Enhancing digital twins through reinforcement learning
Paper in proceedings, 2019

Digital Twins are core enablers of smart and autonomous manufacturing systems. Although they strive to represent their physical counterpart as accurately as possible, slight model or data errors will remain. We present an algorithm to compensate for those residual errors through Reinforcement Learning (RL) and data fed back from the manufacturing system. When learning, the Digital Twin acts as teacher and safety policy to ensure minimal performance. We test the algorithm in a sheet metal assembly context, in which locators of the fixture are optimally adjusted for individual assemblies. Our results show a fast adaption and improved performance of the autonomous system.


Constantin Cronrath

Chalmers, Electrical Engineering, Systems and control, Automation

Abolfazl Rezaei Aderiani

Chalmers, Industrial and Materials Science, Product Development

Bengt Lennartson

Chalmers, Electrical Engineering, Systems and control

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2019-August 293-298 8842888

15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Vancouver, Canada,

Subject Categories

Other Computer and Information Science

Embedded Systems

Computer Systems



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