30 Jul 2022
What is the Role Of Data Science In Robotics | Ekeeda
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Artificial Intelligence and Machine Learning are the major pillar stones of Data Science. It’s the key element to make robots perform various actions. AI and ML do the learning mechanism for robots and computers that make them understand to make decisions freely just like how humans do. Machine Learning let robots recognize patterns, and behaviour in different scenarios & situations. For instance, Face Recognition or Biometrics are some of the best examples of what Data Science And Machine Learning can give to the Robotics Industry.
In the Recognition patterns, the ML mechanism will input a thousand ways of image representation that will help the robot to detect and scan a human face. It will include many ways to train the computer and robot in which a human would be visible and seen so that it can search the data whenever there is any face input given for detecting and then provide the output accordingly.
Along with it, the Deep Learning field of Data Science has also gained importance. It is also emerging as the core feature in Robotics making computational algorithms for Robots & Computer Machines. These algorithms assist the data scientists & data programmers to create powerful action modules for the robots who will sensibly detect the situations & perform in the best possible way for it.
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The latest branch of ML, as known as the Reinforcement form of Learning is the most effective technique to make the machine learn in the way how humans learn and adapt to things. Under this mechanism, the learning will be done by setting the rewards and patterns of human behaviour and then the machine will learn these patterns by trying and committing mistakes to improve in the row. The result will be generated for a machine or robot in the process just like we humans come up with certain results. It means the rewards are given to the robot each time it learns the behaviour in the right way. And, on every mistake, a penalty gets raised to it so that it will remember the formation and mistake so as to correct it for the next time.
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