Pub Date : 2013-06-18DOI: 10.5898/JHRI.2.2.Kolling
A. Kolling, K. Sycara, S. Nunnally, M. Lewis
In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.
{"title":"Human-swarm interaction","authors":"A. Kolling, K. Sycara, S. Nunnally, M. Lewis","doi":"10.5898/JHRI.2.2.Kolling","DOIUrl":"https://doi.org/10.5898/JHRI.2.2.Kolling","url":null,"abstract":"In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71218108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.
{"title":"Controlling social dynamics with a parametrized model of floor regulation","authors":"Crystal Chao, A. Thomaz","doi":"10.5898/JHRI.2.1.Chao","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Chao","url":null,"abstract":"Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Chao","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-02-27DOI: 10.5898/JHRI.2.1.Trafton
G. Trafton, Laura M. Hiatt, Anthony M. Harrison, Frank Tanborello, S. Khemlani, A. Schultz
We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous empirically validated examples of ACT-R/E models.
我们提出了一种人机交互的认知架构——ACT-R/E (Adaptive Character of Thought-Rational / Embodied)。我们使用ACT-R/E的原因有两个。首先，ACT-R/E使研究人员能够建立良好的人的具体化模型，以了解人们如何以及为什么这样思考。然后，我们利用对人的了解来预测一个人在不同情况下会做什么;例如，一个人可能会忘记一些东西，可能需要被提醒，或者一个人不能看到机器人看到的一切。我们还讨论了如何评估认知架构的方法，并展示了许多经验验证的ACT-R/E模型示例。
{"title":"ACT-R/E","authors":"G. Trafton, Laura M. Hiatt, Anthony M. Harrison, Frank Tanborello, S. Khemlani, A. Schultz","doi":"10.5898/JHRI.2.1.Trafton","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Trafton","url":null,"abstract":"We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous empirically validated examples of ACT-R/E models.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Trafton","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Wrede, C. Emmerich, Ricarda Grünberg, Arne Nordmann, Agnes Swadzba, Jochen J. Steil
The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot's movement generation. The explicit modeling of such criteria usually require experts to adapt the robot's movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.
{"title":"A user study on kinesthetic teaching of redundant robots in task and configuration space","authors":"S. Wrede, C. Emmerich, Ricarda Grünberg, Arne Nordmann, Agnes Swadzba, Jochen J. Steil","doi":"10.5898/JHRI.2.1.Wrede","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Wrede","url":null,"abstract":"The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot's movement generation. The explicit modeling of such criteria usually require experts to adapt the robot's movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Wrede","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-02-27DOI: 10.5898/JHRI.2.1.Strabala
K. Strabala, Min Kyung Lee, A. Dragan, J. Forlizzi, S. Srinivasa, M. Cakmak, Vincenzo Micelli
A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure. To that end, we first study how people hand over objects to each other in order to understand their coordination process and the signals and cues that they use and observe with their partners. Based on these studies, we propose a coordination structure for human-robot handovers that considers the physical and social-cognitive aspects of the interaction separately. This handover structure describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers: to agree that the handover will happen (and with what object), to establish the timing of the handover, and to decide the configuration at which the handover will occur. We experimentally evaluate human-robot handover behaviors that exploit this structure and offer design implications for seamless human-robot handover interactions.
{"title":"Toward seamless human-robot handovers","authors":"K. Strabala, Min Kyung Lee, A. Dragan, J. Forlizzi, S. Srinivasa, M. Cakmak, Vincenzo Micelli","doi":"10.5898/JHRI.2.1.Strabala","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Strabala","url":null,"abstract":"A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure. To that end, we first study how people hand over objects to each other in order to understand their coordination process and the signals and cues that they use and observe with their partners. Based on these studies, we propose a coordination structure for human-robot handovers that considers the physical and social-cognitive aspects of the interaction separately. This handover structure describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers: to agree that the handover will happen (and with what object), to establish the timing of the handover, and to decide the configuration at which the handover will occur. We experimentally evaluate human-robot handover behaviors that exploit this structure and offer design implications for seamless human-robot handover interactions.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Strabala","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-02-27DOI: 10.5898/JHRI.2.1.Breazeal
C. Breazeal, N. DePalma, Jeff Orkin, S. Chernova, Malte F. Jung
Supporting a wide variety of interaction styles across a diverse set of people is a significant challenge in human-robot interaction (HRI). In this work, we explore a data-driven approach that relies on crowdsourcing as a rich source of interactions that cover a wide repertoire of human behavior. We first develop an online game that requires two players to collaborate to solve a task. One player takes the role of a robot avatar and the other a human avatar, each with a different set of capabilities that must be coordinated to overcome challenges and complete the task. Leveraging the interaction data recorded in the online game, we present a novel technique for data-driven behavior generation using case-based planning for a real robot. We compare the resulting autonomous robot behavior against a Wizard of Oz base case condition in a real-world reproduction of the online game that was conducted at the Boston Museum of Science. Results of a post-study survey of participants indicate that the autonomous robot behavior matched the performance of the human-operated robot in several important measures. We examined video recordings of the real-world game to draw additional insights as to how the novice participants attempted to interact with the robot in a loosely structured collaborative task. We discovered that many of the collaborative interactions were generated in the moment and were driven by interpersonal dynamics, not necessarily by the task design. We explored using bids analysis as a meaningful construct to tap into affective qualities of HRI. An important lesson from this work is that in loosely structured collaborative tasks, robots need to be skillful in handling these in-the-moment interpersonal dynamics, as these dynamics have an important impact on the affective quality of the interaction for people. How such interactions dovetail with more task-oriented policies is an important area for future work, as we anticipate such interactions becoming commonplace in situations where personal robots perform loosely structured tasks in interaction with people in human living spaces.
{"title":"Crowdsourcing human-robot interaction","authors":"C. Breazeal, N. DePalma, Jeff Orkin, S. Chernova, Malte F. Jung","doi":"10.5898/JHRI.2.1.Breazeal","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Breazeal","url":null,"abstract":"Supporting a wide variety of interaction styles across a diverse set of people is a significant challenge in human-robot interaction (HRI). In this work, we explore a data-driven approach that relies on crowdsourcing as a rich source of interactions that cover a wide repertoire of human behavior. We first develop an online game that requires two players to collaborate to solve a task. One player takes the role of a robot avatar and the other a human avatar, each with a different set of capabilities that must be coordinated to overcome challenges and complete the task. Leveraging the interaction data recorded in the online game, we present a novel technique for data-driven behavior generation using case-based planning for a real robot. We compare the resulting autonomous robot behavior against a Wizard of Oz base case condition in a real-world reproduction of the online game that was conducted at the Boston Museum of Science. Results of a post-study survey of participants indicate that the autonomous robot behavior matched the performance of the human-operated robot in several important measures. We examined video recordings of the real-world game to draw additional insights as to how the novice participants attempted to interact with the robot in a loosely structured collaborative task. We discovered that many of the collaborative interactions were generated in the moment and were driven by interpersonal dynamics, not necessarily by the task design. We explored using bids analysis as a meaningful construct to tap into affective qualities of HRI. An important lesson from this work is that in loosely structured collaborative tasks, robots need to be skillful in handling these in-the-moment interpersonal dynamics, as these dynamics have an important impact on the affective quality of the interaction for people. How such interactions dovetail with more task-oriented policies is an important area for future work, as we anticipate such interactions becoming commonplace in situations where personal robots perform loosely structured tasks in interaction with people in human living spaces.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Breazeal","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yutaka Kondo, K. Takemura, J. Takamatsu, T. Ogasawara
Natural body gesturing and speech dialogue, is crucial for human-robot interaction (HRI) and human-robot symbiosis. Real interaction is not only with one-to-one communication but also among multiple people. We have therefore developed a system that can adjust gestures and facial expressions based on a speaker's location or situation for multi-party communication. By extending our already developed real-time gesture planning method, we propose a gesture adjustment suitable for human demand through motion parameterization and gaze motion planning, which allows communication through eye-to-eye contact. We implemented the proposed motion planning method on an android Actroid-SIT and we proposed to use a Key-Value Store to connect the components of our systems. The Key-Value Store is a high-speed and lightweight dictionary database with parallelism and scalability. We conducted multi-party HRI experiments for 1,662 subjects in total. In our HRI system, over 60% of subjects started speaking to the Actroid, and the residence time of their communication also became longer. In addition, we confirmed our system gave humans a more sophisticated impression of the Actroid.
{"title":"A gesture-centric Android system for multi-party human-robot interaction","authors":"Yutaka Kondo, K. Takemura, J. Takamatsu, T. Ogasawara","doi":"10.5898/JHRI.2.1.Kondo","DOIUrl":"https://doi.org/10.5898/JHRI.2.1.Kondo","url":null,"abstract":"Natural body gesturing and speech dialogue, is crucial for human-robot interaction (HRI) and human-robot symbiosis. Real interaction is not only with one-to-one communication but also among multiple people. We have therefore developed a system that can adjust gestures and facial expressions based on a speaker's location or situation for multi-party communication. By extending our already developed real-time gesture planning method, we propose a gesture adjustment suitable for human demand through motion parameterization and gaze motion planning, which allows communication through eye-to-eye contact. We implemented the proposed motion planning method on an android Actroid-SIT and we proposed to use a Key-Value Store to connect the components of our systems. The Key-Value Store is a high-speed and lightweight dictionary database with parallelism and scalability. We conducted multi-party HRI experiments for 1,662 subjects in total. In our HRI system, over 60% of subjects started speaking to the Actroid, and the residence time of their communication also became longer. In addition, we confirmed our system gave humans a more sophisticated impression of the Actroid.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.2.1.Kondo","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-01-28DOI: 10.5898/JHRI.1.2.Kirchner
N. Kirchner, A. Alempijevic
The industrial revolution undoubtedly defined the role of machines in our society, and it directly shaped the paradigm for human machine interaction - a paradigm which was inherited by the field of Human Robot Interaction (HRI) as the machines became robots. This paper argues that, for a foreseeable set of interactions, reshaping this paradigm would result in more effective and more often successful interactions. This paper presents our Robot Centric paradigm for HRI. Evidence in the form of summaries of relevant literature and our past efforts in developing social-robotics enabling technology is presented to support our paradigm. A definition and a set of recommendations for designing the key enabling component, sociocontextual cues, of our paradigm are presented. Finally, empirical evidence generated through a number of experiments and field studies (N = 456 and N = 320) demonstrates our paradigm is both feasibly incorporated into HRI and moreover, yields significant contributions to the successfulness of a set of HRIs.
{"title":"A robot centric perspective on the HRI paradigm","authors":"N. Kirchner, A. Alempijevic","doi":"10.5898/JHRI.1.2.Kirchner","DOIUrl":"https://doi.org/10.5898/JHRI.1.2.Kirchner","url":null,"abstract":"The industrial revolution undoubtedly defined the role of machines in our society, and it directly shaped the paradigm for human machine interaction - a paradigm which was inherited by the field of Human Robot Interaction (HRI) as the machines became robots. This paper argues that, for a foreseeable set of interactions, reshaping this paradigm would result in more effective and more often successful interactions. This paper presents our Robot Centric paradigm for HRI. Evidence in the form of summaries of relevant literature and our past efforts in developing social-robotics enabling technology is presented to support our paradigm. A definition and a set of recommendations for designing the key enabling component, sociocontextual cues, of our paradigm are presented. Finally, empirical evidence generated through a number of experiments and field studies (N = 456 and N = 320) demonstrates our paradigm is both feasibly incorporated into HRI and moreover, yields significant contributions to the successfulness of a set of HRIs.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.1.2.Kirchner","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-01-28DOI: 10.5898/JHRI.1.2.Goodrich
M. Goodrich, K. S. Eklundh
Human-robot interaction (HRI) is of global importance, as exemplified by the human and financial resources dedicated to research and development in the area. The vision for this special issue was to provide a broad, global perspective on the field. More specifically, the vision was for researchers in representative projects from around the world to share lessons and perspectives on HRI obtained from those projects.
{"title":"Introduction to the special issue on HRI perspectives and projects from around the globe","authors":"M. Goodrich, K. S. Eklundh","doi":"10.5898/JHRI.1.2.Goodrich","DOIUrl":"https://doi.org/10.5898/JHRI.1.2.Goodrich","url":null,"abstract":"Human-robot interaction (HRI) is of global importance, as exemplified by the human and financial resources dedicated to research and development in the area. The vision for this special issue was to provide a broad, global perspective on the field. More specifically, the vision was for researchers in representative projects from around the world to share lessons and perspectives on HRI obtained from those projects.","PeriodicalId":92076,"journal":{"name":"Journal of human-robot interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5898/JHRI.1.2.Goodrich","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71217795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-01-28DOI: 10.5898/JHRI.1.2.Harutyunyan
V. Harutyunyan, V. Manohar, Issak Gezehei, J. Crandall
Remote teleoperation of advanced, semi-autonomous robotic technologies has great potential in many industries critical to the economy and for the environment of the Middle East. Applications include maintenance of under-water oil wells, maintenance of nuclear power plants, counter-terrorism and national defense, law enforcement, remote sensing, and health care. In each of these applications, a user, likely without technology expertise, must operate a complex robot in uncertain and unknown environments. The nature of the tasks and environments encountered by the robot in these applications make it highly likely that the robot's limited autonomy will fail or be insufficient to complete the desired task. In this paper, we argue that, in such scenarios, cognitive telepresence, defined as the ability of the user to comprehend and control the robot's cognition, is an important design principle for human-robot systems. We compare and contrast cognitive telepresence to existing design principles commonly discussed in the literature, and define various metrics of cognitive telepresence. Finally, via two illustrative examples and a user study, we demonstrate the usefulness of cognitive telepresence as an important design principle of human-robot systems consisting of a user with limited technology expertise and a robot with limited and error-prone artificial intelligence.
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