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Automated Active Learning with a Robot

Scharei, Kristina; Herde, Marek; Bieshaar, Maarten; Calma, Adrian; Kottke, Daniel; Sick, Bernhard

Abstract:
In the field of automated processes in industry, a major goal is for robots to solve new tasks without costly adaptions. Therefore, it is of advantage if the robot can perform new tasks independently while the learning process is intuitively understandable for humans. In this article, we present a highly automated and intuitive active learning algorithm for robots. It learns new classification tasks by asking questions to a human teacher and automatically decides when to stop the learning process by self-assessing its confidence. This so-called stopping criterion is required to guarantee a fully automated procedure. Our approach is highly interactive as we use speech for communication and a graphical visualization tool. The latter provides information about the learning progress and the stopping criterion, which helps the human teacher in understanding the training process better. The applicability of our approach is shown and evaluated on a real Baxter robot.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/16
Veröffentlicht am 02.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000117502
Erschienen in Archives of Data Science, Series A (Online First)
Band 5
Heft 1
Seiten A16, 15 S. online
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