Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CASSL: Curriculum Accelerated Self-Supervised Learning   Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects.

18
The categories of this paper are: econ
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an