Deep Learning in Robotics: A Review of Recent Research
H. Pierson, and M. Gashler. (2017)cite arxiv:1707.07217Comment: 41 pages, 135 references.
Abstract
Advances in deep learning over the last decade have led to a flurry of
research in the application of deep artificial neural networks to robotic
systems, with at least thirty papers published on the subject between 2014 and
the present. This review discusses the applications, benefits, and limitations
of deep learning vis-à-vis physical robotic systems, using contemporary
research as exemplars. It is intended to communicate recent advances to the
wider robotics community and inspire additional interest in and application of
deep learning in robotics.
Description
[1707.07217] Deep Learning in Robotics: A Review of Recent Research
%0 Generic
%1 pierson2017learning
%A Pierson, Harry A.
%A Gashler, Michael S.
%D 2017
%K 2017 arxiv deep-learning paper research robotics
%T Deep Learning in Robotics: A Review of Recent Research
%U http://arxiv.org/abs/1707.07217
%X Advances in deep learning over the last decade have led to a flurry of
research in the application of deep artificial neural networks to robotic
systems, with at least thirty papers published on the subject between 2014 and
the present. This review discusses the applications, benefits, and limitations
of deep learning vis-à-vis physical robotic systems, using contemporary
research as exemplars. It is intended to communicate recent advances to the
wider robotics community and inspire additional interest in and application of
deep learning in robotics.
@misc{pierson2017learning,
abstract = {Advances in deep learning over the last decade have led to a flurry of
research in the application of deep artificial neural networks to robotic
systems, with at least thirty papers published on the subject between 2014 and
the present. This review discusses the applications, benefits, and limitations
of deep learning vis-\`a-vis physical robotic systems, using contemporary
research as exemplars. It is intended to communicate recent advances to the
wider robotics community and inspire additional interest in and application of
deep learning in robotics.},
added-at = {2017-12-19T10:40:39.000+0100},
author = {Pierson, Harry A. and Gashler, Michael S.},
biburl = {https://www.bibsonomy.org/bibtex/228cb934d1ba2072d33150514f90081af/achakraborty},
description = {[1707.07217] Deep Learning in Robotics: A Review of Recent Research},
interhash = {cce71ea74307f0c486685bb556ad0f67},
intrahash = {28cb934d1ba2072d33150514f90081af},
keywords = {2017 arxiv deep-learning paper research robotics},
note = {cite arxiv:1707.07217Comment: 41 pages, 135 references},
timestamp = {2017-12-19T10:40:39.000+0100},
title = {Deep Learning in Robotics: A Review of Recent Research},
url = {http://arxiv.org/abs/1707.07217},
year = 2017
}