One-Shot Imitation Learning

Understand

Imitation learning has been commonly applied to solve different tasks in isolation.

• This usually requires either careful feature engineering, or a significant number of samples.
• This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering.
• In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning.