Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Optimization for Trajectory Planning with Heteroscedastic
  Gaussian Processes   Trajectory optimization methods for motion planning attempt to generate
trajectories that minimize a suitable objective function. Such methods
efficiently find solutions even for high degree-of-freedom robots. However, a
globally optimal solution is often intractable in practice and state-of-the-art
trajectory optimization methods are thus prone to local minima, especially in
cluttered environments. In this paper, we propose a novel motion planning
algorithm that employs stochastic optimization based on the cross-entropy
method in order to tackle the local minima problem. We represent trajectories
as samples from a continuous-time Gaussian process and introduce
heteroscedasticity to generate powerful trajectory priors better suited for
collision avoidance in motion planning problems. Our experimental evaluation
shows that the proposed approach yields a more thorough exploration of the
solution space and a higher success rate in complex environments than a current
Gaussian process based state-of-the-art trajectory optimization method, namely
GPMP2, while having comparable execution time.

27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin