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Contact Skill Imitation Learning for Robot-Independent Assembly Programming

Scherzinger, Stefan; Roennau, Arne; Dillmann, Rudiger

Abstract (englisch):
Robotic automation is a key driver for the advancement of technology. The skills of human workers, however, are difficult to program and seem currently unmatched by technical systems. In this work we present a data-driven approach to extract and learn robot-independent contact skills from human demonstrations in simulation environments, using a Long Short Term Memory (LSTM) network. Our model learns to generate error-correcting sequences of forces and torques in task space from object-relative motion, which industrial robots carry out through a Cartesian force control scheme on the real setup. This scheme uses forward dynamics computation of a virtually conditioned twin of the manipulator to solve the inverse kinematics problem. We evaluate our methods with an assembly experiment, in which our algorithm handles part tilting and jamming in order to succeed. The results show that the skill is robust towards localization uncertainty in task space and across different joint configurations of the robot. With our approach, non-experts can easily program force-sensitive assembly tasks in a robot-independent way.

DOI: 10.1109/IROS40897.2019.8967523
Zitationen: 10
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 11.2019
Sprache Englisch
Identifikator ISBN: 978-1-7281-4005-6
KITopen-ID: 1000139835
Erschienen in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3-8 Nov. 2019
Veranstaltung IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Macao, Macao, 03.11.2019 – 08.11.2019
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 4309–4316
Nachgewiesen in Scopus
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