5,649 research outputs found
TMB: Automatic Differentiation and Laplace Approximation
TMB is an open source R package that enables quick implementation of complex
nonlinear random effect (latent variable) models in a manner similar to the
established AD Model Builder package (ADMB, admb-project.org). In addition, it
offers easy access to parallel computations. The user defines the joint
likelihood for the data and the random effects as a C++ template function,
while all the other operations are done in R; e.g., reading in the data. The
package evaluates and maximizes the Laplace approximation of the marginal
likelihood where the random effects are automatically integrated out. This
approximation, and its derivatives, are obtained using automatic
differentiation (up to order three) of the joint likelihood. The computations
are designed to be fast for problems with many random effects (~10^6) and
parameters (~10^3). Computation times using ADMB and TMB are compared on a
suite of examples ranging from simple models to large spatial models where the
random effects are a Gaussian random field. Speedups ranging from 1.5 to about
100 are obtained with increasing gains for large problems. The package and
examples are available at http://tmb-project.org
Automatic LQR Tuning Based on Gaussian Process Global Optimization
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. Results of a two- and four-dimensional
tuning problems highlight the method's potential for automatic controller
tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference
on Robotics and Automation. Video demonstration of the experiments available
at https://am.is.tuebingen.mpg.de/publications/marco_icra_201
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