Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor
  Skill Acquisition in Robots   The automatic and efficient discovery of skills, without supervision, for
long-living autonomous agents, remains a challenge of Artificial Intelligence.
Intrinsically Motivated Goal Exploration Processes give learning agents a
human-inspired mechanism to sequentially select goals to achieve. This approach
gives a new perspective on the lifelong learning problem, with promising
results on both simulated and real-world experiments. Until recently, those
algorithms were restricted to domains with experimenter-knowledge, since the
Goal Space used by the agents was built on engineered feature extractors. The
recent advances of deep representation learning, enables new ways of designing
those feature extractors, using directly the agent experience. Recent work has
shown the potential of those methods on simple yet challenging simulated
domains. In this paper, we present recent results showing the applicability of
those principles on a real-world robotic setup, where a 6-joint robotic arm
learns to manipulate a ball inside an arena, by choosing goals in a space
learned from its past experience.

21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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