Machine Learning in Robotics

Machine learning (ML) is a core component of AI in robotics, enabling machines to improve their performance through experience. Instead of following pre-programmed instructions, robots equipped with ML algorithms analyze data, detect patterns, and refine their actions over time. There are three primary types of machine learning used in robotics:

Supervised Learning – Robots are trained with labeled datasets, learning from examples provided by humans. This approach is common in robotic vision, where robots recognize objects based on prior knowledge.

Unsupervised Learning – Robots analyze large sets of data without predefined labels, identifying patterns independently. This method is useful for anomaly detection in industrial robots.

Reinforcement Learning – Robots learn through trial and error, receiving rewards for optimal actions and penalties for mistakes. This is commonly used in autonomous navigation and robotic grasping tasks.

Machine learning enables robots to adapt to dynamic environments, making them more efficient in complex tasks such as self-driving cars, robotic assistants, and industrial automation.