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Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots
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  • Chen Li ,
  • Dimitrios Chrysostomou ,
  • Daniela Pinto ,
  • Andreas Kornmaaler Hansen ,
  • Simon Bøgh ,
  • Ole Madsen
Chen Li
Aalborg University, Aalborg University

Corresponding Author:[email protected]

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Dimitrios Chrysostomou
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Daniela Pinto
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Andreas Kornmaaler Hansen
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Simon Bøgh
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Ole Madsen
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Assisting employees in acquiring the knowledge and skills necessary to grasp and use new services and technologies on the shop floor is critical for manufacturers to adapt to Industry 4.0 successfully. In this paper, we employ a Learning, Training, Assistance (LTA) approach and propose a framework for a Language-enabled Virtual Assistant (VA) to facilitate this adaptation. In our system, the human-robot interaction is achieved through spoken natural language and a dashboard implemented as a web-based application. This type of interaction enables operators of all levels to control a collaborative robot intuitively in several industrial scenarios and use it as a complementary tool for developing their competencies. Our proposed framework has been extensively tested with 29 users who completed various tasks while interacting with the proposed VA and industrial robots. Through three different scenarios, we evaluated the usability of the system for LTA based on an established System Usability Scale and the cognitive effort required by the users based on the standardised NASA TLX questionnaire. The qualitative and quantitative results of the study show that users of all levels found the VA user friendly with low requirements for physical and mental effort during the interaction. Additionally, the study demonstrates that the VA enables operators to streamline the learning and training phases of new tasks and improve their user experience during the assistance phase for daily tasks. The source code of the proposed VA and the supplementary material of the user study are accessible at https://bit.ly/VA MAX to support the reproducibility of the proposed framework.
23 Dec 2022Published in Applied Sciences volume 13 issue 1 on pages 205. 10.3390/app13010205