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A Semantic-Based Method for Teaching Industrial Robots New Tasks

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Abstract

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.

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Notes

  1. http://www.factory-in-a-day.eu/

  2. The user uses a Graphical User Interface to identify the beginning and end of a task.

  3. The information from the object is obtained either from the vision system or the proximity sensor on the skin. The same is valid for the property ObjectInHand.

  4. The ground-truth was manually labeled by a person considered as an expert since this person received a training session about labeling activities.

  5. The stopping criterion indicates when a process should stop, e.g., duration of the process, the weight of the products, or the number of objects.

  6. This scenario was inspired by the standard process of orange sorting where humans use their tactile sensation to discriminate good versus bad oranges.

  7. One participant was a robotic expert and the other non-expert. We are planning to extend this study to a larger group of participants.

  8. Note that the data from the robot Kinesthetic demonstrations was not used to improve in any way the semantic models \(T_{\textit{sorting}}\).

  9. The knowledge-base used in this experiment contains several squeezable fruits such as oranges, limes, and mandarins. This ontology also contains information about fruits that are not squeezable such as apples and pineapples. Therefore, the proposed system can generalize to the different fruits defined in the ontology domain.

  10. The following link, https://youtu.be/Ti393hP_Z_g [10] presents a video of the obtained results.

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Acknowledgements

We would like to thank our colleagues Katharina Stadler and Wibke Borngesser for all their support during the project Factory-in-a-day.

This work was supported by the European Community Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 609206 and it has been (partially) supported by the German Research Foundation DFG, as part of Collaborative Research Center (Sonderforschungsbereich) 1320 “EASE—Everyday Activity Science and Engineering”, University of Bremen.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Munich, Germany

    Karinne Ramirez-Amaro, Emmanuel Dean-Leon, Florian Bergner & Gordon Cheng

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  1. Karinne Ramirez-Amaro
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  2. Emmanuel Dean-Leon
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  3. Florian Bergner
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  4. Gordon Cheng
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Correspondence to Karinne Ramirez-Amaro.

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Ramirez-Amaro, K., Dean-Leon, E., Bergner, F. et al. A Semantic-Based Method for Teaching Industrial Robots New Tasks. Künstl Intell 33, 117–122 (2019). https://doi.org/10.1007/s13218-019-00582-5

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