Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Directed Locomotion in Modular Robots with Evolvable
  Morphologies   We generalize the well-studied problem of gait learning in modular robots in
two dimensions. Firstly, we address locomotion in a given target direction that
goes beyond learning a typical undirected gait. Secondly, rather than studying
one fixed robot morphology we consider a test suite of different modular
robots. This study is based on our interest in evolutionary robot systems where
both morphologies and controllers evolve. In such a system, newborn robots have
to learn to control their own body that is a random combination of the bodies
of the parents. We apply and compare two learning algorithms, Bayesian
optimization and HyperNEAT. The results of the experiments in simulation show
that both methods successfully learn good controllers, but Bayesian
optimization is more effective and efficient. We validate the best learned
controllers by constructing three robots from the test suite in the real world
and observe their fitness and actual trajectories. The obtained results
indicate a reality gap that depends on the controllers and the shape of the
robots, but overall the trajectories are adequate and follow the target
directions successfully.

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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: physics