Semantic-based Method for Teaching Industrial Robots New Tasks
Artikel i vetenskaplig tidskrift, 2019

This paper presents the results of the Artificial Intelligence (AI) method developed during the European project “Factory-in-a-day”. Advanced AI solutions, as the one proposed, allow a natural Human–Robot-collaboration, which is an important capability of robots in industrial warehouses. This new generation of robots is expected to work in heterogeneous production lines by efficiently interacting and collaborating with human co-workers in open and unstructured dynamic environments. For this, robots need to understand and recognize the demonstrations from different operators. Therefore, a flexible and modular process to program industrial robots has been developed based on semantic representations. This novel learning by demonstration method enables non-expert operators to program new tasks on industrial robots.

Semantic representations · Knowledge and reasoning · Teaching by demonstration


Karinne Ramirez-Amaro

Technische Universität München

Emmanuel Dean

Technische Universität München

Florian Bergner

Technische Universität München

Gordon Cheng

Technische Universität München

KI - Künstliche Intelligenz

0933-1875 (ISSN)

Vol. 33 2 117-122


Robotteknik och automation

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)



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