Alle Publikationen
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2017
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(2017): Shopping with a robotic companion. In: Computers in Human Behavior 77, S. 382-395. DOI: 10.1016/j.chb.2017.02.064
DOI: https://doi.org/10.1016/j.chb.2017.02.064 Abstract: In this paper, we present a robotic shopping assistant, designed with a cognitive architecture, grounded in machine learning systems, in order to study how the human-robot interaction (HRI) is changing the shopping behavior in smart technological stores. In the software environment of the NAO robot, connected to the Internet with cloud services, we designed a social-like interaction where the robot carries out actions with the customer. In particular, we focused our design on two main skills the robot has to learn: the first is the ability to acquire social input communicated by relevant clues that humans provide about their emotional state (emotions, emotional speech), or collected in the Social Media (such as, information on the customer's tastes, cultural background, etc.). The second is the skill to express in turn its own emotional state, so that it can affect the customer buying decision, refining in the user the sense of interacting with a human-like companion. By combining social robotics and machine learning systems the potential of robotics to assist people in real life situations will increase, providing a gentle customers' acceptance of advanced technologies. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Keywords: emotion & robotics, Social robotic -
(2017) : Affective facial expressions recognition for human-robot interaction: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Lisbon, Portugal: IEEE Robotics & Automation Society, S. 805-810
DOI: https://doi.org/10.1109/ROMAN.2017.8172395 Abstract: Affective facial expression is a key feature of nonverbal behaviour and is considered as a symptom of an internal emotional state. Emotion recognition plays an important role in social communication: human-to-human and also for human-to-robot. Taking this as inspiration, this work aims at the development of a framework able to recognise human emotions through facial expression for human-robot interaction. Features based on facial landmarks distances and angles are extracted to feed a dynamic probabilistic classification framework. The public online dataset Karolinska Directed Emotional Faces (KDEF) [1] is used to learn seven different emotions (e.g. angry, fearful, disgusted, happy, sad, surprised, and neutral) performed by seventy subjects. A new dataset was created in order to record stimulated affect while participants watched video sessions to awaken their emotions, different of the KDEF dataset where participants are actors (i.e. performing expressions when asked to). Offline and on-the-fly tests were carried out: leave-one-out cross validation tests on datasets and on-the-fly tests with human-robot interactions. Results show that the proposed framework can correctly recognise human facial expressions with potential to be used in human-robot interaction scenarios.
2015
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(2015): Towards artificial emotions to assist social coordination in HRI. In: Int J of Soc Robotics 7 (1), S. 77-88. DOI: 10.1007/s12369-014-0254-y
DOI: http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-42938-001&site=ehost-live Abstract: Coordination of human–robot joint activity must depend on the ability of human and artificial agencies to interpret and interleave their actions. In this paper we consider the potential of artificial emotions to serve as task-relevant coordination devices in human–robot teams. We present two studies aiming to understand whether a non-humanoid robot can express artificial emotions in a manner that is meaningful to a human observer, the first based on static images and the second on the dynamic production of embodied robot expressions. We present a mixed-methods approach to the problem, combining statistical treatment of ratings data and thematic analysis of qualitative data. Our results demonstrate that even very simple movements of a non-humanoid robot can convey emotional meaning, and that when people attribute emotional states to a robot, they typically apply an event-based frame to make sense of the robotic expressions they have seen. Artificial emotions with high arousal level and negative valence are relatively easy for people to recognise compared to expressions with positive valence. We discuss the potential for using motion in different parts of a non-humanoid robot body to support the attribution of emotion in HRI, towards ethically responsible design of artificial emotions that could contribute to the efficacy of joint human–robot activities. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Keywords: emotion & robotics, Social robotic 2008
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Cañamero, Lola; Aylett, Ruth (Hg.) (2008): Animating expressive characters for social interaction. Amsterdam: John Benjamins Publishing Company (Advances in consciousness research; Vol 74; ISSN: 1381-589X (Print))
Keywords: emotion & robotics, Social robotic 2007
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(2007) : Emotion Interaction System for a Service Robot: RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication: RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication: Jeju, Korea: IEEE, S. 351-356
DOI: https://doi.org/10.1109/ROMAN.2007.4415108 Abstract: This paper introduces an emotion interaction system for a service robot. The purpose of emotion interaction systems in service robots is to make people feel that the robot is not a mere machine, but reliable living assistant in the home. The emotion interaction system is composed of the emotion recognition, generation, and expression systems. A user's emotion is recognized by multi-modality, such as voice, dialogue, and touch. The robot's emotion is generated according to a psychological theory about emotion: OCC (Ortony, Clore, and Collins) model, which focuses on the user's emotional state and the information about environment and the robot itself. The generated emotion is expressed by facial expression, gesture, and the musical sound of the robot. Because the proposed system is composed of all the three components that are necessary for a full emotional interaction cycle, it can be implemented in the real robot system and be tested. Even though the multi- modality in emotion recognition and expression is still in its rudimentary stages, the proposed system is shown to be extremely useful in service robot applications. Furthermore, the proposed framework can be a cornerstone for the design of emotion interaction and generation systems for robots.
2005
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(2005): An android for enhancing social skills and emotion recognition in people with autism. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering 13 (4), S. 507-515. DOI: 10.1109/TNSRE.2005.856076
DOI: https://doi.org/10.1109/TNSRE.2005.856076 Abstract: It is well documented that the processing of social and emotional information is impaired in people with autism. Recent studies have shown that individuals, particularly those with high functioning autism, can learn to cope with common social situations if they are made to enact possible scenarios they may encounter in real life during therapy. The main aim of this work is to describe an interactive life-like facial display (FACE) and a supporting therapeutic protocol that will enable us to verify if the system can help children with autism to learn, identify, interpret, and use emotional information and extend these skills in a socially appropriate, flexible, and adaptive context. The therapeutic setup consists of a specially equipped room in which the subject, under the supervision of a therapist, can interact with FACE. The android display and associated control system has automatic facial tracking, expression recognition, and eye tracking. The treatment scheme is based on a series of therapist-guided sessions in which a patient communicates with FACE through an interactive console. Preliminary data regarding the exposure to FACE of two children are reported.
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