Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep
  Model-Based RL for Sparse Cost Robotic Tasks   Reinforcement learning (RL) for robotics is challenging due to the difficulty
in hand-engineering a dense cost function, which can lead to unintended
behavior, and dynamical uncertainty, which makes exploration and constraint
satisfaction challenging. We address these issues with a new model-based
reinforcement learning algorithm, Safety Augmented Value Estimation from
Demonstrations (SAVED), which uses supervision that only identifies task
completion and a modest set of suboptimal demonstrations to constrain
exploration and learn efficiently while handling complex constraints. We then
compare SAVED with 3 state-of-the-art model-based and model-free RL algorithms
on 6 standard simulation benchmarks involving navigation and manipulation and a
physical knot-tying task on the da Vinci surgical robot. Results suggest that
SAVED outperforms prior methods in terms of success rate, constraint
satisfaction, and sample efficiency, making it feasible to safely learn a
control policy directly on a real robot in less than an hour. For tasks on the
robot, baselines succeed less than 5% of the time while SAVED has a success
rate of over 75% in the first 50 training iterations. Code and supplementary
material is available at https://tinyurl.com/saved-rl.

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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th