–197. ACM (2014)

  • Khaliq, A.A., Köckemann, U., Pecora, F., Saffiotti, A., Bruno, B., Recchiuto, C.T., Sgorbissa, A., Bui, H.D., Chong, N.Y.: Culturally aware planning and execution of robot actions. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 326–332. IEEE (2018)

  • Kosiński, T., Obaid, M., Woźniak, P.W., Fjeld, M., Kucharski, J.: A fuzzy data-based model for human–robot proxemics. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 335–340. IEEE (2016)

  • Ma, Z., Huang, P., Kuang, Z.: Fuzzy approximate learning-based sliding mode control for deploying tethered space robot. IEEE Trans. Fuzzy Syst. 29, 2739–2749 (2020)

    Article  Google Scholar 

  • Mead, R., Matarić, M.J.: Perceptual models of human–robot proxemics. In: Experimental Robotics, pp. 261–276. Springer (2016)

  • Mead, R., Matarić, M.J.: Autonomous human–robot proxemics: socially aware navigation based on interaction potential. Auton. Robot. 41(5), 1189–1201 (2017)

    Article  Google Scholar 

  • Mead, R., Atrash, A., Matarić, M.J.: Automated proxemic feature extraction and behavior recognition: applications in human–robot interaction. Int. J. Soc. Robot. 5(3), 367–378 (2013)

    Article  Google Scholar 

  • Mitsunaga, N., Smith, C., Kanda, T., Ishiguro, H., Hagita, N.: Adapting robot behavior for human-robot interaction. IEEE Trans. Rob. 24(4), 911–916 (2008)

    Article  Google Scholar 

  • Moyle, W., Bramble, M., Jones, C., Murfield, J.: Care staff perceptions of a social robot called Paro and a look-alike Plush Toy: a descriptive qualitative approach. Aging Mental Health 22(3), 330–335 (2018)

    Article  Google Scholar 

  • Muthugala, M.A.V.J., Jayasekara, A.G.B..P.: Mirob: an intelligent service robot that learns from interactive discussions while handling uncertain information in user instructions. In: Moratuwa Engineering Research Conference (MERCon), 2016, pp. 397–402. IEEE (2016)

  • Muthugala, M.A.V.J., Jayasekara, A.G.B.P.: Enhancing user satisfaction by adapting robot’s perception of uncertain information based on environment and user feedback. IEEE Access 5, 26435–26447 (2017)

    Article  Google Scholar 

  • Nguyen, H.T., Walker, C.L., Walker, E.A.: A First Course in Fuzzy Logic. CRC Press, Boca Raton (2018)

    Book  MATH  Google Scholar 

  • Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y.: Learning proxemics for personalized human–robot social interaction. Int. J. Soc. Robot. 12(1), 267–280 (2020)

    Article  Google Scholar 

  • Pérula-Martínez, R., Castro-González. Á., Malfaz, M., Salichs, M.A.: Autonomy in human–robot interaction scenarios for entertainment. In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 259–260. ACM (2017)

  • Phan, K.B., Ha, H.T., Hoang, S.V.: Eliminating the effect of uncertainties of cutting forces by fuzzy controller for robots in milling process. Appl. Sci. 10(5), 1685 (2020)

    Article  Google Scholar 

  • Rossi, S., Staffa, M., Bove, L., Capasso, R., Ercolano, G.: User’s personality and activity influence on HRI comfortable distances. In: International Conference on Social Robotics, pp. 167–177. Springer (2017)

  • Ruijten, P.A., Cuijpers, R.H.: Stopping distance for a robot approaching two conversating persons. In: 2017 26th IEEE International Symposium on Robotics and Human Interactive Communication (RO-MAN), pp. 224–229. IEEE (2017)

  • Samarakoon, S.M.B.P., Muthugala, M.A.V.J., Jayasekara, A.G.B.P.: Replicating natural approaching behavior of humans for improving robot’s approach toward two persons during a conversation. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 552–558. IEEE (2018a)

  • Samarakoon, S.M.B.P., Sirithunge, H.P.C., Muthugala, M.A.V.J., Jayasekara, A.G.B.P.: Proxemics and approach evaluation by service robot based on user behavior in domestic environment. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8192–8199. IEEE (2018b)

  • Samarakoon, S.M.B.P., Muthugala, M.A.V.J., Elara, M.R.: Toward obstacle-specific morphology for a reconfigurable tiling robot. J. Ambient Intell. Human. Comput. 1–13 (2021). https://doi.org/10.1007/s12652-021-03342-2

  • Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N.: How to approach humans? Strategies for social robots to initiate interaction. In: 2009 4th ACM/IEEE International Conference on Human–Robot Interaction (HRI), pp 109–116. IEEE (2009)

  • Shen, S., Tennent, H., Claure, H., Jung, M.: My telepresence, my culture? An intercultural investigation of telepresence robot operators’ interpersonal distance behaviors. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 51. ACM (2018)

  • Syrdal, D.S., Dautenhahn, K., Walters, M.L., Koay, K.L.: Sharing spaces with robots in a home scenario-anthropomorphic attributions and their effect on proxemic expectations and evaluations in a live HRI trial. In: AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems, pp. 116–123 (2008)

  • Tapus, A., Mataric, M.J., Scassellati, B.: Socially assistive robotics [grand challenges of robotics]. IEEE Robot. Autom. Mag. 14(1), 35–42 (2007)

    Article  Google Scholar 

  • Vitiello, A., Acampora, G., Staffa, M., Siciliano, B., Rossi, S.: A neuro-fuzzy-bayesian approach for the adaptive control of robot proxemics behavior. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

  • Walters, M.L.: The design space for robot appearance and behaviour for social robot companions. PhD thesis, University of Hertfordshire (2008)

  • Walters, M.L., Oskoei. M.A., Syrdal, D.S., Dautenhahn, K.: A long-term human–robot proxemic study. In: 2011 RO-MAN, pp. 137–142. IEEE (2011)

  • Wu, H., Pan, W., Xiong, X., Xu, S.: Human activity recognition based on the combined SVM & HMM. In: 2014 IEEE International Conference on Information and Automation (ICIA), pp. 219–224. IEEE (2014)

  • Yuan, W., Li, Z.: Development of a human-friendly robot for socially aware human–robot interaction. In: 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 76–81. IEEE (2017)

  • Zadeh, L.A.: Is there a need for fuzzy logic? Inf. Sci. 178(13), 2751–2779 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Authors would like to thank the participants of the user study.

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Correspondence to S. M. Bhagya P. Samarakoon.

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This work was supported by University of Moratuwa Senate Research Grant Number SRC/LT/2018/20.

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Samarakoon, S.M.B.P., Muthugala, M.A.V.J., Jayasekara, A.G.B.P. et al. Adapting approaching proxemics of a service robot based on physical user behavior and user feedback. User Model User-Adap Inter 33, 195–220 (2023). https://doi.org/10.1007/s11257-022-09329-8

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