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A HMM-based approach to learning probability models of programming strategies for industrial robots

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


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Robotics and Automation, ICRA 2010. Vol.4 : Anchorage, Alaska, USA, 3 - 8 May 2010
Piscataway/NJ: IEEE, 2010
ISBN: 978-1-4244-5038-1
ISBN: 978-1-4244-5040-4
International Conference on Robotics and Automation (ICRA) <2010, Anchorage/Alas.>
Conference Paper
Fraunhofer IPA ()
Hidden-Markov-Modell; Hidden Markov Models; welding robot system; robotic system; KMU (Kleine und mittlere Unternehmen); Roboterprogrammierung; Roboter; Mensch Maschine System; Programmieren

The integration of industrial robot systems into the manufacturing environments of small and medium sized enterprises is a key requirement to 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.
In this paper, 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 smalI-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.