Research

Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots

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Abstract

Assisting employees in acquiring the knowledge and skills necessary to 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-formats, issues, tools (LTA-FIT) 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 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-FIT based on an established system usability scale (SUS) 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.