Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximately Optimal Continuous-Time Motion Planning and Control via
  Probabilistic Inference   The problem of optimal motion planing and control is fundamental in robotics.
However, this problem is intractable for continuous-time stochastic systems in
general and the solution is difficult to approximate if non-instantaneous
nonlinear performance indices are present. In this work, we provide an
efficient algorithm, PIPC (Probabilistic Inference for Planning and Control),
that yields approximately optimal policies with arbitrary higher-order
nonlinear performance indices. Using probabilistic inference and a Gaussian
process representation of trajectories, PIPC exploits the underlying sparsity
of the problem such that its complexity scales linearly in the number of
nonlinear factors. We demonstrate the capabilities of our algorithm in a
receding horizon setting with multiple systems in simulation.

31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas