Relevant bibliographies by topics / Interaction robot-Robot

Academic literature on the topic 'Interaction robot-Robot'

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Published: 25 May 2024

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Journal articles on the topic "Interaction robot-Robot"

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Lee, Heejin. "A Human-Robot Interaction Entertainment Pet Robot." Journal of Korean Institute of Intelligent Systems 24, no. 2 (April 25, 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 (August 2008): 911–16. http://dx.doi.org/10.1109/tro.2008.926867.

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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 (January 15, 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 user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.
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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 (June 2010): 85–89. http://dx.doi.org/10.1109/mra.2010.936953.

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

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Sheridan, Thomas B. "Human–Robot Interaction." Human Factors: The Journal of the Human Factors and Ergonomics Society 58, no. 4 (April 20, 2016): 525–32. http://dx.doi.org/10.1177/0018720816644364.

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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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Jones, Keith S., and Elizabeth A. Schmidlin. "Human-Robot Interaction." Reviews of Human Factors and Ergonomics 7, no. 1 (August 25, 2011): 100–148. http://dx.doi.org/10.1177/1557234x11410388.

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Dissertations / Theses on the topic "Interaction robot-Robot"

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Akan, Batu. "Human Robot Interaction Solutions for Intuitive Industrial Robot Programming." Licentiate thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-14315.

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Over the past few decades the use of industrial robots has increased the efficiency as well as competitiveness of many companies. Despite this fact, in many cases, robot automation investments are considered to be technically challenging. In addition, for most small and medium sized enterprises (SME) this process is associated with high costs. Due to their continuously changing product lines, reprogramming costs are likely to exceed installation costs by a large margin. Furthermore, traditional programming methods for industrial robots are too complex for an inexperienced robot programmer, thus assistance from a robot programming expert is often needed.  We hypothesize that in order to make industrial robots more common within the SME sector, the robots should be reprogrammable by technicians or manufacturing engineers rather than robot programming experts. In this thesis we propose a high-level natural language framework for interacting with industrial robots through an instructional programming environment for the user.  The ultimate goal of this thesis is to bring robot programming to a stage where it is as easy as working together with a colleague.In this thesis we mainly address two issues. The first issue is to make interaction with a robot easier and more natural through a multimodal framework. The proposed language architecture makes it possible to manipulate, pick or place objects in a scene through high level commands. Interaction with simple voice commands and gestures enables the manufacturing engineer to focus on the task itself, rather than programming issues of the robot. This approach shifts the focus of industrial robot programming from the coordinate based programming paradigm, which currently dominates the field, to an object based programming scheme.The second issue addressed is a general framework for implementing multimodal interfaces. There have been numerous efforts to implement multimodal interfaces for computers and robots, but there is no general standard framework for developing them. The general framework proposed in this thesis is designed to perform natural language understanding, multimodal integration and semantic analysis with an incremental pipeline and includes a novel multimodal grammar language, which is used for multimodal presentation and semantic meaning generation.
robot colleague project
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Ali, Muhammad. "Contribution to decisional human-robot interaction: towards collaborative robot companions." Phd thesis, INSA de Toulouse, 2012. http://tel.archives-ouvertes.fr/tel-00719684.

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L'interaction homme-robot arrive dans une phase intéressante ou la relation entre un homme et un robot est envisage comme 'un partenariat plutôt que comme une simple relation maitre-esclave. Pour que cela devienne une réalité, le robot a besoin de comprendre le comportement humain. Il ne lui suffit pas de réagir de manière appropriée, il lui faut également être socialement proactif. Pour que ce comportement puis être mise en pratique le roboticien doit s'inspirer de la littérature déjà riche en sciences sociocognitives chez l'homme. Dans ce travail, nous allons identifier les éléments clés d'une telle interaction dans le contexte d'une tâche commune, avec un accent particulier sur la façon dont l'homme doit collaborer pour réaliser avec succès une action commune. Nous allons montrer l'application de ces éléments au cas un système robotique afin d'enrichir les interactions sociales homme-robot pour la prise de décision. A cet égard, une contribution a la gestion du but de haut niveau de robot et le comportement proactif est montre. La description d'un modèle décisionnel d'collaboration pour une tâche collaboratif avec l'humain est donnée. Ainsi, l'étude de l'interaction homme robot montre l'intéret de bien choisir le moment d'une action de communication lors des activités conjointes avec l'humain.
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Ali, Muhammad. "Contributions to decisional human-robot interaction : towards collaborative robot companions." Thesis, Toulouse, INSA, 2012. http://www.theses.fr/2012ISAT0003/document.

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L'interaction homme-robot arrive dans une phase intéressante ou la relation entre un homme et un robot est envisage comme 'un partenariat plutôt que comme une simple relation maitre-esclave. Pour que cela devienne une réalité, le robot a besoin de comprendre le comportement humain. Il ne lui suffit pas de réagir de manière appropriée, il lui faut également être socialement proactif. Pour que ce comportement puis être mise en pratique le roboticien doit s'inspirer de la littérature déjà riche en sciences sociocognitives chez l'homme. Dans ce travail, nous allons identifier les éléments clés d'une telle interaction dans le contexte d'une tâche commune, avec un accent particulier sur la façon dont l'homme doit collaborer pour réaliser avec succès une action commune. Nous allons montrer l'application de ces éléments au cas un système robotique afin d'enrichir les interactions sociales homme-robot pour la prise de décision. A cet égard, une contribution a la gestion du but de haut niveau de robot et le comportement proactif est montre. La description d'un modèle décisionnel d'collaboration pour une tâche collaboratif avec l'humain est donnée. Ainsi, l'étude de l'interaction homme robot montre l'intéret de bien choisir le moment d'une action de communication lors des activités conjointes avec l'humain
Human Robot Interaction is entering into the interesting phase where the relationship with a robot is envisioned more as one of companionship with the human partner than a mere master-slave relationship. For this to become a reality, the robot needs to understand human behavior and not only react appropriately but also be socially proactive. A Companion Robot will also need to collaborate with the human in his daily life and will require a reasoning mechanism to manage thecollaboration and also handle the uncertainty in the human intention to engage and collaborate. In this work, we will identify key elements of such interaction in the context of a collaborative activity, with special focus on how humans successfully collaborate to achieve a joint action. We will show application of these elements in a robotic system to enrich its social human robot interaction aspect of decision making. In this respect, we provide a contribution to managing robot high-level goals and proactive behavior and a description of a coactivity decision model for collaborative human robot task. Also, a HRI user study demonstrates the importance of timing a verbal communication in a proactive human robot joint action
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Alili, Samir. "Interaction décisionnelle Homme-Robot : planification de tâche pour un robot interactif en environnement humain." Phd thesis, Université Paul Sabatier - Toulouse III, 2011. http://tel.archives-ouvertes.fr/tel-01068811.

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Cette thèse aborde la problématique de la décision partagée homme-robot dans une perspective de résolution interactive de problème à laquelle prennent part l'homme et le robot. Le robot et l'homme poursuivent des objectifs communs et doivent déterminer ensemble les moyens de les réaliser (les capacités et les compétences de chacun étant différentes). Les questions à traiter concernent ce partage des rôles, le partage d'autorité dans l'exécution d'une tche (prise d'initiative), les connaissances à exhiber afin que l'un et l'autre puissent jouer un rôle optimal dans la résolution du problème commun. Nous avons développé un planificateur de tâche nommé HATP (Human Aware Task Planner). Ce planificateur est conçu sur la base de la planification hiérarchique qu'on a enrichie avec des règles sociales. Il permet de produire des plans qui sont socialement acceptables, c'est-à-dire des plans qui rendent lisibles les actions et les intentions du robot. Le planificateur a également la capacité de planifier pour le robot et l'humain tout en garantissant l'optimalité pour chacun d'eux. Nous nous sommes également intéressés à une approche hybride, qui mixe la planification de tâche à la planification géométrique. Cette approche permet au robot d'avoir un contrôle sur la séquence d'actions qu'il produit mais également sur la façon de la réaliser. Ce qui permet de traiter le problème de l'interaction homme-robot de manière plus fine mais également sur plusieurs niveaux.
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Alili, Samir. "Interaction décisionnelle homme-robot : planification de tâche pour un robot interactif en environnement humain." Phd thesis, Toulouse 3, 2011. http://thesesups.ups-tlse.fr/2663/.

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Cette thèse aborde la problématique de la décision partagée homme-robot dans une perspective de résolution interactive de problème à laquelle prennent part l'homme et le robot. Le robot et l'homme poursuivent des objectifs communs et doivent déterminer ensemble les moyens de les réaliser (les capacités et les compétences de chacun étant différentes). Les questions à traiter concernent ce partage des rôles, le partage d'autorité dans l'exécution d'une tâche (prise d'initiative), les connaissances à exhiber afin que l'un et l'autre puissent jouer un rôle optimal dans la résolution du problème commun. Nous avons développé un planificateur de tâche nommé HATP (Human Aware Task Planner). Ce planificateur est conçu sur la base de la planification hiérarchique qu'on a enrichie avec des règles sociales. Il permet de produire des plans qui sont socialement acceptables, c'est-à-dire des plans qui rendent lisibles les actions et les intentions du robot. Le planificateur a également la capacité de planifier pour le robot et l'humain tout en garantissant l'optimalité pour chacun d'eux. Nous nous sommes également intéressés à une approche hybride, qui mixe la planification de tâche à la planification géométrique. Cette approche permet au robot d'avoir un contrôle sur la séquence d'actions qu'il produit mais également sur la façon de la réaliser. Ce qui permet de traiter le problème de l'interaction homme-robot de manière plus fine mais également sur plusieurs niveaux
This thesis addresses the problem of the shared decision between human and robot in the perspective of interactive problem solving that involved human and robot. The robot and human share common goals and must work together to identify how to realize (the capacity and the competence of each one are different). Issues to be addressed concerning this division of roles, sharing of authority in the execution of a task (taking initiative), to exhibit the knowledge such that both can play an optimal role in the resolution of common problems. We developed a task planner named HATP (Human Aware Task Planner). This planner is based on hierarchical task planning that is enriched with social rules. It can produce plans that are socially acceptable that means plans that make legible the actions and intentions of the robot. The planner also has the ability to plan for the robot and humans while ensuring optimality for each. We are also interested in a hybrid approach that mixes between task planning and geometrical planning. This approach allows the robot to have control over the sequence of actions that it produces, but also on how to achieve it. Thereby treat the human-robot interaction problem more cleverly, but also on several levels
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Kruse, Thibault. "Planning for human robot interaction." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30059/document.

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Les avancées récentes en robotique inspirent des visions de robots domestiques et de service rendant nos vies plus faciles et plus confortables. De tels robots pourront exécuter différentes tâches de manipulation d'objets nécessaires pour des travaux de ménage, de façon autonome ou en coopération avec des humains. Dans ce rôle de compagnon humain, le robot doit répondre à de nombreuses exigences additionnelles comparées aux domaines bien établis de la robotique industrielle. Le but de la planification pour les robots est de parvenir à élaborer un comportement visant à satisfaire un but et qui obtient des résultats désirés et dans de bonnes conditions d'efficacité. Mais dans l'interaction homme-robot (HRI), le comportement robot ne peut pas simplement être jugé en termes de résultats corrects, mais il doit être agréable aux acteurs humains. Cela signifie que le comportement du robot doit obéir à des critères de qualité supplémentaire. Il doit être sûr, confortable pour l'homme, et être intuitivement compris. Il existe des pratiques pour assurer la sécurité et offrir un confort en gardant des distances suffisantes entre le robot et des personnes à proximité. Toutefois fournir un comportement qui est intuitivement compris reste un défi. Ce défi augmente considérablement dans les situations d'interaction homme-robot dynamique, où les actions de la personne sont imprévisibles, le robot devant adapter en permanence ses plans aux changements. Cette thèse propose une approche nouvelle et des méthodes pour améliorer la lisibilité du comportement du robot dans des situations dynamiques. Cette approche ne considère pas seulement la qualité d'un seul plan, mais le comportement du robot qui est parfois le résultat de replanifications répétées au cours d'une interaction. Pour ce qui concerne les tâches de navigation, cette thèse présente des fonctions de coûts directionnels qui évitent les problèmes dans des situations de conflit. Pour la planification d'action en général, cette thèse propose une approche de replanification locale des actions de transport basé sur les coûts de navigation, pour élaborer un comportement opportuniste adaptatif. Les deux approches, complémentaires, facilitent la compréhension, par les acteurs et observateurs humains, des intentions du robot et permettent de réduire leur confusion
The recent advances in robotics inspire visions of household and service robots making our lives easier and more comfortable. Such robots will be able to perform several object manipulation tasks required for household chores, autonomously or in cooperation with humans. In that role of human companion, the robot has to satisfy many additional requirements compared to well established fields of industrial robotics. The purpose of planning for robots is to achieve robot behavior that is goal-directed and establishes correct results. But in human-robot-interaction, robot behavior cannot merely be judged in terms of correct results, but must be agree-able to human stakeholders. This means that the robot behavior must suffice additional quality criteria. It must be safe, comfortable to human, and intuitively be understood. There are established practices to ensure safety and provide comfort by keeping sufficient distances between the robot and nearby persons. However providing behavior that is intuitively understood remains a challenge. This challenge greatly increases in cases of dynamic human-robot interactions, where the actions of the human in the future are unpredictable, and the robot needs to constantly adapt its plans to changes. This thesis provides novel approaches to improve the legibility of robot behavior in such dynamic situations. Key to that approach is not to merely consider the quality of a single plan, but the behavior of the robot as a result of replanning multiple times during an interaction. For navigation planning, this thesis introduces directional cost functions that avoid problems in conflict situations. For action planning, this thesis provides the approach of local replanning of transport actions based on navigational costs, to provide opportunistic behavior. Both measures help human observers understand the robot's beliefs and intentions during interactions and reduce confusion
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Bodiroža, Saša. "Gestures in human-robot interaction." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17705.

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Gesten sind ein Kommunikationsweg, der einem Betrachter Informationen oder Absichten übermittelt. Daher können sie effektiv in der Mensch-Roboter-Interaktion, oder in der Mensch-Maschine-Interaktion allgemein, verwendet werden. Sie stellen eine Möglichkeit für einen Roboter oder eine Maschine dar, um eine Bedeutung abzuleiten. Um Gesten intuitiv benutzen zukönnen und Gesten, die von Robotern ausgeführt werden, zu verstehen, ist es notwendig, Zuordnungen zwischen Gesten und den damit verbundenen Bedeutungen zu definieren -- ein Gestenvokabular. Ein Menschgestenvokabular definiert welche Gesten ein Personenkreis intuitiv verwendet, um Informationen zu übermitteln. Ein Robotergestenvokabular zeigt welche Robotergesten zu welcher Bedeutung passen. Ihre effektive und intuitive Benutzung hängt von Gestenerkennung ab, das heißt von der Klassifizierung der Körperbewegung in diskrete Gestenklassen durch die Verwendung von Mustererkennung und maschinellem Lernen. Die vorliegende Dissertation befasst sich mit beiden Forschungsbereichen. Als eine Voraussetzung für die intuitive Mensch-Roboter-Interaktion wird zunächst ein Aufmerksamkeitsmodell für humanoide Roboter entwickelt. Danach wird ein Verfahren für die Festlegung von Gestenvokabulare vorgelegt, das auf Beobachtungen von Benutzern und Umfragen beruht. Anschliessend werden experimentelle Ergebnisse vorgestellt. Eine Methode zur Verfeinerung der Robotergesten wird entwickelt, die auf interaktiven genetischen Algorithmen basiert. Ein robuster und performanter Gestenerkennungsalgorithmus wird entwickelt, der auf Dynamic Time Warping basiert, und sich durch die Verwendung von One-Shot-Learning auszeichnet, das heißt durch die Verwendung einer geringen Anzahl von Trainingsgesten. Der Algorithmus kann in realen Szenarien verwendet werden, womit er den Einfluss von Umweltbedingungen und Gesteneigenschaften, senkt. Schließlich wird eine Methode für das Lernen der Beziehungen zwischen Selbstbewegung und Zeigegesten vorgestellt.
Gestures consist of movements of body parts and are a mean of communication that conveys information or intentions to an observer. Therefore, they can be effectively used in human-robot interaction, or in general in human-machine interaction, as a way for a robot or a machine to infer a meaning. In order for people to intuitively use gestures and understand robot gestures, it is necessary to define mappings between gestures and their associated meanings -- a gesture vocabulary. Human gesture vocabulary defines which gestures a group of people would intuitively use to convey information, while robot gesture vocabulary displays which robot gestures are deemed as fitting for a particular meaning. Effective use of vocabularies depends on techniques for gesture recognition, which considers classification of body motion into discrete gesture classes, relying on pattern recognition and machine learning. This thesis addresses both research areas, presenting development of gesture vocabularies as well as gesture recognition techniques, focusing on hand and arm gestures. Attentional models for humanoid robots were developed as a prerequisite for human-robot interaction and a precursor to gesture recognition. A method for defining gesture vocabularies for humans and robots, based on user observations and surveys, is explained and experimental results are presented. As a result of the robot gesture vocabulary experiment, an evolutionary-based approach for refinement of robot gestures is introduced, based on interactive genetic algorithms. A robust and well-performing gesture recognition algorithm based on dynamic time warping has been developed. Most importantly, it employs one-shot learning, meaning that it can be trained using a low number of training samples and employed in real-life scenarios, lowering the effect of environmental constraints and gesture features. Finally, an approach for learning a relation between self-motion and pointing gestures is presented.
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Ahmed, Muhammad Rehan. "Compliance Control of Robot Manipulator for Safe Physical Human Robot Interaction." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-13986.

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Inspiration from biological systems suggests that robots should demonstrate same level of capabilities that are embedded in biological systems in performing safe and successful interaction with the humans. The major challenge in physical human robot interaction tasks in anthropic environment is the safe sharing of robot work space such that robot will not cause harm or injury to the human under any operating condition. Embedding human like adaptable compliance characteristics into robot manipulators can provide safe physical human robot interaction in constrained motion tasks. In robotics, this property can be achieved by using active, passive and semi active compliant actuation devices. Traditional methods of active and passive compliance lead to complex control systems and complex mechanical design. In this thesis we present compliant robot manipulator system with semi active compliant device having magneto rheological fluid based actuation mechanism. Human like adaptable compliance is achieved by controlling the properties of the magneto rheological fluid inside joint actuator. This method offers high operational accuracy, intrinsic safety and high absorption to impacts. Safety is assured by mechanism design rather than by conventional approach based on advance control. Control schemes for implementing adaptable compliance are implemented in parallel with the robot motion control that brings much simple interaction control strategy compared to other methods. Here we address two main issues: human robot collision safety and robot motion performance.We present existing human robot collision safety standards and evaluate the proposed actuation mechanism on the basis of static and dynamic collision tests. Static collision safety analysis is based on Yamada’s safety criterion and the adaptable compliance control scheme keeps the robot in the safe region of operation. For the dynamic collision safety analysis, Yamada’s impact force criterion and head injury criterion are employed. Experimental results validate the effectiveness of our solution. In addition, the results with head injury criterion showed the need to investigate human bio-mechanics in more details in order to acquire adequate knowledge for estimating the injury severity index for robots interacting with humans. We analyzed the robot motion performance in several physical human robot interaction tasks. Three interaction scenarios are studied to simulate human robot physical contact in direct and inadvertent contact situations. Respective control disciplines for the joint actuators are designed and implemented with much simplified adaptable compliance control scheme. The series of experimental tests in direct and inadvertent contact situations validate our solution of implementing human like adaptable compliance during robot motion and prove the safe interaction with humans in anthropic domains.
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Toris, Russell C. "Bringing Human-Robot Interaction Studies Online via the Robot Management System." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/1058.

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"Human-Robot Interaction (HRI) is a rapidly expanding field of study that focuses on allowing non-roboticist users to naturally and effectively interact with robots. The importance of conducting extensive user studies has become a fundamental component of HRI research; however, due to the nature of robotics research, such studies often become expensive, time consuming, and limited to constrained demographics. This work presents the Robot Management System, a novel framework for bringing robotic experiments to the web. A detailed description of the open source system, an outline of new security measures, and a use case study of the RMS as a means of conducting user studies is presented. Using a series of navigation and manipulation tasks with a PR2 robot, three user study conditions are compared: users that are co-present with the robot, users that are recruited to the university lab but control the robot from a different room, and remote web-based users. The findings show little statistical differences between usability patterns across these groups, further supporting the use of web-based crowdsourcing techniques for certain types of HRI evaluations."
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Nitz, Pettersson Hannes, and Samuel Vikström. "VISION-BASED ROBOT CONTROLLER FOR HUMAN-ROBOT INTERACTION USING PREDICTIVE ALGORITHMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54609.

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The demand for robots to work in environments together with humans is growing. This calls for new requirements on robots systems, such as the need to be perceived as responsive and accurate in human interactions. This thesis explores the possibility of using AI methods to predict the movement of a human and evaluating if that information can assist a robot with human interactions. The AI methods that were used is a Long Short Term Memory(LSTM) network and an artificial neural network(ANN). Both networks were trained on data from a motion capture dataset and on four different prediction times: 1/2, 1/4, 1/8 and a 1/16 second. The evaluation was performed directly on the dataset to determine the prediction error. The neural networks were also evaluated on a robotic arm in a simulated environment, to show if the prediction methods would be suitable for a real-life system. Both methods show promising results when comparing the prediction error. From the simulated system, it could be concluded that with the LSTM prediction the robotic arm would generally precede the actual position. The results indicate that the methods described in this thesis report could be used as a stepping stone for a human-robot interactive system.
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Books on the topic "Interaction robot-Robot"

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Jost, Céline, Brigitte Le Pévédic, Tony Belpaeme, Cindy Bethel, Dimitrios Chrysostomou, Nigel Crook, Marine Grandgeorge, and Nicole Mirnig, eds. Human-Robot Interaction. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42307-0.

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Mansour, Rahimi, and Karwowski Waldemar 1953-, eds. Human-robot interaction. London: Taylor & Francis, 1992.

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Prassler, Erwin, Gisbert Lawitzky, Andreas Stopp, Gerhard Grunwald, Martin Hägele, Rüdiger Dillmann, and Ioannis Iossifidis, eds. Advances in Human-Robot Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b97960.

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Goodrich, Michael A. Human-robot interaction: A survey. Hanover: Now Publishers, 2007.

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Xing, Bo, and Tshilidzi Marwala. Smart Maintenance for Human–Robot Interaction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67480-3.

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Ayanoğlu, Hande, and Emília Duarte, eds. Emotional Design in Human-Robot Interaction. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-96722-6.

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Dautenhahn, Kerstin, and Joe Saunders, eds. New Frontiers in Human–Robot Interaction. Amsterdam: John Benjamins Publishing Company, 2011. http://dx.doi.org/10.1075/ais.2.

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Wang, Xiangyu, ed. Mixed Reality and Human-Robot Interaction. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0582-1.

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New frontiers in human-robot interaction. Philadelphia: John Benjamins Pub., 2011.

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Wang, Xiangyu. Mixed Reality and Human-Robot Interaction. Dordrecht: Springer Science+Business Media B.V., 2011.

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Book chapters on the topic "Interaction robot-Robot"

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Nehmzow, Ulrich. "Computer Modelling of Robot-Environment Interaction." In Robot Behaviour, 1–28. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-397-2_7.

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Duan, Feng, Wenyu Li, and Ying Tan. "Implementation of Robot Voice Interaction Functionality: PocketSphinx." In Intelligent Robot, 239–52. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8253-8_10.

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Duan, Feng, Wenyu Li, and Ying Tan. "Robot Voice Interaction Functions of Basic Theory." In Intelligent Robot, 223–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8253-8_9.

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Ir, André Pirlet. "The Role of Standardization in Technical Regulations." In Human–Robot Interaction, 1–8. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-1.

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Takács, Árpád, Imre J. Rudas, and Tamás Haidegger. "The Other End of Human–Robot Interaction." In Human–Robot Interaction, 137–70. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-10.

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Lőrincz, Márton. "Passive Bilateral Teleoperation with Safety Considerations." In Human–Robot Interaction, 171–86. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-11.

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Fiorini, Paolo, and Riccardo Muradore. "Human–Robot Interfaces in Autonomous Surgical Robots." In Human–Robot Interaction, 187–99. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-12.

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Fosch-Villaronga, Eduard, and Angelo Jr Golia. "The Intricate Relationships Between Private Standards and Public Policymaking in Personal Care Robots: Who Cares More?" In Human–Robot Interaction, 9–18. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-2.

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Fiorini, Sandro Rama, Abdelghani Chibani, Tamás Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, et al. "Standard Ontologies and HRI." In Human–Robot Interaction, 19–47. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-3.

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Park, Hong Seong, and Gurvinder Singh Virk. "Robot Modularity for Service Robots." In Human–Robot Interaction, 49–70. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315213781-4.

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Conference papers on the topic "Interaction robot-Robot"

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Billings, Deborah R., Kristin E. Schaefer, Jessie Y. C. Chen, and Peter A. Hancock. "Human-robot interaction." In the seventh annual ACM/IEEE international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2157689.2157709.

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"Human robot interaction." In 2016 9th International Conference on Human System Interactions (HSI). IEEE, 2016. http://dx.doi.org/10.1109/hsi.2016.7529627.

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St-Onge, David, Nicolas Reeves, and Nataliya Petkova. "Robot-Human Interaction." In HRI '17: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3029798.3034785.

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Björling, Elin A., Emma Rose, and Rachel Ren. "Teen-Robot Interaction." In HRI '18: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3173386.3177068.

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Shahid, Suleman, Emiel Krahmer, and Marc Swerts. "Child-robot interaction." In the 2011 annual conference extended abstracts. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1979742.1979781.

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Reynolds-Cuéllar, Pedro, and Andrés F. Salazar-Gómez. "Nature-Robot Interaction." In HRI '23: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3568294.3580034.

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Jeong-Yean Yang and Dong-Soo Kwon. "The effect of multiple robot interaction on human-robot interaction." In 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2012. http://dx.doi.org/10.1109/urai.2012.6462923.

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Wang, Heng, Xiuzhi Li, and Xiangyin Zhang. "Multimodal Human-robot Interaction on Service Robot." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9391068.

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Ayub, Ali, Marcus Scheunemann, Christoforos Mavrogiannis, Jimin Rhim, Kerstin Dautenhahn, Chrystopher L. Nehaniv, Verena V. Hafner, and Daniel Polani. "Robot Curiosity in Human-Robot Interaction (RCHRI)." In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2022. http://dx.doi.org/10.1109/hri53351.2022.9889478.

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Laplaza, Javier, Nicolas Rodriguez, J. E. Dominguez-Vidal, Fernando Herrero, Sergi Hernandez, Alejandro Lopez, Alberto Sanfeliu, and Anais Garrell. "IVO Robot: A New Social Robot for Human-Robot Collaboration." In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2022. http://dx.doi.org/10.1109/hri53351.2022.9889458.

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Reports on the topic "Interaction robot-Robot"

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Arkin, Ronald C., and Lilia Moshkina. Affect in Human-Robot Interaction. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada593747.

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Sofge, D., Dennis Perzanowski, M. Skubic, N. Cassimatis, J. G. Trafton, D. Brock, Magda Bugajska, William Adams, and Alan C. Schultz. Achieving Collaborative Interaction with a Humanoid Robot. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada434972.

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Martinson, E., and W. Lawson. Learning Speaker Recognition Models through Human-Robot Interaction. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada550036.

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Manring, Levi H., John Monroe Pederson, and Dillon Gabriel Potts. Improving Human-Robot Interaction and Control Through Augmented Reality. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1467198.

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Jiang, Shu, and Ronald C. Arkin. Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada620347.

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Scholtz, Jean, Jeff Young, Holly A. Yanco, and Jill L. Drury. Evaluation of Human-Robot Interaction Awareness in Search and Rescue. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456128.

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Bagchi, Shelly, Murat Aksu, Megan Zimmerman, Jeremy A. Marvel, Brian Antonishek, Heni Ben Amor, Terry Fong, Ross Mead, and Yue Wang. Workshop Report: Test Methods and Metrics for Effective HRI in Collaborative Human-Robot Teams, ACM/IEEE Human-Robot Interaction Conference, 2019. National Institute of Standards and Technology, December 2020. http://dx.doi.org/10.6028/nist.ir.8339.

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Bagchi, Shelly, Jeremy A. Marvel, Megan Zimmerman, Murat Aksu, Brian Antonishek, Heni Ben Amor, Terry Fong, Ross Mead, and Yue Wang. Workshop Report: Test Methods and Metrics for Effective HRI in Real-World Human-Robot Teams, ACM/IEEE Human-Robot Interaction Conference, 2020 (Virtual). National Institute of Standards and Technology, January 2021. http://dx.doi.org/10.6028/nist.ir.8345.

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Schaefer, Kristin E., Deborah R. Billings, James L. Szalma, Jeffrey K. Adams, Tracy L. Sanders, Jessie Y. Chen, and Peter A. Hancock. A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Human-Robot Interaction. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada607926.

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Bagchi, Shelly, Jeremy A. Marvel, Megan Zimmerman, Murat Aksu, Brian Antonishek, Xiang Li, Heni Ben Amor, Terry Fong, Ross Mead, and Yue Wang. Workshop Report: Novel and Emerging Test Methods and Metrics for Effective HRI, ACM/IEEE Conference on Human-Robot Interaction, 2021. National Institute of Standards and Technology, February 2022. http://dx.doi.org/10.6028/nist.ir.8417.

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