Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Configuration Space Decomposition for Scalable Proxy Collision Checking
  in Robot Planning and Control   Real-time robot motion planning in complex high-dimensional environments
remains an open problem. Motion planning algorithms, and their underlying
collision checkers, are crucial to any robot control stack. Collision checking
takes up a large portion of the computational time in robot motion planning.
Existing collision checkers make trade-offs between speed and accuracy and
scale poorly to high-dimensional, complex environments. We present a novel
space decomposition method using K-Means clustering in the Forward Kinematics
space to accelerate proxy collision checking. We train individual configuration
space models using Fastron, a kernel perceptron algorithm, on these decomposed
subspaces, yielding compact yet highly accurate models that can be queried
rapidly and scale better to more complex environments. We demonstrate this new
method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter
robot producing on average 29x faster collision checks and up to 9.8x faster
motion planning compared to state-of-the-art geometric collision checkers.

20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc