Learning Probabilistic Models to Enhance the Efficiency of Programming-by Demonstration for Industrial Robots

Abstract

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.