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Learning probabilistic models to enhance the efficiency of programming-by demonstration for industrial robots

: Hollmann, Rebecca; Hägele, Martin; Verl, Alexander

Informationstechnische Gesellschaft -ITG-, Information Technology Society of VDE; Verband Deutscher Maschinen- und Anlagenbau e.V. -VDMA-, Fachverband Robotik und Automation, Frankfurt/Main; International Federation of Robotics; Deutsche Gesellschaft für Robotik -DGR-:
ISR/ROBOTIK 2010, Proceedings for the joint conference of ISR 2010, 41st International Symposium on Robotics und ROBOTIK 2010, 6th German Conference on Robotics : 7-9 June 2010 - Parallel to AUTOMATICA
Berlin: VDE-Verlag, 2010
ISBN: 978-3-8007-3273-9
International Symposium on Robotics (ISR) <41, 2010, Munich>
German Conference on Robotics (ROBOTIK) <6, 2010, Munich>
Internationale Fachmesse für Automation und Mechatronik (Automatica) <4, 2010, Munich>
Fraunhofer IPA ()
Hidden Markov Models; Hidden-Markov-Modell; Roboterprogrammierung; KMU (Kleine und mittlere Unternehmen); Roboter; Mensch Maschine System; Zusammenarbeit; Programmieren

The integration of industrial robot systems into the manufacturing environments of small and medium sized enterprises is a key requirement the guarantee competitiveness and productivity. Due to the still complex and time-consuming procedure of robot path definition, novel programming strategies are needed converting the robotic system into a flexible coworker that actively supports its operator via an efficient user interface. In this article, a learning-from-demonstration strategy based on Hidden Markov Models is presented, which permits the robot system to adapt to user- as well as process-specific features. To evaluate the suitability of this approach for small-lot production, the learning strategy has been implemented for an arc welding robot and has been evaluated on-site at a medium sized metal-working company.