Alle Publikationen
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2019
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(2019) : Good Robot Design or Machiavellian? An In-the-Wild Robot Leveraging Minimal Knowledge of Passersby’s Culture: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Daegu, Korea: IEEE, S. 382-391
DOI: https://doi.org/10.1109/HRI.2019.8673326 Abstract: Social robots are being designed to use human-like communication techniques, including body language, social signals, and empathy, to work effectively with people. Just as between people, some robots learn about people and adapt to them. In this paper we present one such robot design: we developed Sam, a robot that learns minimal information about a person’s background, and adapts to this background. Our in-the-wild study found that people helped Sam for significantly longer when it adapted to match their background. While initially we saw this as a success, in re-considering our study we started seeing a different angle. Our robot effectively deceived people (changed its story and text), based on some knowledge of their background, to get more work from them. There was little direct benefit to the person from this adaptation, yet the robot stood to gain free labor. We would like to pose the question to the community: is this simply good robot design, or, is our robot being manipulative? Where does the ethical line lay between a robot leveraging social techniques to improve interaction, and the more negative framing of a robot or algorithm taking advantage of people? How can we decide what is good here, and what is less desirable?
Keywords: Body language, Cultural differences, Culture, Ethics, Global communication, human-like communication techniques, human-robot interaction, ieee xplore, in the wild, in-the-wild robot, learning (artificial intelligence), minimal information, Mobile robots, Mood, Moral & Ethik, passersby culture, Persuasive Robots, robot design, Robots, Sam, Shape, social robots, Social signals, social techniques, Task Analysis 2017
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Burgoon, Judee K.; Magnenat-Thalmann, Nadia; Pantic, Maja (Hg.) (2017): Social signal processing. Cambridge: Cambridge University Press
Abstract: Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field.
Keywords: Computer vision, Computerwissenschaft, Disziplin, doppelter Treffer, Favoriten, Human behaviour analysis', Interaktion, Mensch-Technik-Relationen (MTR), Modelle/Theorien, Realtechnik, Sammelband, Signal, Signale, Signaling social preferences/ Social signal processing, Social interactions, social signal processing, Social Signal Processing (SSP), social signalling, Social signals, Soziale Angemessenheit, soziale Kognition, Soziosensitive Systeme, speech processing, Technik 2016
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(2016): Editorial: Understanding Social Signals: How Do We Recognize the Intentions of Others?. In: Frontiers in psychology 7. DOI: 10.3389/fpsyg.2016.00281
DOI: https://doi.org/10.3389/fpsyg.2016.00281 Abstract: Humans interact with each other seamlessly, smoothly, and without obvious effort. Social signals are the basis of this highly effective communication. These signals are speech utterances, body movements such as gestures, manipulations of objects, and combinations thereof. For example, interlocutors typically position themselves in an F-formation (Goffman, 1963; Ciolek and Kendon, 1980; Kendon, 1990) and thereby signal to each other that they are part of that interaction. If another participant joins that interaction, the interlocutors integrate her in a new F-formation. The movements of each individual were comparably inconspicuous, but the intention for producing them was easily recognizable to the recipient. Humans use these signals intuitively and without conscious awareness. But in order to enable a robot to understand and respond appropriately to social signals, their form and function have to be made explicit. This research topic presents methods for identifying, understanding, and applying social signals in human–machine interaction.
2015
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(2015): Ghost-in-the-Machine reveals human social signals for human–robot interaction. In: Frontiers in psychology 6. DOI: 10.3389/fpsyg.2015.01641
DOI: https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01641/full Abstract: We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognise the intentions of customers on the basis of the output of the robotic recognisers. Specifically, we measured which recogniser modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behaviour necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognisers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognisers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human-robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience.
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