Two-Stage Model-Agnostic Meta-Learning With Noise Mechanism For One-Shot Imitation

Given that humans and animals can learn new behaviors in a short time by observing others, the question we need to consider is how to make robots behave like humans or animals, that is, through effective demonstration, robots can quickly understand and learn a new ability. One possible solution is imitation based meta-learning, but most of the related approaches are limited in a particular network structure or a specific task. Particularly, meta-learning methods based on gradient-update are prone to overfit. In this article, we propose a generic meta-learning algorithm that divides the learning process into two independent stages (skill cloning and skill transfer) with a noise mechanism which is compatible with any model. The skill cloning stage enables a good understanding of the demonstration, which helps the skill transfer stage when the robot applies the learned experience into new tasks. The experimental results show that our algorithm can alleviate the phenomenon of overfitting by introducing a noise mechanism. Our method not only performs well on the regression task but is significantly better than the existing state-of-the-art one-shot imitation learning methods in the same simulation environments (i.e., simulated pushing and simulated reaching).