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User Perception of the Robot’s Error in Heterogeneous Multi-robot System Performing Sequential Cooperative Task

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Social Robotics (ICSR 2023)

Abstract

This study investigates how the user’s mental model of an error in a heterogeneous multi-robot system (MRS) is formed while executing a sequential cooperative task. We applied a heterogeneous MRS consisting of a delivery robot and an information robot to the medicine delivery service in the infectious disease ward where the separation of the contaminated area and non-contaminated area is necessary to avoid infections. In this study, we define the robot which initiates the service in a sequential task as an initiating robot and the robot which executes the subsequent task following after the initiating robot as the follow-up robot. We examine how the initiating robot’s error would affect the user perception of the follow-up robot’s error and how the perceived error influences competence and trust in the follow-up robot. As a result, we found that when two robots cooperate sequentially, the user's evaluation of the follow-up robot’s error can be influenced by the initial robot’s error. In addition, we discovered that the trust and competence of the follow-up robot can be impacted by the initiating robot in heterogeneous MRS performing sequential cooperative tasks.

This work was supported by the Korea Institute of Science and Technology (KIST) Institutional Program under Grant (2E32302) and the Government-wide R&D Fund for Infections Disease Research (GFID), funded by the Ministry of the Interior and Safety, Republic of Korea (20014463).

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References

  1. Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robot.. Robot. 8(3), 345–383 (2000)

    Article  Google Scholar 

  2. Oh, G., Kim, Y., Ahn, J., Choi, H.L.: PSO-based optimal task allocation for cooperative timing missions. IFAC-PapersOnLine 49(17), 314–319 (2016)

    Article  Google Scholar 

  3. Abukhalil, T., Patil, M., Patel, S., Sobh, T.: Coordinating a heterogeneous robot swarm using Robot Utility-based Task Assignment (RUTA). In: 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC), pp. 57–62 (2016)

    Google Scholar 

  4. Kim, M.H., Baik, H., Lee, S.: Resource welfare based task allocation for UAV team with resource constraints. J. Intell. Rob. Syst.Intell. Rob. Syst. 77(3), 611–627 (2014)

    Google Scholar 

  5. Benavidez, P., Kumar, M., Agaian, S., Jamshidi, M.: Design of a home multi-robot system for the elderly and disabled. In: Proceedings of the 10th System of Systems Engineering Conference (SoSE), pp. 392–397 (2015)

    Google Scholar 

  6. Kraus, S.: Intelligent agents for rehabilitation and care of disabled and chronic patients. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4032–4036 (2015)

    Google Scholar 

  7. Murphy, R.R., Gandudi, V.B., Amin, T., Clendenin, A., Moats, J.: An analysis of international use of robots for COVID-19. Robot. Auton. Syst.Auton. Syst. 148, 103922 (2022)

    Article  Google Scholar 

  8. Mills, E.C., Savage, E., Lieder, J., Chiu, E.S.: Telemedicine and the COVID-19 pandemic: are we ready to go live? Adv. Skin Wound Care 33(8), 410–417 (2020)

    Article  Google Scholar 

  9. Yang, G.Z., et al.: Combating COVID-19-the role of robotics in managing public health and infectious diseases. Sci. Robot. 5(40), 1–2 (2020)

    Article  Google Scholar 

  10. Shajahan, A., Culp, C.H., Williamson, B.: Effects of indoor environmental parameters related to building heating, ventilation, and air conditioning systems on patients’ medical outcomes: a review of scientific research on hospital buildings. Indoor Air 29(2), 161–176 (2019)

    Article  Google Scholar 

  11. Sundell, J., et al.: Ventilation rates and health: multidisciplinary review of the scientific literature. Indoor Air 21(3), 191–204 (2011)

    Article  Google Scholar 

  12. Al-Benna, S.: Negative pressure rooms and COVID-19. J. Perioper. Pract.Perioper. Pract. 31(1–2), 18–23 (2020)

    Google Scholar 

  13. Ragni, M., Rudenko, A., Kuhnert, B., Arras, K.O.: Errare humanum EST: erroneous robots in human-robot interaction. In: Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 501–506 (2016)

    Google Scholar 

  14. Kim, T., Hinds, P.: Who should I blame? Effects of autonomy and transparency on attributions in human-robot interaction. In: Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 80–85 (2006)

    Google Scholar 

  15. Furlough, C., Stokes, T., Gillan, D.J.: Attributing blame to robots: I. The influence of robot autonomy. Hum. Fact. 63(4), 592–602 (2021)

    Google Scholar 

  16. Das, T.K., Teng, B.S.: The risk-based view of trust: a conceptual framework. J. Bus. Psychol. 19(1), 85–116 (2004)

    Article  Google Scholar 

  17. Groom, V., Nass, C.: Can robots be teammates?: Benchmarks in human–robot teams. Interact. Stud. 8(3), 483–500 (2007)

    Article  Google Scholar 

  18. Greca, I.M., Moreira, M.A.: Mental models, conceptual models, and modelling. Int. J. Sci. Educ. 22(1), 1–11 (2010)

    Article  Google Scholar 

  19. Carroll, J.M., Olson, J.R.: Mental Models in Human-Computer Interaction. In: Helander, M. (ed.) Handbook of Human-Computer Interaction, pp. 45–65. Elsevier North Holland, Amsterdam (1988)

    Chapter  Google Scholar 

  20. Gentner, D., Stevens, A.L.: Mental Models. Psychology Press, New York (2014)

    Book  Google Scholar 

  21. Leo, X., Huh, Y.E.: Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms. Comput. Hum. Behav.. Hum. Behav. 113, 1–13 (2020)

    Google Scholar 

  22. Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K.: Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust. In: Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 1–8 (2015)

    Google Scholar 

  23. Dahlbäck, N., Jönsson, A., Ahrenberg, L.: Wizard of Oz studies—why and how. Knowl.-Based Syst..-Based Syst. 6(4), 258–266 (1993)

    Article  Google Scholar 

  24. Lee, M.K., Kiesler, S., Forlizzi, J., Srinivasa, S., Rybski, P.: Gracefully mitigating breakdowns in robotic services. In: Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 203–210 (2010)

    Google Scholar 

  25. Surprenant, C.F., Solomon, M.R.: Predictability and personalization in the service encounter. J. Mark. 51(2), 86–96 (2018)

    Article  Google Scholar 

  26. Fogg, B.J., Tseng, H.: The elements of computer credibility. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 80–87 (2019)

    Google Scholar 

  27. Beggiato, M., Krems, J.F.: The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information. Transport. Res. F: Traffic Psychol. Behav. 18, 47–57 (2013)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the Korea Institute of Science and Technology (KIST) Institutional Program under Grant (2E32302) and the Government-wide R&D Fund for Infections Disease Research (GFID), funded by the Ministry of the Interior and Safety, Republic of Korea (20014463).

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Correspondence to Sonya S. Kwak .

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Shin, S., Kwon, Y., Lim, Y., Kwak, S.S. (2024). User Perception of the Robot’s Error in Heterogeneous Multi-robot System Performing Sequential Cooperative Task. In: Ali, A.A., et al. Social Robotics. ICSR 2023. Lecture Notes in Computer Science(), vol 14454. Springer, Singapore. https://doi.org/10.1007/978-981-99-8718-4_28

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  • DOI: https://doi.org/10.1007/978-981-99-8718-4_28

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  • Online ISBN: 978-981-99-8718-4

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