Towards real-world force-sensitive robotic assembly through deep reinforcement learning in simulations

Hebecker M, Lambrecht J, Schmitz M (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2021-July

Pages Range: 1045-1051

Conference Proceedings Title: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Event location: Delft NL

ISBN: 9781665441391

DOI: 10.1109/AIM46487.2021.9517356

Abstract

In manufacturing industries, vast potential exists in regards to the adaptability of automated systems through efficient robotic skill acquisition. This paper examines the use of deep reinforcement learning to automate the process of contact rich compliant assembly. Thereby, we consider an exemplary real-world use case in car assembly. To obtain training data, we use a simulated representation of the production system comprising a robotic arm which is controlled through a deep reinforcement learning agent trained with proximal policy optimization. Furthermore, we conduct a basic system analysis to improve the similarity between our physical and simulated environments. After iteratively training and evaluating different models, which distinguish through the reward design and the grade of environment variation, we validate the results on the physical hardware. We successfully obtain agents that generate expedient trajectories, which can generalize to changing environments. Success rates clearly above 90% can be achieved in simulation even with high grades of variation of the target position and the parts' surface friction. For the transfer to the physical assembly system, we conclude that further optimization is necessary to obtain truly compliant behavior.

Authors with CRIS profile

Markus Walk Lehrstuhl für Wirtschaftsinformatik, insbesondere IT-Management

Involved external institutions

Technische Universität Berlin DE Germany (DE)

How to cite

APA:

Hebecker, M., Lambrecht, J., & Schmitz, M. (2021). Towards real-world force-sensitive robotic assembly through deep reinforcement learning in simulations. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (pp. 1045-1051). Delft, NL: Institute of Electrical and Electronics Engineers Inc..

MLA:

Hebecker, Marius, Jens Lambrecht, and Markus Schmitz. "Towards real-world force-sensitive robotic assembly through deep reinforcement learning in simulations." Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021, Delft Institute of Electrical and Electronics Engineers Inc., 2021. 1045-1051.

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