The Furtherance of Autonomous Engineering via Reinforcement Learning

Doris Antensteiner, Vincent Dietrich, Michael Fiegert

2021

Abstract

Engineering efforts are one of the major cost factors in today’s industrial automation systems. We present a configuration system, which grants a reduced obligation of engineering effort. Through self-learning the configuration system can adapt to various tasks by actively learning about its environment. We validate our configuration system using a robotic perception system, specifically a picking application. Perception systems for robotic applications become increasingly essential in industrial environments. Today, such systems often require tedious configuration and design from a well trained technician. These processes have to be carried out for each application and each change in the environment. Our robotic perception system is evaluated on the BOP benchmark and consists of two elements. First, we design building blocks, which are algorithms and datasets available for our configuration algorithm. Second, we implement agents (configuration algorithms) which are designed to intelligently interact with our building blocks. On an examplary industrial robotic picking problem we show, that our autonomous engineering system can reduce engineering efforts.

Paper Citation

in Harvard Style

Antensteiner D., Dietrich V. and Fiegert M. (2021). The Furtherance of Autonomous Engineering via Reinforcement Learning. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 49-59. DOI: 10.5220/0010544200490059

in Bibtex Style

@conference{icinco21,
author={Doris Antensteiner and Vincent Dietrich and Michael Fiegert},
title={The Furtherance of Autonomous Engineering via Reinforcement Learning},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={49-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010544200490059},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - The Furtherance of Autonomous Engineering via Reinforcement Learning
SN - 978-989-758-522-7
AU - Antensteiner D.
AU - Dietrich V.
AU - Fiegert M.
PY - 2021
SP - 49
EP - 59
DO - 10.5220/0010544200490059