Alle Publikationen
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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.
2013
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(2013): Ontology-based state representations for intention recognition in human–robot collaborative environments. In: Robotics and Autonomous Systems 61 (11), S. 1224-1234. DOI: 10.1016/j.robot.2013.04.004
DOI: https://doi.org/10.1016/j.robot.2013.04.004 Abstract: In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human–robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level state relationships that must be true for future actions to occur, a robot can use the detailed state information described in this paper to infer the probability of subsequent actions occurring. This would allow the robot to better help the human with the task or, at a minimum, better stay out of his or her way. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Keywords: Bedienung & Handhabung, Environmental Effects, Human Machine Systems, human robot environments, Intention, intention recognition, Intentional Learning, Knowledge representation, Ontology (Philosophy), ontology based state representations, Recognition (Learning), Robotics, Spatial Learning, spatial relationships
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