The Experts below are selected from a list of 2817 Experts worldwide ranked by ideXlab platform
Maja J Mataric - One of the best experts on this subject based on the ideXlab platform.
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autonomous human robot Proxemics socially aware navigation based on interaction potential
Autonomous Robots, 2017Co-Authors: Ross Mead, Maja J MataricAbstract:To enable situated human---robot interaction (HRI), an autonomous robot must both understand and control Proxemics--the social use of space--to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of Proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and models were implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human---robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human---robot Proxemics with respect to "interaction potential"--predicted automated speech and gesture recognition rates as the robot enters into and engages in face-to-face social encounters with a human user--illustrating their efficacy to support richer robot perception and autonomy in HRI.
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robots have needs too how and why people adapt their proxemic behavior to improve robot social signal understanding
Human-Robot Interaction, 2016Co-Authors: Ross Mead, Maja J MataricAbstract:Human preferences of distance (Proxemics) to a robot significantly impact the performance of the robot's automated speech and gesture recognition during face-to-face, social human-robot interactions. This work investigated how people respond to a sociable robot based on its performance at different locations. We performed an experiment in which the robot's ability to understand social signals was artificially attenuated by distance. Participants (N = 180) instructed the robot using speech and pointing gestures, provided proxemic preferences before and after the interaction, and responded to a questionnaire. Our analysis of questionnaire responses revealed that robot performance factors---rather than human-robot Proxemics---are significant predictors of user evaluations of robot competence, anthropomorphism, engagement, likability, and technology adoption. Our behavioral analysis suggests that human proxemic preferences change over time as users interact with and come to understand the needs of the robot, and those changes improve robot performance.
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perceptual models of human robot Proxemics
International Symposium on Experimental Robotics, 2016Co-Authors: Ross Mead, Maja J MataricAbstract:To enable socially situated human-robot interaction, a robot must both understand and control Proxemics—the social use of space—to employ communication mechanisms analogous to those used by humans. In this work, we considered how proxemic behavior is influenced by human speech and gesture production, and how this impacts robot speech and gesture recognition in face-to-face social interactions. We conducted a data collection to model these factors conditioned on distance. This resulting models of pose, speech, and gesture were consistent with related work in human-human interactions, but were inconsistent with related work in human-human interactions—participants in our data collection pos itioned themselves much farther away than has been observed in related work. These models have been integrated into a situated autonomous proxemic robot controller, in which the robot selects interagent pose parameters to maximize its expectation to recognize natural human speech and body gestures during an interaction. This work contributes to the understanding of the underlying per-cultural processes that govern human proxemic behavior, and has implications for the development of robust proxemic controllers for sociable and socially assistive robots situated in complex interactions (e.g., with multiple people or individuals with hearing/visual impairments) and environments (e.g., in which there is loud noise, reverberation, low lighting, or visual occlusion).
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probabilistic models of Proxemics for spatially situated communication in hri
Human-Robot Interaction, 2014Co-Authors: Ross Mead, Maja J MataricAbstract:To enable socially situated human-robot interaction, a robot must both understand and control Proxemics—the social use of space—in order to employ communication mechanisms analogous to those used by humans. In this work, we focus on social speech and gesture production and recognition during both human-human and human-robot interactions. We conducted a data collection to model these factors as a function of the relative distance and orientation between sociable agents. The resulting models were used to implement a spatially situated autonomous proxemic robot controller. The controller utilizes a samplingbased approach, wherein each sample represents interagent distance and orientation, as well as agent speech and gesture production and recognition estimates; a particle filter (with resampling) uses these estimates to maximize the performance of both the robot and the human during the interaction. This functional approach yields pose, speech, and gesture estimates consistent with related work in human-human and human-robot interactions. This work contributes to the understanding of the underlying pre-cultural processes that govern proxemic behavior, and has implications for the development of robust proxemic controllers for robots situated in complex interactions (e.g., with more than two agents, or with individuals with hearing or visual impairments) and environments (e.g., with loud noises, low light, or visual occlusions).
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automated proxemic feature extraction and behavior recognition applications in human robot interaction
International Journal of Social Robotics, 2013Co-Authors: Ross Mead, Amin Atrash, Maja J MataricAbstract:In this work, we discuss a set of feature repre- sentations for analyzing human spatial behavior (Proxemics) motivated by metrics used in the social sciences. Specif- ically, we consider individual, physical, and psychophys- ical factors that contribute to social spacing. We demon- strate the feasibility of autonomous real-time annotation of these proxemic features during a social interaction between two people and a humanoid robot in the presence of a vi- sual obstruction (a physical barrier). We then use two differ- ent feature representations—physical and psychophysical— to train Hidden Markov Models (HMMs) to recognize spa- tiotemporal behaviors that signify transitions into (initiation) and out of (termination) a social interaction. We demonstrate that the HMMs trained on psychophysical features, which encode the sensory experience of each interacting agent, outperform those trained on physical features, which only encode spatial relationships. These results suggest a more powerful representation of proxemic behavior with particu- lar implications in autonomous socially interactive and so- cially assistive robotics.
Ross Mead - One of the best experts on this subject based on the ideXlab platform.
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Autonomous human–robot Proxemics: socially aware navigation based on interaction potential
Autonomous Robots, 2017Co-Authors: Ross Mead, Maja J MatarićAbstract:To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control Proxemics —the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of Proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and models were implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot Proxemics with respect to “interaction potential”— predicted automated speech and gesture recognition rates as the robot enters into and engages in face-to-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI.
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autonomous human robot Proxemics socially aware navigation based on interaction potential
Autonomous Robots, 2017Co-Authors: Ross Mead, Maja J MataricAbstract:To enable situated human---robot interaction (HRI), an autonomous robot must both understand and control Proxemics--the social use of space--to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of Proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and models were implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human---robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human---robot Proxemics with respect to "interaction potential"--predicted automated speech and gesture recognition rates as the robot enters into and engages in face-to-face social encounters with a human user--illustrating their efficacy to support richer robot perception and autonomy in HRI.
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robots have needs too how and why people adapt their proxemic behavior to improve robot social signal understanding
Human-Robot Interaction, 2016Co-Authors: Ross Mead, Maja J MataricAbstract:Human preferences of distance (Proxemics) to a robot significantly impact the performance of the robot's automated speech and gesture recognition during face-to-face, social human-robot interactions. This work investigated how people respond to a sociable robot based on its performance at different locations. We performed an experiment in which the robot's ability to understand social signals was artificially attenuated by distance. Participants (N = 180) instructed the robot using speech and pointing gestures, provided proxemic preferences before and after the interaction, and responded to a questionnaire. Our analysis of questionnaire responses revealed that robot performance factors---rather than human-robot Proxemics---are significant predictors of user evaluations of robot competence, anthropomorphism, engagement, likability, and technology adoption. Our behavioral analysis suggests that human proxemic preferences change over time as users interact with and come to understand the needs of the robot, and those changes improve robot performance.
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perceptual models of human robot Proxemics
International Symposium on Experimental Robotics, 2016Co-Authors: Ross Mead, Maja J MataricAbstract:To enable socially situated human-robot interaction, a robot must both understand and control Proxemics—the social use of space—to employ communication mechanisms analogous to those used by humans. In this work, we considered how proxemic behavior is influenced by human speech and gesture production, and how this impacts robot speech and gesture recognition in face-to-face social interactions. We conducted a data collection to model these factors conditioned on distance. This resulting models of pose, speech, and gesture were consistent with related work in human-human interactions, but were inconsistent with related work in human-human interactions—participants in our data collection pos itioned themselves much farther away than has been observed in related work. These models have been integrated into a situated autonomous proxemic robot controller, in which the robot selects interagent pose parameters to maximize its expectation to recognize natural human speech and body gestures during an interaction. This work contributes to the understanding of the underlying per-cultural processes that govern human proxemic behavior, and has implications for the development of robust proxemic controllers for sociable and socially assistive robots situated in complex interactions (e.g., with multiple people or individuals with hearing/visual impairments) and environments (e.g., in which there is loud noise, reverberation, low lighting, or visual occlusion).
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probabilistic models of Proxemics for spatially situated communication in hri
Human-Robot Interaction, 2014Co-Authors: Ross Mead, Maja J MataricAbstract:To enable socially situated human-robot interaction, a robot must both understand and control Proxemics—the social use of space—in order to employ communication mechanisms analogous to those used by humans. In this work, we focus on social speech and gesture production and recognition during both human-human and human-robot interactions. We conducted a data collection to model these factors as a function of the relative distance and orientation between sociable agents. The resulting models were used to implement a spatially situated autonomous proxemic robot controller. The controller utilizes a samplingbased approach, wherein each sample represents interagent distance and orientation, as well as agent speech and gesture production and recognition estimates; a particle filter (with resampling) uses these estimates to maximize the performance of both the robot and the human during the interaction. This functional approach yields pose, speech, and gesture estimates consistent with related work in human-human and human-robot interactions. This work contributes to the understanding of the underlying pre-cultural processes that govern proxemic behavior, and has implications for the development of robust proxemic controllers for robots situated in complex interactions (e.g., with more than two agents, or with individuals with hearing or visual impairments) and environments (e.g., with loud noises, low light, or visual occlusions).
Cade Mccall - One of the best experts on this subject based on the ideXlab platform.
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facing off with unfair others introducing proxemic imaging as an implicit measure of approach and avoidance during social interaction
PLOS ONE, 2015Co-Authors: Cade Mccall, Tania SingerAbstract:Nonverbal behavior expresses many of the dynamics underlying face-to-face social interactions, implicitly revealing one’s attitudes, emotions, and social motives. Although research has often described nonverbal behavior as approach versus avoidant (i.e., through the study of Proxemics), psychological responses to many social contexts are a mix of these two. Fairness violations are an ideal example, eliciting strong avoidance-related responses such as negative attitudes, as well as strong approach-related responses such as anger and retaliation. As such, nonverbal behavior toward unfair others is difficult to predict in discrete approach versus avoidance terms. Here we address this problem using proxemic imaging, a new method which creates frequency images of dyadic space by combining motion capture data of interpersonal distance and gaze to provide an objective but nuanced analysis of social interactions. Participants first played an economic game with fair and unfair players and then encountered them in an unrelated task in a virtual environment. Afterwards, they could monetarily punish the other players. Proxemic images of the interactions demonstrate that, overall, participants kept the fair player closer. However, participants who actively punished the unfair players were more likely to stand directly in front of those players and even to turn their backs on them. Together these patterns illustrate that fairness violations influence nonverbal behavior in ways that further predict differences in more overt behavior (i.e., financial punishment). Moreover, they demonstrate that proxemic imaging can detect subtle combinations of approach and avoidance behavior during face-to-face social interactions.
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mapping social interactions the science of Proxemics
Current topics in behavioral neurosciences, 2015Co-Authors: Cade MccallAbstract:Interpersonal distance and gaze provide a wealth of information during face-to-face social interactions. These “proxemic” behaviors offer a window into everyday social cognition by revealing interactants’ affective states (e.g., interpersonal attitudes) and cognitive responses (e.g., social attention). Here we provide a brief overview of the social psychological literature in this domain. We focus on new techniques for experimentally manipulating and measuring Proxemics, including the use of immersive virtual environments and digital motion capture. We also discuss ways in which these approaches can be integrated with psychophysiological and neuroimaging techniques. Throughout, we argue that contemporary Proxemics research provides psychology and neuroscience with a means to study social cognition and behavior as they naturally emerge and unfold in vivo.
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the effects of virtual agent humor and gaze behavior on human virtual agent Proxemics
Intelligent Virtual Agents, 2011Co-Authors: Peter Khooshabeh, Cade Mccall, Sudeep Gandhe, Jonathan Gratch, Jim Blascovich, David TraumAbstract:We study whether a virtual agent that delivers humor through verbal behavior can affect an individual's proxemic behavior towards the agent. Participants interacted with a virtual agent through natural language and, in a separate task, performed an embodied interpersonal interaction task in a virtual environment. The study used minimum distance as the dependent measure. Humor generated by the virtual agent through a text chat did not have any significant effects on the proxemic task. This is likely due to the experimental constraint of only allowing participants to interact with a disembodied agent through a textual chat dialogue.
Peshala G. Jayasekara - One of the best experts on this subject based on the ideXlab platform.
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an exploratory study on Proxemics preferences of humans in accordance with attributes of service robots
Robot and Human Interactive Communication, 2019Co-Authors: S. M. Bhagya, P. Samarakoon, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. Jayasekara, Mohan Rajesh ElaraAbstract:Service robots that possess social interactive capabilities are vital to cater to the demand in emerging domains of robotic applications. A service robot frequently needs to interact with users when performing service tasks. The comfortability of users depends on the human-robot Proxemics during these interactions. Hence, a service robot should be capable of maintaining proper Proxemics that improves the comfort of users. The Proxemics preferences of users might depend on diverse attributes of a robot, such as emotional state, noise level, and physical appearance. Therefore, it is vital to gain a better understanding of a robot’s attributes which influence human-robot Proxemics behavior. This paper contributes to an exploratory study to analyze the effects on human-robot Proxemics preferences due to a robot’s attributes; facial and vocal emotions, level of internal noises, and the physical appearance. Four sub-studies have been conducted to gather the required human-robot Proxemics data. The gathered data have been analyzed through statistical tests. The test statistics reveal that facial and vocal emotions, internal noise level, and the physical appearance of a robot have significant effects on Proxemics preferences of humans. The outcomes of this exploratory study would be useful in designing and developing human-robot Proxemics strategies of a service robot that would enhance social interaction.
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Proxemics and approach evaluation by service robot based on user behavior in domestic environment
Intelligent Robots and Systems, 2018Co-Authors: S. M. Bhagya, P. Samarakoon, H. P. Chapa Sirithunge, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. JayasekaraAbstract:Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, Proxemics planner of a service robot should be cable of deciding the approaching Proxemics based on user behavior. This paper proposes a method to decide the approaching Proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the Proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.
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IROS - Proxemics and Approach Evaluation by Service Robot Based on User Behavior in Domestic Environment
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: S. M. Bhagya, P. Samarakoon, H. P. Chapa Sirithunge, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. JayasekaraAbstract:Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, Proxemics planner of a service robot should be cable of deciding the approaching Proxemics based on user behavior. This paper proposes a method to decide the approaching Proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the Proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.
S. M. Bhagya - One of the best experts on this subject based on the ideXlab platform.
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an exploratory study on Proxemics preferences of humans in accordance with attributes of service robots
Robot and Human Interactive Communication, 2019Co-Authors: S. M. Bhagya, P. Samarakoon, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. Jayasekara, Mohan Rajesh ElaraAbstract:Service robots that possess social interactive capabilities are vital to cater to the demand in emerging domains of robotic applications. A service robot frequently needs to interact with users when performing service tasks. The comfortability of users depends on the human-robot Proxemics during these interactions. Hence, a service robot should be capable of maintaining proper Proxemics that improves the comfort of users. The Proxemics preferences of users might depend on diverse attributes of a robot, such as emotional state, noise level, and physical appearance. Therefore, it is vital to gain a better understanding of a robot’s attributes which influence human-robot Proxemics behavior. This paper contributes to an exploratory study to analyze the effects on human-robot Proxemics preferences due to a robot’s attributes; facial and vocal emotions, level of internal noises, and the physical appearance. Four sub-studies have been conducted to gather the required human-robot Proxemics data. The gathered data have been analyzed through statistical tests. The test statistics reveal that facial and vocal emotions, internal noise level, and the physical appearance of a robot have significant effects on Proxemics preferences of humans. The outcomes of this exploratory study would be useful in designing and developing human-robot Proxemics strategies of a service robot that would enhance social interaction.
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Proxemics and approach evaluation by service robot based on user behavior in domestic environment
Intelligent Robots and Systems, 2018Co-Authors: S. M. Bhagya, P. Samarakoon, H. P. Chapa Sirithunge, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. JayasekaraAbstract:Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, Proxemics planner of a service robot should be cable of deciding the approaching Proxemics based on user behavior. This paper proposes a method to decide the approaching Proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the Proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.
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IROS - Proxemics and Approach Evaluation by Service Robot Based on User Behavior in Domestic Environment
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: S. M. Bhagya, P. Samarakoon, H. P. Chapa Sirithunge, M. A. Viraj, J. Muthugala, A. G. Buddhika, Peshala G. JayasekaraAbstract:Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, Proxemics planner of a service robot should be cable of deciding the approaching Proxemics based on user behavior. This paper proposes a method to decide the approaching Proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the Proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.
