Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneously Learning Vision and Feature-based Control Policies for
  Real-world Ball-in-a-Cup   We present a method for fast training of vision based control policies on
real robots. The key idea behind our method is to perform multi-task
Reinforcement Learning with auxiliary tasks that differ not only in the reward
to be optimized but also in the state-space in which they operate. In
particular, we allow auxiliary task policies to utilize task features that are
available only at training-time. This allows for fast learning of auxiliary
policies, which subsequently generate good data for training the main,
vision-based control policies. This method can be seen as an extension of the
Scheduled Auxiliary Control (SAC-X) framework. We demonstrate the efficacy of
our method by using both a simulated and real-world Ball-in-a-Cup game
controlled by a robot arm. In simulation, our approach leads to significant
learning speed-ups when compared to standard SAC-X. On the real robot we show
that the task can be learned from-scratch, i.e., with no transfer from
simulation and no imitation learning. Videos of our learned policies running on
the real robot can be found at
https://sites.google.com/view/rss-2019-sawyer-bic/.

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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an