Machine Learning and Artificial Intelligence Role in Robotics

Mr. Kunal Dewangan

Assistant Professor - Department of Mechanical Engineering Kalinga University, New Raipur

“Robotics are beginning to cross that line from absolutely primitive motion to motion that resembles animal or human behavior.” —J. J. Abrams

Robotics, which was founded in the early 1950s, is now widely recognized as an autonomous machine with accurately taught and learned inputs. Robots were initially built to perform a learned set of repetitive activities. Artificial intelligence was first used in digitally programmed industrial robots in the 2000s. Robotics and machine learning (AI) have been expertly combined in order to expand the specified reach of intelligent robotics.

The robotics applications of AI and ML (machine learning), as well as their impact on our daily lives, are rapidly expanding. Artificial intelligence instructs functions of robots such as spatial relations, motion control, object grasping, computer vision, and so on to help them understand and interact with previously unknown information and circumstances. Four categories can be used to group these functions:

Vision: Robotics may observe and discern patterns that they have never seen before with AI at work. Not only does AI increase identification, but it additionally employs a lot more effort on these patterns’ precision than traditional robotics.

Grasping: AI and ML (machine learning) instruct robots on the most forceful gripping stance for an object.

Motion Control – Controlling locomotive features is crucial for giving a robot a human-like appearance. ML (Machine learning) is advantageous to robots in this area since It removes an obstruction identification and interactive dynamic.

Data is the lifeblood of every project; limited accurate data can ensure its success.

The industrial sector consumes the most robots and other forms of automation. AI gives robots enough computer vision and motion control to understand their surroundings. Machine learning teaches robots to learn from their own errors, removing the need for continual human intervention.


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