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Applications of Deep Learning in Robotics
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Author(s): Pranav Katte (Vellore Institute of Technology, India), Pranav Arage (Vellore Institute of Technology, India), Satvik Nadkarni (Vellore Institute of Technology, India)and Ramani Selvanambi (Vellore Institute of Technology, India)
Copyright: 2023
Pages: 17
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch010
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Abstract
Deep artificial neural network applications to robotic systems have seen a surge of study due to advancements in deep learning over the past 10 years. The ability of robots to explain the descriptions of their decisions and beliefs leads to a collaboration with the human race. The intensity of the challenges increases as robotics moves from lab to the real-world scenario. Existing robotic control algorithms find it extremely difficult to master the wide variety seen in real-world contexts. The robots have now been developed and advanced to such an extent that they can be useful in our day-to-day lives. All this has been possible because of improvisation of the algorithmic techniques and enhanced computation powers. The majority of traditional machine learning techniques call for parameterized models and functions that must be manually created, making them unsuitable for many robotic jobs. The pattern recognition paradigm may be switched from the combined learning of statistical representations, labelled classifiers, to the joint learning of manmade features and analytical classifiers.
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