Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Aware Learning from Demonstrations in Multiple Contexts
  using Bayesian Neural Networks   Diversity of environments is a key challenge that causes learned robotic
controllers to fail due to the discrepancies between the training and
evaluation conditions. Training from demonstrations in various conditions can
mitigate---but not completely prevent---such failures. Learned controllers such
as neural networks typically do not have a notion of uncertainty that allows to
diagnose an offset between training and testing conditions, and potentially
intervene. In this work, we propose to use Bayesian Neural Networks, which have
such a notion of uncertainty. We show that uncertainty can be leveraged to
consistently detect situations in high-dimensional simulated and real robotic
domains in which the performance of the learned controller would be sub-par.
Also, we show that such an uncertainty based solution allows making an informed
decision about when to invoke a fallback strategy. One fallback strategy is to
request more data. We empirically show that providing data only when requested
results in increased data-efficiency.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom