Few-Shot Imitation Learning

Few-Shot Imitation Learning is a machine learning approach aimed at enabling models to quickly learn to perform new tasks through a small number of demonstration samples. Its core objective is to improve learning efficiency, reduce the reliance on large amounts of labeled data, and achieve rapid adaptation and generalization. This technology has significant application value in fields such as robotics control and natural language processing, significantly enhancing the flexibility and responsiveness of systems.