Assessing the Role of Machine Learning in Robotics
Santhosh S1, Karthik J2, Chesmi B R3, Anju Thomas4, Davis Patel5
1Prof Santhosh S, B.E, MTECH in field of digital electronics and communication. Research Work on Embedded Systems and Robotics.
2Karthik J, 4th Year, B.E (Electronics and Communication Engineering) Finalists at eYRC-2018.
3Chesmi B R, 4th Year, B.E (Electronics and Communication Engineering) Finalists at eYRC-2018.
4Anju Thomas, 4th Year, B.E (Electronics and Communication Engineering) Finalists at eYRC-2018.
5Davis Patel, 4th Year, B.E (Electronics and Communication Engineering) Finalists at eYRC-2018.
Manuscript received on March 03, 2020. | Revised Manuscript received on March 12, 2020. | Manuscript published on March 15, 2020. | PP: 13-15 | Volume-6, Issue-5, March 2020. | Retrieval Number: E1202036520/2020©BEIESP | DOI: 10.35940/ijisme.E1202.036520
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Neural framework offers wide support for machine learning algorithms. It is an interface, library or tool which allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. The neural framework is an exceptionally intricate piece of a person that co-ordinate its activities Moreover, tactile data by transmitting signs to and from various pieces of the body. Neural frameworks are applied to perform object gathering and a grasp orchestrating task. Machine Learning techniques have been applied to many sub problems in robot perception – pattern recognition and self-organisation. Modern robot framework which demands a complete detail of each movement of the robot, which breaks the pick-and-spot issue into about free, computationally conceivable sub-issues as a phase toward a comprehensive endeavour level framework.
Keywords: Robot, Machine Learning, Pick and Spot, Artificial Intelligence, Framework.