Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Locally Weighted Regression Pseudo-Rehearsal for Online Learning of
  Vehicle Dynamics   We consider the problem of online adaptation of a neural network designed to
represent vehicle dynamics. The neural network model is intended to be used by
an MPC control law to autonomously control the vehicle. This problem is
challenging because both the input and target distributions are non-stationary,
and naive approaches to online adaptation result in catastrophic forgetting,
which can in turn lead to controller failures. We present a novel online
learning method, which combines the pseudo-rehearsal method with locally
weighted projection regression. We demonstrate the effectiveness of the
resulting Locally Weighted Projection Regression Pseudo-Rehearsal (LW-PR$^2$)
method in simulation and on a large real world dataset collected with a 1/5
scale autonomous vehicle.

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The categories of this paper are: cond-mat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
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The categories of this paper are: physics