Interactive Imitation Learning in Robotics: A Survey
This survey reviews Interactive Imitation Learning (IIL), which involves robots learning by receiving intermittent human feedback during execution. It contrasts IIL with other imitation learning methods by focusing on data efficiency and robustness against distribution mismatch. The paper details how feedback types, learning models, and auxiliary task features are used to guide robot behavior effectively.