Machine learning
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Machine learning (ML) is a branch of artificial intelligence in which systems automatically learn patterns and improve their performance from data, without being explicitly programmed for every task. Drawing on statistical learning theory, probabilistic models, and optimization techniques, ML algorithms build representations of the world by exposure to examples rather than hand-crafted rules. In robotics and AI, ML underpins a vast range of capabilities: deep neural networks enable visual perception and speech recognition, reinforcement learning allows robots to acquire complex behaviors through trial and error, transfer learning accelerates adaptation to new tasks, and recurrent or temporal convolutional networks process sequential sensor data. Scalable frameworks like TensorFlow make it practical to train and deploy these models on real hardware across distributed systems. ML matters because it allows robots and autonomous agents to handle the variability and complexity of real-world environments that rule-based programming cannot feasibly address, enabling advances in autonomous navigation, manipulation, human-robot interaction, and beyond.
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