Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAFARI: Safe and Active Robot Imitation Learning with Imagination   One of the main issues in Imitation Learning is the erroneous behavior of an
agent when facing out-of-distribution situations, not covered by the set of
demonstrations given by the expert. In this work, we tackle this problem by
introducing a novel active learning and control algorithm, SAFARI. During
training, it allows an agent to request further human demonstrations when these
out-of-distribution situations are met. At deployment, it combines model-free
acting using behavioural cloning with model-based planning to reduce
state-distribution shift, using future state reconstruction as a test for state
familiarity. We empirically demonstrate how this method increases the
performance on a set of manipulation tasks with respect to passive Imitation
Learning, by gathering more informative demonstrations and by minimizing
state-distribution shift at test time. We also show how this method enables the
agent to autonomously predict failure rapidly and safely.

3
The categories of this paper are: physics
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th