Alle Publikationen
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2017
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(2017) : Cross-cultural differences for adaptive strategies of robots in public spaces: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Lisbon, Portugal: IEEE Robotics & Automation Society, S. 573-578
DOI: https://doi.org/10.1109/ROMAN.2017.8172360 Abstract: Robots deployed in public spaces must necessarily deal with situations that demand them to engage humans in a socially and culturally appropriate manner. However, social environments are often complex and ambiguous: many queries to the robot are collaborative (e.g. a family), and in case of conflicting queries, social robots need to participate in value decisions and negotiating multi-party interactions. Given the strong influence of the people’s demographic information and social schema among people, such as relationships and hierarchies, the focus of this research is to examine whether and how people exhibit socio-psychological effects with a shared robot deployed at international events or spaces (e.g. airports). With the aim to investigate who robots should adapt to (children or adults) in multi-party situations within human-robot interactions in public spaces and whether this adaptation can be influenced by culture, this paper presents a cross-cultural study conducted online. The results include a number of interesting findings based on people’s relationship with a child and their parental status. In addition, a number of cross-cultural differences were identified in respondents’ attitude towards robot’s multi-party adaptation in various public settings.
Keywords: Airports, Angemessen(heit) (von Technik), control engineering computing, cross-cultural differences, cultural aspects, Cultural differences, Face recognition, Foot, human-robot interaction, human-robot interactions, ieee xplore, multiparty adaptation, PSYCHOLOGY, public spaces, Robots, social robots, socio-psychological effects, speech, Videos -
(2017) : Learning behavioral norms in uncertain and changing contexts: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom): Debrecen, Hungary: IEEE, S. 000301-000306
DOI: https://doi.org/10.1109/CogInfoCom.2017.8268261 Abstract: Human behavior is often guided by social and moral norms. Robots that enter human societies must therefore behave in norm-conforming ways as well to increase coordination, predictability, and safety in human-robot interactions. However, human norms are context-specific and laced with uncertainty, making the representation, learning, and communication of norms challenging. We provide a formal representation of norms using deontic logic, Dempster-Shafer Theory, and a machine learning algorithm that allows an artificial agent to learn norms under uncertainty from human data. We demonstrate a novel cognitive capability with which an agent can dynamically learn norms while being exposed to distinct contexts, recognizing the unique identity of each context and the norms that apply in it.
Keywords: Artificial agent, behavioral norms, behavioural sciences computing, Cognition, Conferences, Dempster-Shafer Theory, deontic logic, Ethics, formal logic, Human behavior, human societies, human-robot interaction, human-robot interactions, ieee xplore, inference mechanisms, learning (artificial intelligence), Libraries, machine learning algorithm, Moral & Ethik, moral norms, norm-conforming ways, Robot kinematics, Social Norms, uncertainty, uncertainty handling 2016
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(2016): A survey of autonomous human affect detection methods for social robots engaged in natural HRI. In: Journal of Intelligent & Robotic Systems 82 (1), S. 101-133. DOI: 10.1007/s10846-015-0259-2
DOI: https://doi.org/10.1007/s10846-015-0259-2 Abstract: In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Keywords: affect classification models, Affective Valence, automated affect detection, Body language, FACIAL EXPRESSIONS, Human Computer Interaction, human-robot interactions, Mensch-Roboter-Interaktion, Mensch-Technik-Relation, multi-modal, physiological signals, physiology, Social intelligence, Soziale Intelligenz, VOICE 2014
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(2014): Recognizing emotional body language displayed by a human-like social robot. In: International Journal of Social Robotics 6 (2), S. 261-280. DOI: 10.1007/s12369-013-0226-7
DOI: https://doi.org/10.1007/s12369-013-0226-7 Abstract: Natural social human–robot interactions (HRIs) require that robots have the ability to perceive and identify complex human social behaviors and, in turn, be able to also display their own behaviors using similar communication modes. Recently, it has been found that body language plays an important role in conveying information about changes in human emotions during human–human interactions. Our work focuses on extending this concept to robotic affective communication during social HRI. Namely, in this paper, we explore the design of emotional body language for our human-like social robot, Brian 2.0. We develop emotional body language for the robot using a variety of body postures and movements identified in human emotion research. To date, only a handful of researchers have focused on the use of robotic body language to display emotions, with a significant emphasis being on the display of emotions through dance. Such emotional dance can be effective for small robots with large workspaces, however, it is not as appropriate for life-sized robots such as Brian 2.0 engaging in one-on-one interpersonal social interactions with a person. Experiments are presented to evaluate the feasibility of the robot’s emotional body language based on human recognition rates. Furthermore, a unique comparison study is presented to investigate the perception of human body language features displayed by the robot with respect to the same body language features displayed by a human actor. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
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