Apr 14, 2022 · In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal ...
Jun 7, 2023 · In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal ...
In this work, we present a method to meta-learn an adaptive controller offline from previously collected data. Our meta-learning is control- oriented rather ...
Our meta-learning is control- oriented rather than regression-oriented; specifically, we: 1) collect input- output data on the true system, 2) train a ...
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems. Author(s). Spencer M. Richards. Navid Azizan. Jean-Jacques E. Slotine. Marco Pavone.
The control-oriented meta-learning algorithm aims to solve this problem by learning a controller that can adapt to dynamic environments. This algorithm has ...
Training data, trained parameters, and test results are all conveniently saved in this repository, since it can take a while to re-generate them.
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Our meta-learning is control- oriented rather than regression-oriented; specifically, we: 1) collect input- output data on the true system, 2) train a ...
In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model ...
In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model ...