MACHINE LEARNING IN ROBOTICS: ADAPTIVE CONTROL AND AUTONOMY
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Abstract
This article explores the application of machine learning techniques in robotics to enable adaptive control and enhance robot autonomy. The study analyzes reinforcement learning algorithms, neural networks, and their role in autonomous navigation and decision-making. Examples of successful integration of machine learning in industrial and service robots are presented, along with a discussion on future developments in this field.
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References
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