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Artículos de revistas sobre el tema "Robot-Robot interaction"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Qu, Jingtao, Mateusz Jarosz, and Bartlomiej Sniezynski. "Robot Control Platform for Multimodal Interactions with Humans Based on ChatGPT." Applied Sciences 14, no. 17 (2024): 8011. http://dx.doi.org/10.3390/app14178011.

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This paper presents the architecture of a multimodal human–robot interaction control platform that leverages the advanced language capabilities of ChatGPT to facilitate more natural and engaging conversations between humans and robots. Implemented on the Pepper humanoid robot, the platform aims to enhance communication by providing a richer and more intuitive interface. The motivation behind this study is to enhance robot performance in human interaction through cutting-edge natural language processing technology, thereby improving public attitudes toward robots, fostering the development and

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2

Bharatharaj, Jaishankar, Loulin Huang, Ahmed Al-Jumaily, Rajesh Elara Mohan, and Chris Krägeloh. "Sociopsychological and physiological effects of a robot-assisted therapy for children with autism." International Journal of Advanced Robotic Systems 14, no. 5 (2017): 172988141773689. http://dx.doi.org/10.1177/1729881417736895.

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This article reports our findings from a robot-assisted therapeutic study conducted over 49 days to investigate the sociopsychological and physiological effects in children with autism spectrum disorder using a parrot-inspired robot, KiliRo, that we developed to help in therapeutic settings. We investigated the frequency of participants’ interactions among each other and assessed any changes in interaction using social network analysis. Interactions were assessed through manual observation before and after exposure to the robot. Urinary and salivary tests were performed to obtain protein and α

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3

Lai, Yujun, Gavin Paul, Yunduan Cui, and Takamitsu Matsubara. "User intent estimation during robot learning using physical human robot interaction primitives." Autonomous Robots 46, no. 2 (2022): 421–36. http://dx.doi.org/10.1007/s10514-021-10030-9.

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AbstractAs robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human

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4

Lee, Heejin. "A Human-Robot Interaction Entertainment Pet Robot." Journal of Korean Institute of Intelligent Systems 24, no. 2 (2014): 179–85. http://dx.doi.org/10.5391/jkiis.2014.24.2.179.

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5

Mitsunaga, N., C. Smith, T. Kanda, H. Ishiguro, and N. Hagita. "Adapting Robot Behavior for Human--Robot Interaction." IEEE Transactions on Robotics 24, no. 4 (2008): 911–16. http://dx.doi.org/10.1109/tro.2008.926867.

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6

Park, Eunil, and Jaeryoung Lee. "I am a warm robot: the effects of temperature in physical human–robot interaction." Robotica 32, no. 1 (2013): 133–42. http://dx.doi.org/10.1017/s026357471300074x.

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SUMMARYWhat factors affect users' perceptions of physical human–robot interactions? To answer this question, this study examined whether the skin temperature of a social robot affected users' perceptions of the robot during physical interaction. Results from a between-subjects experiment (warm, intermediate, cool, or no interaction) with a dinosaur robot demonstrated that skin temperature significantly affects users' perceptions and evaluations of a socially interactive robot. Additionally, this study found that social presence had partial mediating effects on several dependent variables. Impo

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7

Takamatsu, Jun. "Human-Robot Interaction." Journal of the Robotics Society of Japan 37, no. 4 (2019): 293–96. http://dx.doi.org/10.7210/jrsj.37.293.

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8

Jia, Yunyi, Biao Zhang, Miao Li, Brady King, and Ali Meghdari. "Human-Robot Interaction." Journal of Robotics 2018 (October 1, 2018): 1–2. http://dx.doi.org/10.1155/2018/3879547.

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9

Murphy, Robin, Tatsuya Nomura, Aude Billard, and Jennifer Burke. "Human–Robot Interaction." IEEE Robotics & Automation Magazine 17, no. 2 (2010): 85–89. http://dx.doi.org/10.1109/mra.2010.936953.

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10

Sethumadhavan, Arathi. "Human-Robot Interaction." Ergonomics in Design: The Quarterly of Human Factors Applications 20, no. 3 (2012): 27–28. http://dx.doi.org/10.1177/1064804612449796.

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11

Sheridan, Thomas B. "Human–Robot Interaction." Human Factors: The Journal of the Human Factors and Ergonomics Society 58, no. 4 (2016): 525–32. http://dx.doi.org/10.1177/0018720816644364.

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12

Pearson, Yvette. "Child-Robot Interaction." American Scientist 108, no. 1 (2020): 16. http://dx.doi.org/10.1511/2020.108.1.16.

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13

Jones, Keith S., and Elizabeth A. Schmidlin. "Human-Robot Interaction." Reviews of Human Factors and Ergonomics 7, no. 1 (2011): 100–148. http://dx.doi.org/10.1177/1557234x11410388.

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14

Couto, Marta, Shruti Chandra, Elmira Yadollahi, and Vicky Charisi. "Child-robot interaction." Interaction Studies 23, no. 2 (2022): 151–56. http://dx.doi.org/10.1075/is.00014.edi.

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15

Robins, Ben, Kerstin Dautenhahn, and Janek Dubowski. "Does appearance matter in the interaction of children with autism with a humanoid robot?" Interaction Studies 7, no. 3 (2006): 479–512. http://dx.doi.org/10.1075/is.7.3.16rob.

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This article studies the impact of a robot’s appearance on interactions involving four children with autism. This work is part of the Aurora project with the overall aim to support interaction skills in children with autism, using robots as ‘interactive toys’ that can encourage and mediate interactions. We follow an approach commonly adopted in assistive robotics and work with a small group of children with autism. This article investigates which robot appearances are suitable to encourage interactions between a robot and children with autism. The children’s levels of interaction with and resp

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16

Dimitrova, Maya, Snezhina Mihailova, Daniela Milanova, and Ivan Kolev. "Construct Validity in Child-Robot Interaction Research." Complex Control Systems, ISSN 2603-4697 (Online) 4, no. 1 (2022): 22–25. https://doi.org/10.5281/zenodo.6670187.

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The paper provides a review of existing approaches to understanding and defining validity in child-robot interaction research. Two main approaches are contrasted: construct validity in defining the phenomena, emerging in the process of interaction of the child with the robot, and system validity, defining the robot solely as a technological device. Examples are provided within a newly proposed approach, aiming at overcoming the existing theoretical debate in understanding construct validity in child-robot interaction research.

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17

Akmaev, Vladislav A., and Dmitry S. Kornienko. "Dynamics of negative attitudes and anxiety toward interaction with humanoid robot: an experimental study." Theoretical and experimental psychology 18, no. 1 (2025): 9–26. https://doi.org/10.11621/tep-25-01.

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Background. The introduction of robotic technologies into various aspects of life and activity inevitably raises questions about human-robot interaction and attitudes toward robots as participants in these interactions. Currently, there are few experimental studies examining the dynamics of attitudes toward robots during real-world interactions. Objective. The study had its purpose to investigate the impact of interaction with a social robot VitruBot and the influence of interaction format (professional, free, and observational) on the dynamics of negative attitudes and anxiety toward robots.

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18

Kim, Yoon-Sang, Kwang-Ho Seok, Chang-Mug Lee, and Oh-Young Kwon. "A Robot Motion Authoring Using Finger-Robot Interaction." Journal of information and communication convergence engineering 8, no. 2 (2010): 180–84. http://dx.doi.org/10.6109/jicce.2010.8.2.180.

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19

Berg, Julia, Albrecht Lottermoser, Christoph Richter, and Gunther Reinhart. "Human-Robot-Interaction for mobile industrial robot teams." Procedia CIRP 79 (2019): 614–19. http://dx.doi.org/10.1016/j.procir.2019.02.080.

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20

Du, Guanglong, Mingxuan Chen, Caibing Liu, Bo Zhang, and Ping Zhang. "Online Robot Teaching With Natural Human–Robot Interaction." IEEE Transactions on Industrial Electronics 65, no. 12 (2018): 9571–81. http://dx.doi.org/10.1109/tie.2018.2823667.

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21

Su, Wei Hua, Jing Gong Sun, Fu Niu, and Xin Yue Xu. "The Human-Robot Interaction: An Investigation of Rescue Robot." Advanced Materials Research 711 (June 2013): 523–28. http://dx.doi.org/10.4028/www.scientific.net/amr.711.523.

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The thesis research aimed to further the study of human-robot interaction (HRI) issues, especially regarding the development of rescue robot. The paper firstly discussed the status of the rescue robot and described the framework of human-robot interaction of search-rescue robot and rescue-evacuation robot. Subsequently, the general HRI issues will be discussed to explain how they affect the use of robots. Finally, we present suggested this multidisciplinary field of research, namely human-robot interaction, requires contributions from a variety of research fields such as robotics, human-comput

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22

Ros, Raquel, Elettra Oleari, Clara Pozzi, et al. "A Motivational Approach to Support Healthy Habits in Long-term Child--Robot Interaction." International Journal of Social Robotics 8, no. 5 (2016): 599–617. https://doi.org/10.1007/s12369-016-0356-9.

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We examine the use of role-switching as an intrinsic motivational mechanism to increase engagement in long-term child--robot interaction. The present study describes a learning framework where children between 9 and 11-years-old interact with a robot to improve their knowledge and habits with regards to healthy life-styles. Experiments were carried out in Italy where 41 children were divided in three groups interacting with: (i) a robot with a role-switching mechanism, (ii) a robot without a role-switching mechanism and (iii) an interactive video. Additionally, a control group composed of 43 m

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23

Tanaka, Ryosuke, Jinseok Woo, and Naoyuki Kubota. "Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (2019): 584–91. http://dx.doi.org/10.20965/jaciii.2019.p0584.

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The research and development of robot partners have been actively conducted to support human daily life. Human-robot interaction is one of the important research field, in which verbal and nonverbal communication are essential elements for improving the interactions between humans and robots. Thus, the purpose of this research was to establish a method to adapt a human-robot interaction mechanism for robot partners to various situations. In the proposed system, the robot needs to analyze the gestures of humans to interact with them. Humans have the ability to interact according to dynamically

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24

Lee, Youngho, Young Jae Ryoo, and Jongmyung Choi. "Framework for Interaction Among Human–Robot-Environment in DigiLog Space." International Journal of Humanoid Robotics 11, no. 04 (2014): 1442005. http://dx.doi.org/10.1142/s0219843614420055.

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With the development of computing technology, robots are now popular in our daily life. Human–robot interaction is not restricted to a direct communication between them. The communication could include various different human to human interactions. In this paper, we present a framework for enhancing the interaction among human–robot-environments. The proposed framework is composed of a robot part, a user part, and the DigiLog space. To evaluate the proposed framework, we applied the framework into a real-time remote robot-control platform in the smart DigiLog space. We are implementing real ti

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25

Shiomi, Masahiro, Hidenobu Sumioka, and Hiroshi Ishiguro. "Special Issue on Human-Robot Interaction in Close Distance." Journal of Robotics and Mechatronics 32, no. 1 (2020): 7. http://dx.doi.org/10.20965/jrm.2020.p0007.

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As social robot research is advancing, the interaction distance between people and robots is decreasing. Indeed, although we were once required to maintain a certain physical distance from traditional industrial robots for safety, we can now interact with social robots in such a close distance that we can touch them. The physical existence of social robots will be essential to realize natural and acceptable interactions with people in daily environments. Because social robots function in our daily environments, we must design scenarios where robots interact closely with humans by considering v

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26

Tapus, Adriana, Andreea Peca, Amir Aly, et al. "Children with autism social engagement in interaction with Nao, an imitative robot." Interaction Studies 13, no. 3 (2012): 315–47. http://dx.doi.org/10.1075/is.13.3.01tap.

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This paper presents a series of 4 single subject experiments aimed to investigate whether children with autism show more social engagement when interacting with the Nao robot, compared to a human partner in a motor imitation task. The Nao robot imitates gross arm movements of the child in real-time. Different behavioral criteria (i.e. eye gaze, gaze shifting, free initiations and prompted initiations of arm movements, and smile/laughter) were analyzed based on the video data of the interaction. The results are mixed and suggest a high variability in reactions to the Nao robot. The results are

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27

Woo, Jinseok, and Naoyuki Kubota. "Human-Robot Interaction Design Using Smart Device Based Robot Partner." International Journal of Artificial Life Research 6, no. 2 (2016): 23–43. http://dx.doi.org/10.4018/ijalr.2016070102.

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Nowadays, various robot partners have been developed to realize human-friendly interactions. In general, a robot system is composed of hardware modules, software modules, and application contents. It takes much time to design utterance contents and motion patterns as application contents simultaneously, but the design support systems mainly focus on the generation of robot motion patterns. Furthermore, a methodology is needed to easily change the specification of hardware and software according to diversified needs, and the developmental environment to design the application contents on verbal

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28

Xin, Hong Bing, and Qiang Huang. "Interaction and Coupling of Robot." Advanced Materials Research 383-390 (November 2011): 1299–303. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1299.

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Robot interaction involves in the robot sociality, on the basis of bionics, robotic technologies, psychology, network and system science etc., after the basic characteristics of the robot interaction and implementation discussed, a design for coupling to heterogeneous robot for motion control is introduced. The dynamic coupling method of heterogeneous robots has been implemented by adopting the inheritance, polymorphism and template technologies.

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29

Maijala, Minna, and Maarit Mutta. "The Teacher's Role in Robot-assisted Language Learning and its Impact on Classroom Ecology." EuroCALL Review 30, no. 2 (2024): 6–23. http://dx.doi.org/10.4995/eurocall.2023.17018.

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In recent years, social robots have emerged as a new teaching aid in foreign language (FL) classrooms. Interaction in FL classrooms usually takes place between teachers and learners or among learners. However, this constellation of interactions changes when a robot enters the classroom. The robot’s role in the classroom has been studied previously, however, in this article we examine how initial encounters between a social robot and learners occur, focusing on the teacher’s role during these encounters. Additionally, we examine how children seek help or assurance from their teacher when intera

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30

Regmi, Sambad, Devin Burns, and Yun Seong Song. "A robot for overground physical human-robot interaction experiments." PLOS ONE 17, no. 11 (2022): e0276980. http://dx.doi.org/10.1371/journal.pone.0276980.

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Many anticipated physical human-robot interaction (pHRI) applications in the near future are overground tasks such as walking assistance. For investigating the biomechanics of human movement during pHRI, this work presents Ophrie, a novel interactive robot dedicated for physical interaction tasks with a human in overground settings. Unique design requirements for pHRI were considered in implementing the one-arm mobile robot, such as the low output impedance and the ability to apply small interaction forces. The robot can measure the human arm stiffness, an important physical quantity that can

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31

Giannopulu, I., and G. Pradel. "From Child-Robot Interaction to Child-Robot-Therapist Interaction: A Case Study in Autism." Applied Bionics and Biomechanics 9, no. 2 (2012): 173–79. http://dx.doi.org/10.1155/2012/682601.

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Troubles in social communication as well as deficits in the cognitive treatment of emotions are supposed to be a fundamental part of autism. We present a case study based on multimodal interaction between a mobile robot and a child with autism in spontaneous, free game play. This case study tells us that the robot mediates the interaction between the autistic child and therapist once the robot-child interaction has been established. In addition, the child uses the robot as a mediator to express positive emotion playing with the therapist. It is thought that the three-pronged interaction i.e.,

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32

Momen, Ali, and Eva Wiese. "Noticing Extroversion Effects Attention: How Robot and Participant Personality Affect Gaze Cueing." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1557–61. http://dx.doi.org/10.1177/1541931218621352.

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Social robots with expressive gaze have positive effects on human-robot interaction. In particular, research suggests that when robots are programmed to express introverted or extroverted gaze behavior, individuals enjoy interacting more with robots that match their personality. However, how this affects social-cognitive performance during human-robot interactions has not been thoroughly examined yet. In the current paper, we examine whether the perceived match between human and robot personality positively affects the degree to which the robot’s gaze is followed (i.e., gaze cueing, as a proxy

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33

Candon, Kate. "Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29249–50. https://doi.org/10.1609/aaai.v39i28.35202.

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My work aims to enable robots to better learn from human feedback in human-robot interactions. The way in which people want to collaborate with a robot can vary person-to-person, interaction-to-interaction, or even within an interaction with a given person. Thus, robots need to be able to adapt their behavior during interactions. Robots typically learn from humans via explicit feedback, such as evaluative feedback, preferences, or demonstrations. We know that humans also provide additional information implicitly through non-verbal behavior that gives clues about their internal states during in

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34

Hsieh, Wei-Fen, Eri Sato-Shimokawara, and Toru Yamaguchi. "Investigation of Robot Expression Style in Human-Robot Interaction." Journal of Robotics and Mechatronics 32, no. 1 (2020): 224–35. http://dx.doi.org/10.20965/jrm.2020.p0224.

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In our daily conversation, we obtain considerable information from our interlocutor’s non-verbal behaviors, such as gaze and gestures. Several studies have shown that nonverbal messages are prominent factors in smoothing the process of human-robot interaction. Our previous studies have shown that not only a robot’s appearance but also its gestures, tone, and other nonverbal factors influence a person’s impression of it. The paper presented an analysis of the impressions made when human motions are implemented on a humanoid robot, and experiments were conducted to evaluate impressions made by r

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35

Losey, Dylan P., Andrea Bajcsy, Marcia K. O’Malley, and Anca D. Dragan. "Physical interaction as communication: Learning robot objectives online from human corrections." International Journal of Robotics Research 41, no. 1 (2021): 20–44. http://dx.doi.org/10.1177/02783649211050958.

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When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human–robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intent

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36

Fischer, Kerstin. "Tracking Anthropomorphizing Behavior in Human-Robot Interaction." ACM Transactions on Human-Robot Interaction 11, no. 1 (2022): 1–28. http://dx.doi.org/10.1145/3442677.

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Existing methodologies to describe anthropomorphism in human-robot interaction often rely either on specific one-time responses to robot behavior, such as keeping the robot's secret, or on post hoc measures, such as questionnaires. Currently, there is no method to describe the dynamics of people's behavior over the course of an interaction and in response to robot behavior. In this paper, I propose a method that allows the researcher to trace anthropomorphizing and non-anthropomorphizing responses to robots dynamically moment-by-moment over the course of human-robot interactions. I illustrate

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37

Blevis, Eli. "Future robot." Interactions 23, no. 1 (2015): 88. http://dx.doi.org/10.1145/2856122.

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38

Lee, Jae-Joon, Dae-Won Kim, and Bo-Yeong Kang. "Exploiting Child-Robot Aesthetic Interaction for a Social Robot." International Journal of Advanced Robotic Systems 9, no. 3 (2012): 81. http://dx.doi.org/10.5772/51191.

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39

Jeong, Jaesik, Jeehyun Yang, and Jacky Baltes. "Robot magic show as testbed for humanoid robot interaction." Entertainment Computing 40 (January 2022): 100456. http://dx.doi.org/10.1016/j.entcom.2021.100456.

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40

Ullrich, Daniel. "Robot Personality Insights. Designing Suitable Robot Personalities for Different Domains." i-com 16, no. 1 (2017): 57–67. http://dx.doi.org/10.1515/icom-2017-0003.

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AbstractWith the development of social robots that are primarily designed for interacting with humans, particular facets of interaction need to be explored. One of them is the manifestation of robot personalities, which have the potential to raise acceptance and enhance user experience if done appropriate – or ruin both if done wrong.The present paper argues for the relevance of suitable robot personalities and discusses the factors that affect suitability, in particular interaction domain and personal preferences.An experiment ($\mathrm{N}=30$) with four different interaction scenarios (goal-

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41

Thomaz, Andrea, Guy Hoffman, and Maya Cakmak. "Computational Human-Robot Interaction." Foundations and Trends in Robotics 4, no. 2-3 (2016): 104–223. http://dx.doi.org/10.1561/2300000049.

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42

Karniel, Amir, Angelika Peer, Opher Donchin, Ferdinando A. Mussa-Ivaldi, and Gerald E. Loeb. "Haptic Human-Robot Interaction." IEEE Transactions on Haptics 5, no. 3 (2012): 193–95. http://dx.doi.org/10.1109/toh.2012.47.

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43

Pook, Polly K., and Dana H. Ballard. "Deictic human/robot interaction." Robotics and Autonomous Systems 18, no. 1-2 (1996): 259–69. http://dx.doi.org/10.1016/0921-8890(95)00080-1.

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44

Young, James E., JaYoung Sung, Amy Voida, et al. "Evaluating Human-Robot Interaction." International Journal of Social Robotics 3, no. 1 (2010): 53–67. http://dx.doi.org/10.1007/s12369-010-0081-8.

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45

Daza, Marcos, Dennis Barrios-Aranibar, José Diaz-Amado, Yudith Cardinale, and João Vilasboas. "An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots." Micromachines 12, no. 2 (2021): 193. http://dx.doi.org/10.3390/mi12020193.

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–

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46

Kim, Ji Young, and Hyuk Jae Choi. "A Study on Robot UX Design for Human-Robot Interaction." Korea Institute of Design Research Society 9, no. 3 (2024): 475–85. http://dx.doi.org/10.46248/kidrs.2024.3.475.

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The purpose of this paper is to conduct an in-depth review of robot user experience (UX) design to optimize the interaction between humans and robots. As a research method, the existing literature on robot UX design was reviewed, related theories and concepts were summarized, and various types of human-robot interaction were analyzed. By analyzing various robot UX design cases in various fields such as service, healthcare, and education, and suggesting the direction of future robot UX design with User Experience Design, the importance and possibility of robot UX design in the future society ar

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47

van Maris, Anouk, Nancy Zook, Sanja Dogramadzi, Matthew Studley, Alan Winfield, and Praminda Caleb-Solly. "A New Perspective on Robot Ethics through Investigating Human–Robot Interactions with Older Adults." Applied Sciences 11, no. 21 (2021): 10136. http://dx.doi.org/10.3390/app112110136.

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This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we sho

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48

Zhao, Mengyao. "Emotion Recognition in Psychology of Human-robot Interaction." Psychomachina 1 (November 21, 2023): 1–11. http://dx.doi.org/10.59388/pm00331.

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The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotio

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Mori, Yoshikazu, Koji Ota, and Tatsuya Nakamura. "Robot Motion Algorithm Based on Interaction with Human." Journal of Robotics and Mechatronics 14, no. 5 (2002): 462–70. http://dx.doi.org/10.20965/jrm.2002.p0462.

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In this paper, we quantitatively analyze weariness and impression that a human senses for a robot when the human interacted with the robot through some movements. A red ball and a blue ball are displayed on a simulation screen. The human moves the red ball with a mouse and the computer moves the blue ball. By using these balls, the impression that the action of the robot gives to the human is examined. We analyze the relationship between robot's interactive characterisrtics and produced impressions about the robot in human-robot-interction experiments by using methods of information theory. Th

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Schadenberg, Bob R., Dennis Reidsma, Dirk K. J. Heylen, and Vanessa Evers. "“I See What You Did There”." ACM Transactions on Human-Robot Interaction 10, no. 3 (2021): 1–28. http://dx.doi.org/10.1145/3461534.

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Unpredictability in robot behaviour can cause difficulties in interacting with robots. However, for social interactions with robots, a degree of unpredictability in robot behaviour may be desirable for facilitating engagement and increasing the attribution of mental states to the robot. To generate a better conceptual understanding of predictability, we looked at two facets of predictability, namely, the ability to predict robot actions and the association of predictability as an attribute of the robot. We carried out a video human-robot interaction study where we manipulated whether participa

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