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
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/2
Y1 - 2016/3/2
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 - Automation
KW - Machine learning algorithms
KW - Robot control
UR - http://www.scopus.com/inward/record.url?scp=84969716644&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969716644&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2015.98
DO - 10.1109/ICMLA.2015.98
M3 - Conference contribution
AN - SCOPUS:84969716644
T3 - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
SP - 168
EP - 175
BT - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Y2 - 9 December 2015 through 11 December 2015
ER -