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User Modeling and User-Adapted Interaction (2023) 33:189–194https://doi.org/10.1007/s11257-023-09365-yPreface to the special issue on personalization andadaptation in human–robot interactive communicationSilvia Rossi1 ·Mariacarla Staffa2 ·Maartje M. A. de Graaf3 · Cristina Gena4Published online: 23 April 2023© The Author(s), under exclusive licence to Springer Nature B.V. 20231 IntroductionThe role of user-adaptive interfaces and interaction is widely recognized in the human–computer interaction (HCI) community to improve the acceptance and naturalness incomparison with non-adaptive ones (Martins et al. 2017). However, when dealing withrobots, to make the interaction socially acceptable, legible, and natural from the user’spoint of view additional challenges arise due to the physical presence of the robot andthe required multi-modal interaction and perception capabilities (Rossi et al. 2017).A lack of perceived adaptability and sociability are the two main reasons why peoplediscontinue the use of a robot, even after initial adoption (de Graaf et al. 2017). Indeed,creating robotic systems capable of correctly acquiring, recognizing, and consequently,modeling human behavior is still a challenging task for robots (Lee et al. 2012).Moreover, the need to correctly infer and model the users’ preferences, needs, andmotivations (Churamani et al. 2017) become very critical, especially in the domain ofsocially assistive robotics and when working with vulnerable user populations (Caoet al. 2019). A robot should be able to cope with local uncertainties of the environment,variations of human desires and motivations, and volatilities of the interaction itself.In addition, the embodiment condition of a robot requires the ability to extract suchB Silvia Rossisilvia.rossi@unina.itMariacarla Staffamariacarla.staffa@uniparthenope.itMaartje M. A. de Graafm.m.a.degraaf@uu.nlCristina Genacristina.gena@unito.it1 University of Naples Federico II, Napoli, Italy2 University of Naples Parthenope, Napoli, Italy3 Utrecht University, Utrecht, The Netherlands4 University of Turin, Torino, Italy123190 S. Rossi et al.relevant information from the interaction history but also from the indirect observationof the user (Rossi et al. 2020; Botta et al. 2022). Thus, a user modeling componentshould cope with these challenging and evolving requirements.With respect to software agents and vocal assistants, a robot requires also to considerthe physical characteristics of the interaction, such as the user preferences regardingthe robot’s physical movements in the space (e.g., proxemics, speed, and trajectories)(Syrdal et al. 2007). A personalized and adaptive interaction, different from pure reac-tive strategies, strongly relies on the learning of such computational models of humanbehavior and the integration of these into the decision-making algorithms of the robot(Martins et al. 2019). This includes also the possibility of endowing the robot withmeta-cognition capabilities such as the capability of reasoning on the other individ-uals’ intentions, desires, and beliefs, as well as their internal states, personality, andemotions (Gordon et al. 2016; Karpouzis et al. 2013; Leite et al. 2012). Finally, sincehuman–robot interaction (HRI) may rely on a complex set of multi-modal interac-tion modalities (Caccavale et al. 2014), the ability of a robot to adapt its multi-modalbehavior according to social expectations, specific cultural norms, and possible indi-vidual preferences, will determine the success and large-scale use of such roboticsapplications.This special issue aims at examining and promoting recent developments in per-sonalized and adaptive interactive communication in robotics, providing the UMUAIjournal with a different perspective related to the specific characteristics of the inter-action with a physical robot. The SI is composed of 11 articles addressing differentchallenges for user-adapted interaction in HRI.2 Papers in this issueAdaptation of the robot’s physical behavior is addressed in the paper fromSamarakoonet al.: “Adapting approaching proxemics of a service robot based on physical userbehavior and user feedback”. The manuscript proposes an approach that combineson-the-fly adaptation of the robot’s proxemics, evaluated by considering the currentphysical user behavior, with personal factors that are learned by a fuzzy neural networkvia user feedback. The fuzzy neural network perceives user behavior through dynamicparameters of skeletal joints to determine the appropriate termination position of anapproach. This determination can be adapted toward the preferences of a user bymodifying the internal parameters of the neural network based on user feedback.In the paper fromGarcía-Corretjer et al. “Empathy as an engaging strategy in socialrobotics: a pilot study”, adaptation is achieved by means of empathy which plays afundamental role in building relationships. Here empathy is not modeled throughpassive observation of a situation, but intended as the result of an active process wherethe actors actively collaborate to resolve a task by deciding together at each step whileexchanging their points of view. In this study, quantitative way indicators of earlyempathy realization between a human and a robot are evaluated using expectations,trust, and affective attachment toward the robot.Monitoring of the user’s state and reactions is also addressed in the paper fromAlmousa et al. “Conceptualization and development of an autonomous and person-123Preface to the special issue on personalization and... 191alized early literacy content and robot tutor behavior for preschool children”. Themanuscript analyzes personalization in learning and in particular the development ofan autonomous robot tutor with a personalization policy for preschool children. Per-sonalization is performed by automatically adjusting the difficulty level of the lessondelivery and assessment, as well as adjusting the feedback based on the reaction ofchildren. Results are evaluated in terms of the correctness of the answer, attention,and hesitation. The findings of the study reveal that the personalized interaction withthe robot showed a positive potential in increasing the children’s learning gains andattracting their engagement.On the same line, the work of Umbrico et al. “A dichotomic approach to adaptiveinteraction for socially assistive robots” proposes a solution based on a user modelgrounded on the international classification of functioning, disability, and health (ICF)and a novel control architecture inspired by the dual-process theory. In this paper, theauthors focus on a social robot in charge of the synthesis of personalized trainingsessions for the cognitive stimulation of older adults, customizing the adaptive verbalbehavior according to the characteristics of the users and their dynamic reactionswhen interacting. Given a user profile and a set of inferred situation opportunities, apersonalized assistive plan can be provided taking into account the set of stimuli that“best” address the impairments of a person, while a policy-based approach is used formanaging the interaction.How to include preferences in the decision-making system of the robot is alsoaddressed in the contribution from Canal et al. “Generating predicate suggestionsbased on the space of plans: an example of planning with preferences”. The paperdeals with the role of user preferences in task planning for human–robot environmentswhich are usually hard to obtain. To deal with this issue, the authors present a space-of-plan-based suggestion approach which provides suggestions for some planningpredicates (e.g., the robot speed) to the users taking into account the configurationvalues that have already been set.The paper from Maroto-Gómez et al. “An adaptive decision-making system sup-ported on user preference predictions for human–robot interactive communication”also deals with the decision-making system of a social robot to produce a personal-ized interactive communication experience by considering the preferences of the user.The main contribution is the development of a preference learning framework, usingthe label ranking (LR) method, that allows a social robot to suggest their preferredactivities to each user balancing activity exploration with the selection of the favoriteentertaining activities.Personalization and adaptation play a fundamental role when dealing with assis-tive applications. The paper from Di Napoli et al. “Personalized home-care supportfor the elderly: a field experience with a social robot at home” deals with person-alization and adaptation for socially assistive robotics applications for people withmild cognitive impairments in home environments. Personalization is based on theuser’s characteristics such as clinical needs, personality, and cognitive profile, butalso on environmental conditions, while an adaptation process takes into accountdynamic changes. Functional and non-functional aspects of personalization are mod-eled through a service-oriented approach. Acceptability and reliability results ofinto-the-wild experimentations with seven patients are reported showing the feasibil-123192 S. Rossi et al.ity and a positive evaluation for such robotic application in a not controlled domesticenvironment.Short-term adaptivity and personalization of robot socially assistive behaviorare explored also in the paper from Andriella et al. “Introducing CARESSER: Aframework for in situ learning robot social assistance from expert knowledge anddemonstrations”. The manuscript introduces a framework for actively learning roboticassistive behavior by leveraging the therapist’s expertise (knowledge-driven approach)and their demonstrations (data-driven approach) while providing cognitive exercises.To achieve that, a patient-specific simulator, which models the patient and the robotat a symbolic high level, was employed to generate data. The evaluation of the frame-work is conducted with two user studies in a daily care center in which older adultsare affected by mild dementia and mild cognitive impairment.The contribution of the paper from Irfan et al. “Personalized socially assistiverobot for cardiac rehabilitation: Critical reflections on long-term interactions in thereal world” is a better understanding of whether and to what extent personalization ofa social robot in a robot-based rehabilitation program may considerably improve ther-apeutic efficacy over traditional programs and programs supported by a less sociablerobot. This paper presents a complete real-world study that lasted 2.5 years, drawingupon results from 26 patients that completed the program focusing on the benefits andchallenges of personalization in long-term interactions in the real world.While most prior work on social robot exercise coaching systems has utilizedgeneric, predefined feedback, the work of Hun Lee et al. “Design, development, andevaluation of an interactive personalized social robot to monitor and coach post-strokerehabilitation exercises” aims at iteratively engaging therapists and post-stroke sur-vivors to design, develop, and evaluate a social robot exercise coaching system forpersonalized rehabilitation. The paper presents a social robot exercise coaching sys-tem that integrates a neural network model with a rule-based model to automaticallymonitor and assess patients’ rehabilitation exercises and can be tuned with individualpatients’ data to generate real-time, personalized corrective feedback for improvement.Finally, the study presented in the paper from Jeong et al. “Deploying a roboticpositive psychology coach to improve college students’ psychological well-being”investigates the influence of a robot coach, which is meant to give interactive psy-chological therapies to students in on-campus dorms, on students’ mental health. Theone-week study with 35 students found that robot interventions significantly increasestudents’ well-being, mood, and readiness to modify their behavior to improve theirwell-being after the study. The researchers also discovered a link between participants’personality qualities and the intervention’s outcome, as well as their working alliancewith the robot and overall satisfaction.ReferencesBotta, M., Camilleri, D., Cena, F., Sario, F., Gena, C., Ignone, G., Mattutino, C.: Cloud-based user mod-eling for social robots: a first attempt. In: Workshop on Adapted intEraction with SociAl Robots(cAESAR’20), in conjunction with the 25th ACM conference on Intelligent User Interfaces, pp. 1–6(2022)123Preface to the special issue on personalization and... 193Caccavale, R., Leone, E., Lucignano, L., Rossi, S., Staffa, M., Finzi, A.: Attentional regulations in a situatedhuman-robot dialogue. In: 23rd IEEE International Symposium on Robot and Human InteractiveCommunication, pp. 844–849 (2014)Cao, H.L., Van de Perre, G., Kennedy, J., Senft, E., Gómez Esteban, P., De Beir, A., Simut, R., Belpaeme, T.,Lefeber, D., Vanderborght, B.: A personalized and platform-independent behavior control system forsocial robots in therapy: development and applications. IEEE Trans. Cogn. Dev. Syst. 11(3), 334–346(2019)Churamani, N., Anton, P., Brügger, M., Fließwasser, E., Hummel, T., Mayer, J., Mustafa, W., Ng, H.G.,Nguyen, T.L.C., Nguyen, Q., Soll, M., Springenberg, S., Griffiths, S., Heinrich, S., Navarro-Guerrero,N., Strahl, E., Twiefel, J., Weber, C., Wermter, S.: The impact of personalisation on human-robotinteraction in learning scenarios. In: Proceedings of the 5th International Conference on Human AgentInteraction,Association for ComputingMachinery,NewYork,NY,USA,HAI ’17, pp. 171–180 (2017)Gordon, G., Spaulding, S., Westlund, J.K., Lee, J.J., Plummer, L., Martinez, M., Das, M., Breazeal, C.:Affective personalization of a social robot tutor for children’s second language skills. In: Proceedingsof the Thirtieth AAAI Conference on Artificial Intelligence, AAAI Press, AAAI’16, pp. 3951–3957(2016)de Graaf, M., Ben Allouch, S., van Dijk, J.:Why do they refuse to use my robot? reasons for non-use derivedfrom a long-term home study. In: Proceedings of the 2017 ACM/IEEE International Conference onHuman-Robot Interaction, Association for Computing Machinery, New York, NY, USA, HRI ’17, pp.224–233 (2017)Karpouzis, K., Yannakakis, G., Paiva, A., Nielsen, J.H., Vasalou, A., Jhala, A.: User modelling and adap-tive, natural interaction for conflict resolution. In: Humaine Association Conference on AffectiveComputing and Intelligent Interaction, pp. 719–721 (2013)Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S.: Personalization in hri: A longi-tudinal field experiment. In: 7th ACM/IEEE International Conference on Human-Robot Interaction(HRI), pp. 319–326 (2012)Leite, I., Pereira, A., Castellano, G., Mascarenhas, S., Martinho, C., Paiva, A.: Modelling empathy in socialrobotic companions. In: Advances inUserModeling, pp. 135–147. Springer, Berlin, Heidelberg (2012)Martins, G.S., Santos, L., Dias, J.: Bum: Bayesian user model for distributed social robots. In: 26th IEEEInternational Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1279–1284 (2017)Martins, G.S., Santos, L., Dias, J.: User-adaptive interaction in social robots: a survey focusing on non-physical interaction. Int. J. Soc. Robot. 11(1), 185–205 (2019)Rossi, S., Ferland, F., Tapus, A.: User profiling and behavioral adaptation for hri: a survey. Pattern Recognit.Lett. 99, 3–12 (2017)Rossi, S., Rossi, A., Dautenhahn, K.: The secret life of robots: perspectives and challenges for robot’sbehaviours during non-interactive tasks. Int. J. Soc. Robot. 12(6), 1265–1278 (2020)Syrdal, D.S., Lee Koay, K., Walters, M.L., Dautenhahn, K.: A personalized robot companion?-the role ofindividual differences on spatial preferences in hri scenarios. In: RO-MAN 2007 - The 16th IEEEInternational Symposium on Robot and Human Interactive Communication, pp. 1143–1148 (2007)Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published mapsand institutional affiliations.Silvia Rossi is an associate professor in Computer Science at the Department of Electrical Engineering andInformation Technologies of the University of Naples Federico II. She is currently the chief manager ofthe PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) lab. Her research interestsinclude Artificial Intelligence, Socially Assistive Robotics, Cognitive Robotics, and Human-Robot Inter-action.Mariacarla Staffa is an assistant professor in Artificial Intelligence and Cognitive Robotics and ComputerScience at the University of Naples Parthenope, Italy. She is mainly interested in exploring computationalneuroscience and cognitive robotics to generate innovative strategies and solutions for scientific problemsand technological limitations. She authored several works on Social Assistive Robots, Adaptive Human-Robot Interaction, Human Behavior, and Emotion interpretation.123194 S. Rossi et al.Maartje M. A. de Graaf is an assistant professor in Human-Computer Interaction at the department ofInformation and Computing Sciences at Utrecht University, The Netherlands. Her research focuses onpeople’s affective, behavioral, and cognitive responses to robots generating knowledge to inform thedevelopment of socially acceptable robots that benefit society.Cristina Gena is currently an associate professor at the Department of Computer Science, University ofTorino, and President of the Master Degree in Communication, ICT, and Media (CIME). She teaches atthe School of ICT (social Innovation Communication and new Technologies), and at the Bachelor in Com-puter Science, and she is part of the PhD Commitee in Computer science. She is in charge of the roboticsand advanced interactions laboratory of her department, and she is heading the smart HCI lab of the ICxTInnovation center.123

Preface to the special issue on personalization and adaptation in human–robot interactive communication

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Preface to the special issue on personalization and adaptation in human–robot interactive communication

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