Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 962-969 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 978-1-7281-1699-0 |
| ISBN (Print) | 978-1-7281-1698-3, 978-1-7281-1700-3 |
| Publikationsstatus | Veröffentlicht - Aug. 2019 |
| Veranstaltung | 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019 - Tianjin, China Dauer: 4 Aug. 2019 → 7 Aug. 2019 |
Publikationsreihe
| Name | Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019 |
|---|---|
| ISSN (Print) | 2152-7431 |
| ISSN (elektronisch) | 2152-744X |
Abstract
The present paper deals with an online approach to learn the inverse dynamics of any robot. This is realized by the use of Gaussian Processes drifting parallel along the system data. An extension by a database enables the efficient use of data points from the past. The central component of this work is the implementation of such a method in a controller in order to achieve the actual goal: the feedforward control of an industrial robot by means of machine learning. This is done by splitting the procedure into two threads running parallel so that the prediction is decoupled from the computing-intensive training of the models. Experiments show that the method reduces the tracking errors more clearly than an elaborately identified rigid body model including friction. For a defined trajectory, the squared areas of the tracking errors of all axes are reduced by more than 54% compared to motion without pre-control. In addition, a highly dynamic pick-and-place experiment is used to investigate the possible changes in system dynamics. Compared to an offline trained model, the approximation error of the proposed online approach is smaller for the remaining time of the experiment after an initial phase. Furthermore, this error is smaller throughout the experiment for online learning with parallel drifting Gaussian Processes than when using a single one.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Signalverarbeitung
- Ingenieurwesen (insg.)
- Maschinenbau
- Mathematik (insg.)
- Steuerung und Optimierung
- Informatik (insg.)
- Artificial intelligence
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Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. S. 962-969 8816298 (Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Online Learning of the Inverse Dynamics with Parallel Drifting Gaussian Processes
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
AU - Habich, Tim-Lukas
AU - Kaczor, Daniel
AU - Tappe, Svenja
AU - Ortmaier, Tobias
PY - 2019/8
Y1 - 2019/8
N2 - The present paper deals with an online approach to learn the inverse dynamics of any robot. This is realized by the use of Gaussian Processes drifting parallel along the system data. An extension by a database enables the efficient use of data points from the past. The central component of this work is the implementation of such a method in a controller in order to achieve the actual goal: the feedforward control of an industrial robot by means of machine learning. This is done by splitting the procedure into two threads running parallel so that the prediction is decoupled from the computing-intensive training of the models. Experiments show that the method reduces the tracking errors more clearly than an elaborately identified rigid body model including friction. For a defined trajectory, the squared areas of the tracking errors of all axes are reduced by more than 54% compared to motion without pre-control. In addition, a highly dynamic pick-and-place experiment is used to investigate the possible changes in system dynamics. Compared to an offline trained model, the approximation error of the proposed online approach is smaller for the remaining time of the experiment after an initial phase. Furthermore, this error is smaller throughout the experiment for online learning with parallel drifting Gaussian Processes than when using a single one.
AB - The present paper deals with an online approach to learn the inverse dynamics of any robot. This is realized by the use of Gaussian Processes drifting parallel along the system data. An extension by a database enables the efficient use of data points from the past. The central component of this work is the implementation of such a method in a controller in order to achieve the actual goal: the feedforward control of an industrial robot by means of machine learning. This is done by splitting the procedure into two threads running parallel so that the prediction is decoupled from the computing-intensive training of the models. Experiments show that the method reduces the tracking errors more clearly than an elaborately identified rigid body model including friction. For a defined trajectory, the squared areas of the tracking errors of all axes are reduced by more than 54% compared to motion without pre-control. In addition, a highly dynamic pick-and-place experiment is used to investigate the possible changes in system dynamics. Compared to an offline trained model, the approximation error of the proposed online approach is smaller for the remaining time of the experiment after an initial phase. Furthermore, this error is smaller throughout the experiment for online learning with parallel drifting Gaussian Processes than when using a single one.
KW - Feedforward control
KW - Gaussian Process
KW - Implementation
KW - Inverse dynamics
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=85072398591&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816298
DO - 10.1109/ICMA.2019.8816298
M3 - Conference contribution
AN - SCOPUS:85072398591
SN - 978-1-7281-1698-3
SN - 978-1-7281-1700-3
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 962
EP - 969
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2019 through 7 August 2019
ER -