Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Manipulation States and Actions for Efficient Non-prehensile
  Rearrangement Planning   This paper addresses non-prehensile rearrangement planning problems where a
robot is tasked to rearrange objects among obstacles on a planar surface. We
present an efficient planning algorithm that is designed to impose few
assumptions on the robot's non-prehensile manipulation abilities and is simple
to adapt to different robot embodiments. For this, we combine sampling-based
motion planning with reinforcement learning and generative modeling. Our
algorithm explores the composite configuration space of objects and robot as a
search over robot actions, forward simulated in a physics model. This search is
guided by a generative model that provides robot states from which an object
can be transported towards a desired state, and a learned policy that provides
corresponding robot actions. As an efficient generative model, we apply
Generative Adversarial Networks. We implement and evaluate our approach for
robots endowed with configuration spaces in SE(2). We demonstrate empirically
the efficacy of our algorithm design choices and observe more than 2x speedup
in planning time on various test scenarios compared to a state-of-the-art
approach.

30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat