Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of IEEE ICMLA'15 |
| Number of pages | 8 |
| Publisher | IEEE |
| Publication date | 2015 |
| Publication status | Published - 2015 |
| Event | 14th International Conference on Machine Learning and Applications - Miami, Florida, United States Duration: 9 Dec 2015 → 11 Dec 2015 |
Conference
| Conference | 14th International Conference on Machine Learning and Applications |
|---|---|
| Country | United States |
| City | Miami, Florida |
| Period | 09/12/2015 → 11/12/2015 |
Keywords
- Robot control
- Automation
- Machine learning algorithms
Cite this
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Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning. / Andersen, Thomas Timm; Amor, Heni Ben; Andersen, Nils Axel; Ravn, Ole.
Proceedings of IEEE ICMLA'15. IEEE, 2015.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning.
AU - Andersen, Thomas Timm
AU - Amor, Heni Ben
AU - Andersen, Nils Axel
AU - Ravn, Ole
PY - 2015
Y1 - 2015
N2 - Latencies and delays play an important role in temporally precise robot control. During dynamic tasks in particular, a robot has to account for inherent delays to reach manipulated objects in time. The different types of occurring delays are typically convoluted and thereby hard to measure and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well as the uncertainty of the prediction. Experiments on two widely used robot platforms show significant actuation and response delays in standard control loops. Predictive models can, therefore, be used to reason about expected delays and improve temporal accuracy during control. The approach can easily be used on different robot platforms.
AB - Latencies and delays play an important role in temporally precise robot control. During dynamic tasks in particular, a robot has to account for inherent delays to reach manipulated objects in time. The different types of occurring delays are typically convoluted and thereby hard to measure and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well as the uncertainty of the prediction. Experiments on two widely used robot platforms show significant actuation and response delays in standard control loops. Predictive models can, therefore, be used to reason about expected delays and improve temporal accuracy during control. The approach can easily be used on different robot platforms.
KW - Robot control
KW - Automation
KW - Machine learning algorithms
M3 - Article in proceedings
BT - Proceedings of IEEE ICMLA'15
PB - IEEE
ER -