Active Sorting - An Efficient Training of a Sorting Robot with Active Learning Techniques

Marek Herde, Daniel Kottke, Adrian Calma, Maarten Bieshaar, Stephan Deist, Bernhard Sick

Abstract: As robots are employed for automating processes in industry, there is a strong demand on robots being able to solve new tasks without costly adaptions. In this article, we present a probabilistic active learning approach for adapting robots to tasks involving object sorting by means of classification according to a human understanding of the problem and its solution. In the beginning, the robot extracts appropriate features of images of the available objects. These features are used by the robot to actively learn to solve the sorting task by asking a human teacher. A novel visualization tool allows to supervise the learning process and to determine when training is complete. Then, the robot is able to successfully sort the objects autonomously. We show our method's superiority compared to other active learning strategies and present results of its application on a real robot to prove the above concept.

External IDs:dblp:conf/ijcnn/HerdeKCBDS18