Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentiable Algorithm Networks for Composable Robot Learning   This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.

11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph