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Articoli di riviste sul tema "Human-robot interaction"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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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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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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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4

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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5

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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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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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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8

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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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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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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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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Abstract (sommario):

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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12

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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13

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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14

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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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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Tyler, Neil. "Human Robot Interactions." New Electronics 51, no. 22 (2019): 12–14. http://dx.doi.org/10.12968/s0047-9624(22)61505-0.

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KAMBAROV, Ikrom, Matthias BROSSOG, Jorg FRANKE, David KUNZ, and Jamshid INOYATKHODJAEV. "From Human to Robot Interaction towards Human to Robot Communication in Assembly Systems." Eurasia Proceedings of Science Technology Engineering and Mathematics 23 (October 16, 2023): 241–52. http://dx.doi.org/10.55549/epstem.1365802.

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Abstract (sommario):

The interaction between humans and robots has been a rapidly developing technology and a frequently discussed research topic in the last decade because current robots ensure the physical safety of humans during close proximity assembly operations. This interaction promises capability flexibility due to human dexterity skills and capacity flexibility due to robot accuracy. Nevertheless, in these interactions, the humans are marginally outside of the system, while the robots are seen as a crucial component of the assembly activities, which causes the systems to lack flexibility and efficiency. T

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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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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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20

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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SUWANNATHAT, Thatsaphan, Jun-ichi IMAI, and Masahide KANEKO. "1P1-K06 Audio-Visual Speaker Detection in Human-Robot Interaction." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2007 (2007): _1P1—K06_1—_1P1—K06_4. http://dx.doi.org/10.1299/jsmermd.2007._1p1-k06_1.

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22

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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23

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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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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25

Goodrich, Michael A., and Alan C. Schultz. "Human-Robot Interaction: A Survey." Foundations and Trends® in Human-Computer Interaction 1, no. 3 (2007): 203–75. http://dx.doi.org/10.1561/1100000005.

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26

Pieskä, Sakari, Jari Kaarela, and Ossi Saukko. "Towards easier human-robot interaction." Intelligent Decision Technologies 9, no. 1 (2014): 41–53. http://dx.doi.org/10.3233/idt-140204.

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27

Archer, Susan, and Patricia L. McDermott. "Advances in Human-Robot Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 3 (2005): 386. http://dx.doi.org/10.1177/154193120504900336.

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28

Agrawal, Pramila, Changchun Liu, and Nilanjan Sarkar. "Interaction between human and robot." Interaction Studies 9, no. 2 (2008): 230–57. http://dx.doi.org/10.1075/is.9.2.05agr.

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This paper presents a human–robot interaction framework where a robot can infer implicit affective cues of a human and respond to them appropriately. Affective cues are inferred by the robot in real-time from physiological signals. A robot-based basketball game is designed where a robotic “coach” monitors the human participant’s anxiety to dynamically reconfigure game parameters to allow skill improvement while maintaining desired anxiety levels. The results of the above-mentioned anxiety-based sessions are compared with performance-based sessions where in the latter sessions, the game is adap

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29

Scalzone, Franco, and Guglielmo Tamburrini. "Human-robot interaction and psychoanalysis." AI & SOCIETY 28, no. 3 (2012): 297–307. http://dx.doi.org/10.1007/s00146-012-0413-3.

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30

Berns, Karsten, and Zuhair Zafar. "Emotion based human-robot interaction." MATEC Web of Conferences 161 (2018): 01001. http://dx.doi.org/10.1051/matecconf/201816101001.

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Human-machine interaction is a major challenge in the development of complex humanoid robots. In addition to verbal communication the use of non-verbal cues such as hand, arm and body gestures or mimics can improve the understanding of the intention of the robot. On the other hand, by perceiving such mechanisms of a human in a typical interaction scenario the humanoid robot can adapt its interaction skills in a better way. In this work, the perception system of two social robots, ROMAN and ROBIN of the RRLAB of the TU Kaiserslautern, is presented in the range of human-robot interaction.

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31

Bradshaw, Melissa. "FREEpHRI Targets Human-Robot Interaction." Engineer 302, no. 7934 (2022): 9. http://dx.doi.org/10.12968/s0013-7758(22)90353-8.

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32

Bonarini, Andrea. "Communication in Human-Robot Interaction." Current Robotics Reports 1, no. 4 (2020): 279–85. http://dx.doi.org/10.1007/s43154-020-00026-1.

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Abstract Purpose of Review To present the multi-faceted aspects of communication between robot and humans (HRI), putting in evidence that it is not limited to language-based interaction, but it includes all aspects that are relevant in communication among physical beings, exploiting all the available sensor channels. Recent Findings For specific purposes, machine learning algorithms could be exploited when data sets and appropriate algorithms are available. Summary Together with linguistic aspects, physical aspects play an important role in HRI and make the difference with respect to the more

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Marti, Patrizia, Leonardo Giusti, Alessandro Pollini, and Alessia Rullo. "Expressiveness in Human-Robot Interaction." Interaction Design and Architecture(s), no. 5_6 (March 20, 2009): 93–98. http://dx.doi.org/10.55612/s-5002-005_6-015.

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This article presents the design of Iromec, a modular robot companion tailored towards engaging in social exchanges with children with different disabilities with the aim to empower them to discover a wide rage of play styles from solitary to social and cooperative play. In particular this paper focuses on expressiveness as a fundamental feature of the robot for engaging in meaningful interaction with different typologies of disable children – Autistic children, Moderate Mentally Retarded children and Severe Motor Impaired children. Modularity and configurability of expressive traits contribut

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34

Kim, Rae Yule. "Anthropomorphism and Human-Robot Interaction." Communications of the ACM 67, no. 2 (2024): 80–85. http://dx.doi.org/10.1145/3624716.

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35

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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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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37

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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Abstract (sommario):

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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38

Avelino, João, Tiago Paulino, Carlos Cardoso, Ricardo Nunes, Plinio Moreno, and Alexandre Bernardino. "Towards natural handshakes for social robots: human-aware hand grasps using tactile sensors." Paladyn, Journal of Behavioral Robotics 9, no. 1 (2018): 221–34. http://dx.doi.org/10.1515/pjbr-2018-0017.

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Abstract Handshaking is a fundamental part of human physical interaction that is transversal to various cultural backgrounds. It is also a very challenging task in the field of Physical Human-Robot Interaction (pHRI), requiring compliant force control in order to plan the arm’s motion and for a confident, but at the same time pleasant grasp of the human user’s hand. In this paper,we focus on the study of the hand grip strength for comfortable handshakes and perform three sets of physical interaction experiments between twenty human subjects in the first experiment, thirty-five human subjects i

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39

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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40

Filippini, Chiara, David Perpetuini, Daniela Cardone, and Arcangelo Merla. "Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression." Sensors 21, no. 19 (2021): 6438. http://dx.doi.org/10.3390/s21196438.

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An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with

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41

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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42

Yosihda, Eiichi, and Ko Ayusawa. "Physical Human-Robot Interaction and Human Model." Journal of the Robotics Society of Japan 42, no. 10 (2024): 953–58. https://doi.org/10.7210/jrsj.42.953.

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Yang, Shangshang, Xiao Gao, Zhao Feng, and Xiaohui Xiao. "Learning Pose Dynamical System for Contact Tasks under Human Interaction." Actuators 12, no. 4 (2023): 179. http://dx.doi.org/10.3390/act12040179.

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Robots are expected to execute various operation tasks like a human by learning human working skills, especially for complex contact tasks. Increasing demands for human–robot interaction during task execution makes robot motion planning and control a considerable challenge, not only to reproduce demonstration motion and force in the contact space but also to resume working after interacting with a human without re-planning motion. In this article, we propose a novel framework based on a time-invariant dynamical system (DS), taking into account both human skills transfer and human–robot interac

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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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Nakauchi, Yasushi. "Special Issue on Human Robot Interaction." Journal of Robotics and Mechatronics 14, no. 5 (2002): 431. http://dx.doi.org/10.20965/jrm.2002.p0431.

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Recent advances in robotics are disseminating robots into the social living environment as humanoids, pets, and caregivers. Novel human-robot interaction techniques and interfaces must be developed, however, to ensure that such robots interact as expected in daily life and work. Unlike conventional personal computers, such robots may assume a variety of configurations, such as industrial, wheel-based, ambulatory, remotely operated, autonomous, and wearable. They may also implement different communications modalities, including voice, video, haptics, and gestures. All of these aspects require t

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Priyanayana, S., B. Jayasekara, and R. Gopura. "Adapting concept of human-human multimodal interaction in human-robot applications." Bolgoda Plains 2, no. 2 (2022): 18–20. http://dx.doi.org/10.31705/bprm.v2(2).2022.4.

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Human communication is multimodal in nature. In a normal environment, people use to interact with other humans and with the environment using more than one modality or medium of communication. They speak, use gestures and look at things to interact with nature and other humans. By listening to the different voice tones, looking at face gazes, and arm movements people understand communication cues. A discussion with two people will be in vocal communication, hand gestures, head gestures, and facial cues, etc. [1]. If textbook definition is considered synergistic use of these interaction methods

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Rauchbauer, Birgit, Bruno Nazarian, Morgane Bourhis, Magalie Ochs, Laurent Prévot, and Thierry Chaminade. "Brain activity during reciprocal social interaction investigated using conversational robots as control condition." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1771 (2019): 20180033. http://dx.doi.org/10.1098/rstb.2018.0033.

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We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human–human interaction, HHI) to an interaction with a conversational robot (human–robot interaction, HRI). The social interaction consists of 1 min blocks of live bidirectional discussion between the scanned participant and the human or robot agent. A final sample of 21 participants is included in the corpus comprising physiological (blood oxygen level-dependent, respiration and peripheral blood flow) and behavioural (recorded speech from all inte

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Oliveira, Raquel, Patrícia Arriaga, and Ana Paiva. "Human-Robot Interaction in Groups: Methodological and Research Practices." Multimodal Technologies and Interaction 5, no. 10 (2021): 59. http://dx.doi.org/10.3390/mti5100059.

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Understanding the behavioral dynamics that underline human-robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human-robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in-the-wild. This is a problem because it hinders the development of general models of human-robot interaction, and makes the comprehension of the inner workings of the rela

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Arun Kumar Soumya, Akash. "Natural Language Processing in Robotics: Leveraging Python for Human-Robot Interaction." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 100–102. http://dx.doi.org/10.21275/sr241021044213.

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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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