Abstract
Personalization and localization are essential when developing social robots for different sectors, including education, industry, healthcare or restaurants. This requires adjusting the robot's behavior to an individual's needs, preferences, or personality when referring to personalization or the social convention or country's culture when referring to localization. Current literature presents different models that enable personalization and localization, each with its advantages and drawbacks. This work aims to help researchers in social robotics by reviewing and analyzing different papers in this domain. We focus our review on exploring various technical methods used to make decisions and adapt social robots’ non-verbal and verbal skills, including the state-of-the-art techniques in the sector of artificial intelligence.
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References
Gasteiger, N., Hellou, M., Ahn, H.: Factors for personalization and localization to optimize human-robot interaction: a literature review. Int. J. Soc. Robot. (2021). https://doi.org/10.1007/s12369-021-00811-8
Pieskä, S., Luimula, M., Jauhiainen, J., et al.: Social service robots in public and private environments. Circ. Syst. Multi. Autom. Control, 190–95 (2012)
Huang, C-M., Mutlu, B.: Robot behavior toolkit: generating effective social behaviors for robots. In: ACM/IEEE international conference on Human-Robot Interaction, pp. 25–32 (2012)
Kanda, T., Shiomi, M., Miyashita, Z., et al.: A communication robot in a shopping mall. IEEE Trans. Rob. 26(5), 897–913 (2010)
McColl, D., Nejat, G.: Meal-time with a socially assistive robot and older adults at a long-term care facility. J. Hum.-Robot. Interact. 2(1), 152–171 (2013)
Aly, A., Tapus, A.: A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction. In: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Tokyo, Japan: IEEE (2013)
Keizer, S., Foster, M., Wang, Z., et al.: Machine learning for social multiparty human–robot interaction. ACM Trans. Interact. Intell. Syst. 4(3), 1–32 (2014)
Liu, P., Glas, D.F., Kanda, T., Ishiguro, H.: Learning proactive behavior for interactive social robots. Auton. Robot. 42(5), 1067–1085 (2017). https://doi.org/10.1007/s10514-017-9671-8
Qureshi, A., Nakamura, Y., Yoshikawa, Y., et al.: Robot gains social intelligence through multimodal deep reinforcement learning. arxiv 2017
Portugal, D., Santos, L., Alvito, P., et al.: SocialRobot: an interactive mobile robot for elderly home care. In: IEEE/SICE International Symposium on System Integration (2015)
Foster, M., Craenen, B., Deshmukh, A., et al.: MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces. arXiv.org (2019)
Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming, 1st edn. John Wiley & Sons Inc, USA (1994)
Sekman, A., Challa, B.: Assessment of adaptive human–robot interactions. Knowl.-Based Syst. 42, 49–59 (2012)
Ekman, P., Friesen, W., O’Sullivan, M., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712–717 (1987)
Craenen, B., Deshmukh, A., Foster, M., et al.: Shaping gestures to shape personalities: the relationship between gesture parameters, attributed personality traits and godspeed scores. In: 27th IEEE International Conference on Robot and Human Interactive Communication. IEEE, Nanjing, China (2018)
Cassell, J., Vilhjálmsson, H., Bickmore, T.: BEAT: the behavior expression animation toolkit. In: Prendinger, H., Ishizuka, M., eds. Life-Like Characters. Cognitive Technologies. Berlin, Heidelberg, Springer (2004). https://doi.org/10.1007/978-3-662-08373-4_8
Yoon, Y., Ko, W-R., Jang, M., et al.: Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots. arXiv (2018)
The snackbot: documenting the design of a robot for long-term human-robot interaction. In: ACM/IEEE International Conference on Human-Robot Interaction (2009)
Churamani, N., Anton, P., Brügger, M., et al.: The impact of personalisation on human-robot interaction in learning scenarios. In: 5th International Conference on Human Agent Interaction HAI 2017. ACM, Bielefeld, Germany, pp. 171–80 (2017)
Torrey, C., Powers, A., Marge, M., et al.: Effects of adaptive robot dialogue on information exchange and social relations. In: 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction. Salt Lake City, Utah, USA, pp. 126–33 (2006)
Mairesse, F., Walker, M.: Controlling user perceptions of linguistic style: trainable generation of personality traits. Comput. Linguist. 37(3), 455–488 (2011)
Perera, V., Pereira, T., Connell, J., et al.: Setting up Pepper for autonomous navigation and personalized interaction with users. arXiv 2017
Acknowledgment
The project was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020–0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments).
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Hellou, M., Gasteiger, N., Ahn, H.S. (2021). Personalization and Localization to Improve Social Robots’ Behaviors: A Literature Review. In: Li, H., et al. Social Robotics. ICSR 2021. Lecture Notes in Computer Science(), vol 13086. Springer, Cham. https://doi.org/10.1007/978-3-030-90525-5_68
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DOI: https://doi.org/10.1007/978-3-030-90525-5_68
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