Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Robot Skills with Temporal Variational Inference   In this paper, we address the discovery of robotic options from
demonstrations in an unsupervised manner. Specifically, we present a framework
to jointly learn low-level control policies and higher-level policies of how to
use them from demonstrations of a robot performing various tasks. By
representing options as continuous latent variables, we frame the problem of
learning these options as latent variable inference. We then present a temporal
formulation of variational inference based on a temporal factorization of
trajectory likelihoods,that allows us to infer options in an unsupervised
manner. We demonstrate the ability of our framework to learn such options
across three robotic demonstration datasets.

36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin