Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometry-aware Bayesian Optimization in Robotics using Riemannian
  Mat\'ern Kernels   Bayesian optimization is a data-efficient technique which can be used for
control parameter tuning, parametric policy adaptation, and structure design in
robotics. Many of these problems require optimization of functions defined on
non-Euclidean domains like spheres, rotation groups, or spaces of
positive-definite matrices. To do so, one must place a Gaussian process prior,
or equivalently define a kernel, on the space of interest. Effective kernels
typically reflect the geometry of the spaces they are defined on, but designing
them is generally non-trivial. Recent work on the Riemannian Mat\'ern kernels,
based on stochastic partial differential equations and spectral theory of the
Laplace-Beltrami operator, offers promising avenues towards constructing such
geometry-aware kernels. In this paper, we study techniques for implementing
these kernels on manifolds of interest in robotics, demonstrate their
performance on a set of artificial benchmark functions, and illustrate
geometry-aware Bayesian optimization for a variety of robotic applications,
covering orientation control, manipulability optimization, and motion planning,
while showing its improved performance.

23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph