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Perceived Safety in Social Human-Robot Interaction
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-6168-0706
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This compilation thesis contributes to a deeper understanding of perceived safety in human-robot interaction (HRI) with a particular focus on social robots. The current understanding of safety in HRI is mostly limited to physical safety, whereas perceived safety has often been neglected and underestimated. However, safe HRI requires a conceptualization of safety that goes beyond physical safety covering also perceived safety of the users. Within this context, this thesis provides a comprehensive analysis of perceived safety in HRI with social robots, considering a diverse set of human-related and robot-related factors.

Two particular challenges for providing perceived safety in HRI are 1) understanding and evaluating human safety perception through direct and indirect measures, and 2) utilizing the measured level of perceived safety for adapting the robot behaviors. The primary contribution of this dissertation is in addressing the first challenge. The thesis investigates perceived safety in HRI by alternating between conducting user studies, literature review, and testing the findings from the literature within user studies.

In this thesis, six main factors influencing perceived safety in HRI are lifted: the context of robot use, the user’s comfort, experience and familiarity with robots, trust, sense of control over the interaction, and transparent and predictable robot behaviors. These factors could provide a common understanding of perceived safety and bridge the theoretical gap in the literature. Moreover, this thesis proposes an experimental paradigm to observe and quantify perceived safety using objective and subjective measures. This contributes to bridging the methodological gap in the literature.

The six factors are reviewed in HRI literature, and the robot features that affect these factors are organized in a taxonomy. Although this taxonomy focuses on social robots, the identified characteristics are relevant to other types of robots and autonomous systems. In addition to the taxonomy, the thesis provides a set of guidelines for providing perceived safety in social HRI. As a secondary contribution, the thesis presents an overview of reinforcement learning applications in social robotics as a suitable learning mechanism for adapting the robots’ behaviors to mitigate psychological harm.

Place, publisher, year, edition, pages
Örebro: Örebro University , 2022. , p. 77
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 94
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-98102ISBN: 9789175294322 (print)OAI: oai:DiVA.org:oru-98102DiVA, id: diva2:1645315
Public defence
2022-04-28, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2022-03-17 Created: 2022-03-17 Last updated: 2022-05-04Bibliographically approved
List of papers
1. An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
Open this publication in new window or tab >>An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
2017 (English)In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper, Published paper (Refereed)
Abstract [en]

The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

Place, publisher, year, edition, pages
Springer International Publishing, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10652
Keywords
Sense of safety, Sense of security, Eldercare, Video-based evaluation, Quantitative evaluation tool
National Category
Computer Systems Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62768 (URN)10.1007/978-3-319-70022-9_62 (DOI)000449941100062 ()2-s2.0-85035814295 (Scopus ID)978-3-319-70022-9 (ISBN)978-3-319-70021-2 (ISBN)
Conference
9th International Conference on Social Robotics (ICSR 2017), Tsukuba, Japan, November 22-24, 2017
Projects
SOCRATES
Funder
EU, Horizon 2020, 721619
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2024-01-16Bibliographically approved
2. Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
Open this publication in new window or tab >>Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
2019 (English)In: Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction / [ed] Oliver Korn, Springer, 2019, p. 237-264Chapter in book (Refereed)
Abstract [en]

For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

Place, publisher, year, edition, pages
Springer, 2019
Series
Human-Computer Interaction Series, ISSN 1571-5035, E-ISSN 2524-4477
Keywords
Sense of safety and security, Quantitative evaluation tool, Social robots, Elder care
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-78493 (URN)10.1007/978-3-030-17107-0_12 (DOI)978-3-030-17106-3 (ISBN)978-3-030-17107-0 (ISBN)
Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
3. The Influence of Feedback Type in Robot-Assisted Training
Open this publication in new window or tab >>The Influence of Feedback Type in Robot-Assisted Training
2019 (English)In: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 3, no 4Article in journal (Refereed) Published
Abstract [en]

Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute, 2019
Keywords
feedback, acceptance, flattering robot, sense of safety and security, robot-assisted training
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-78492 (URN)10.3390/mti3040067 (DOI)000623570700003 ()2-s2.0-85079720466 (Scopus ID)
Funder
EU, Horizon 2020, 721619
Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
4. Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
Open this publication in new window or tab >>Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
2022 (English)In: International journal of human-computer studies, ISSN 1071-5819, E-ISSN 1095-9300, Vol. 158, article id 102744Article in journal (Refereed) Published
Abstract [en]

Safety in human-robot interaction can be divided into physical safety and perceived safety, where the later is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We conducted a two-by-five mixed-subjects design experiment. There were two between-subjects conditions: the faulty robot was experienced at the beginning or the end of the interaction. The five within-subjects conditions correspond to (1) baseline, and the manipulations of robot behaviors to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased sense of control and (5) distrust. The idea of triggering a deprivation of these factors was motivated by the definition of safety in the literature where safety is often defined by the absence of it. Twenty-seven young adult participants took part in the experiments. Participants were asked to answer questionnaires that measure the manipulated factors after within-subjects conditions. Besides questionnaire data, we collected objective measures such as videos and physiological data. The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety. Since these factors are the main factors that influence perceived safety, they should be considered in human-robot interaction design decisions. We also discuss the effect of individual human characteristics (such as personality and gender) that they could be predictors of perceived safety. We used the physiological signal data and facial affect from videos for estimating perceived safety where participants’ subjective ratings were utilized as labels. The data from objective measures revealed that the prediction rate was higher from physiological signal data. This paper can play an important role in the goal of better understanding perceived safety in human-robot interaction.

Place, publisher, year, edition, pages
Academic Press, 2022
Keywords
Perceived safety, Human robot interaction, Comfort, Sense of control, Trust, Physiological signal data, Facial expressions, Multidisciplinary perspective
National Category
Robotics
Research subject
Human-Computer Interaction; Computer Science
Identifiers
urn:nbn:se:oru:diva-95673 (URN)10.1016/j.ijhcs.2021.102744 (DOI)000782270600008 ()2-s2.0-85119702541 (Scopus ID)
Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-16Bibliographically approved
5. Reinforcement Learning Approaches in Social Robotics
Open this publication in new window or tab >>Reinforcement Learning Approaches in Social Robotics
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, article id 1292Article, review/survey (Refereed) Published
Abstract [en]

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Human-robot interaction, physical embodiment, reinforcement learning, reward design, social robotics
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-90245 (URN)10.3390/s21041292 (DOI)000624663200001 ()33670257 (PubMedID)2-s2.0-85100651693 (Scopus ID)
Funder
EU, Horizon 2020, 721619
Available from: 2021-03-08 Created: 2021-03-08 Last updated: 2024-01-16Bibliographically approved
6. Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
Open this publication in new window or tab >>Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
(English)Manuscript (preprint) (Other academic)
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-98456 (URN)
Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2022-04-04Bibliographically approved

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