Robot Skill Learning
Overview
Robot skill learning asks how machines can acquire reusable behaviors—such as grasping, insertion, locomotion, and tool use—and apply them in new situations. Common approaches include imitation learning from robot or human demonstrations, reinforcement learning in simulation and on hardware, and learned motion primitives or diffusion and vision-language-action policies. Researchers organize behaviors into skill libraries, hierarchies, or programs so robots can compose them for longer tasks, while using tactile feedback, language, and human corrections to adapt execution. A central challenge is making skills transfer reliably across objects, scenes, and robot bodies without losing earlier abilities; success on individual skills still does not guarantee robust long-horizon composition.
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