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Autonomous Motion Empirical Inference Probabilistic Numerics Intelligent Control Systems Conference Paper 2015

Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

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Intelligent Control Systems
Sebastian Trimpe
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Probabilistic Numerics, Empirical Inference
Philipp Hennig
Affiliated Researcher
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Autonomous Motion
Jeannette Bohg
  • Affiliated Researcher
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Autonomous Motion
Stefan Schaal
  • Director
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Intelligent Control Systems
Alonso Marco Valle
Posterior

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

Author(s): Alonso Marco and Philipp Hennig and Jeannette Bohg and Stefan Schaal and Sebastian Trimpe
Book Title: Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS)
Pages:
Year: 2015
Month: October
Day: 2
Publisher:
Project(s):
Bibtex Type: Conference Paper (conference)
Event Name: Machine Learning in Planning and Control of Robot Motion Workshop
Event Place: Hamburg, Germany
State: Published
Electronic Archiving: grant_archive
Attachments:

BibTex 

@conference{marcoMLPC15,
  title = {Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results},
  booktitle = {Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS)},
  abstract = {This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.},
  month = oct,
  year = {2015},
  slug = {marcomlpc15},
  author = {Marco, Alonso and Hennig, Philipp and Bohg, Jeannette and Schaal, Stefan and Trimpe, Sebastian},
  pages = { },
  publisher = { },
  month_numeric = {10}
}