TITLE:
Deep Learning for Robotics
AUTHORS:
Radouan Ait Mouha
KEYWORDS:
Deep Learning, Neural Networks, Artificial Intelligence, Robotic Grasping, Motion Planning
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.9 No.2,
April
21,
2021
ABSTRACT: The application of deep learning to robotics over
the past decade has led to a wave of research into deep artificial neural
networks and to a very specific problems and questions that are not usually
addressed by the computer vision and machine learning communities. Robots have
always faced many unique challenges as the robotic platforms move from the lab
to the real world. Minutely, the sheer amount of diversity we encounter in
real-world environments is a huge challenge to deal with today’s robotic
control algorithms and this necessitates the use of machine learning algorithms
that are able to learn the controls of a given data. However, deep learning
algorithms are general non-linear models capable of learning features directly
from data making them an excellent choice for such robotic applications.
Indeed, robotics and artificial intelligence (AI) are increasing and amplifying
human potential, enhancing productivity and moving from simple thinking towards
human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges
of deep learning robots were discussed. The problem addressed was robotic
grasping and tracking motion planning for robots which was the most fundamental
and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of
tracking and motion planning. The system is tested on simulated data and real
experiments with success.