In the paper, we address the problem of learning from demonstrations when the demonstrator is different (e.g a human) than the agent, or the environment has changed (e.g the real world has obstacles in the way). To achieve this flexibility in imitation, our method, namely selective imitation learning from observations (SILO), learns to pick feasible frames to follow instead of following sequential frames.