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In this paper a concept and its implementation of an ergonomic cognitive assistant system for supporting human workers at complex assembly tasks in industrial environments is introduced. Depending on the level of the user's product knowledge this mixed-initiative system follows and gains knowledge from the human worker's construction steps while it is also(More)
—We present the MuDiS project. The main goal of MuDiS is to develop a Multimodal Dialogue System that can be adapted quickly to a wide range of various scenarios. In this interdisciplinary project, we unite researchers from diverse areas, including computational linguistics, computer science, electrical engineering, and psychology. The different research(More)
— This paper presents a concept of a smart working environment designed to allow true joint-actions of humans and industrial robots. The proposed system perceives its environment with multiple sensor modalities and acts in it with an industrial robot manipulator to assemble capital goods together with a human worker. In combination with the reactive(More)
— Traditional systems for digital assistance in manual assembly, e.g. optical displays at the work place, are inherently suboptimal for providing efficient and ergonomically feasible worker guidance. The display of sequential instructions does not offer an increase in productivity beyond a certain degree. Little situational support and the resulting(More)
Keywords: Adaptive control Hybrid assembly Instruction based learning Multi-modal interaction Worker surveillance a b s t r a c t Efficient cooperation of humans and industrial robots is based on a common understanding of the task as well as the perception and understanding of the partner's action in the next step. In this article, a hybrid assembly station(More)
This paper presents a system for another input modality in a multimodal human-machine interaction scenario. In addition to other common input modalities, e.g. speech, we extract head gestures by image interpretation techniques based on machine learning algorithms to have a nonverbal and familiar way of interacting with the system. Our experimental(More)
In this paper, we present a novel approach for multimodal interactions between humans and industrial robots. The application scenario is situated in a factory, where a human worker is supported by a robot to accomplish a given hybrid assembly scenario, that covers manual and automated assembly steps. The robot is acting as an assistant as well as a fully(More)
This article presents an integrated framework for multi-modal adap-tive cognitive technical systems to guide, assist and observe human workers in complex manual assembly environments. The demand for highly flexible construction facilities obviously contradicts longer training and preparation phases of human workers. By giving context-aware building(More)