Robot learning
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Robot learning refers to the set of methods and frameworks that enable robotic systems to autonomously acquire, refine, and generalize skills through experience rather than relying solely on hand-coded instructions. It encompasses several interconnected paradigms: reinforcement learning, where robots improve behavior by maximizing cumulative rewards through trial-and-error interaction with an environment; learning from demonstration, where robots observe and imitate human or expert performance to acquire new motor skills; and continual or lifelong learning, where robots incrementally build on prior knowledge without forgetting previously learned tasks. These approaches are complemented by techniques such as Gaussian process-based data-efficient learning, active learning, and deep imitation learning from teleoperation. Robot learning matters because manually engineering behaviors for complex, unstructured real-world environments is impractical — robots must adapt to variability in tasks, objects, and human collaborators. By enabling machines to learn from data and interaction, robot learning accelerates deployment in manufacturing, healthcare, and service domains while making systems more flexible, robust, and capable of improving over time.
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