Alle Publikationen
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2017
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(2017): Enabling robotic social intelligence by engineering human social-cognitive mechanisms. In: COGNITIVE SYSTEMS RESEARCH 43, S. 190-207. DOI: 10.1016/j.cogsys.2016.09.005
DOI: https://doi.org/10.1016/j.cogsys.2016.09.005 Abstract: For effective human-robot interaction, we argue that robots must gain social-cognitive mechanisms that allow them to function naturally and intuitively during social interactions with humans. However, a lack of consensus on social cognitive processes poses a challenge for how to design such mechanisms for artificial cognitive systems. We discuss a recent integrative perspective of social cognition to provide a systematic theoretical underpinning for computational instantiations of these mechanisms. We highlight several commitments of our approach that we refer to as Engineering Human Social Cognition. We then provide a series of recommendations to facilitate the development of the perceptual, motor, and cognitive architecture for this proposed artificial cognitive system in future work. For each recommendation, we highlight their relation to the discussed social-cognitive mechanisms, provide the rationale for these recommendations and potential benefits, and detail examples of associated computational formalisms that could be leveraged to instantiate our recommendations. Overall, the goal of this paper is to outline an interdisciplinary and multi-theoretic approach to facilitate the design of robots that will one day function, and be perceived, as socially interactive and effective teammates. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
2004
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(2004): From humanoid embodiment to theory of mind. In: EMBODIED ARTIFICIAL INTELLIGENCE, S. 202-218
Abstract: We propose to investigate the foundations of communication and symbolic behavior by means or a robotics approach, i.e. by studying how these behaviors might emerge from the physical dynamics of an agent and its sensory-motor interactions with the real world. In this perspective, the human-robot interface problem can be viewed as one of coupling the interaction dynamics of all agents. Through a number of case studies we will show that within this interaction dynamics there is sparse global structure, i.e. a structure that can be characterized by only a small number of points in phase space, and that it is best to interact with the agent, i.e. interfere with its dynamics, at these points. We introduce a humanoid robot with the capability for dynamic full-body movement. The preliminary results of two experiments, sitting and standing up, are presented. Lastly, experiments with self exploratory learning of embodiment and visual motor learning of neonatal imitation abilities are introduced.
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