Reviews: One-Shot Imitation Learning

Summary --- Complex and useful robotic manipulation tasks are difficult because of the difficulty of manipulation itself, but also because it's difficult to communicate the intent of a task. Both of these problems can be alleviated through the use of imitation learning, but in order for this to be practical the learner must be able to generalize from few examples. This paper presents an architecture inspired by recent work in meta learning which generalizes manipulation of a robot arm from a single task demonstration; i.e., it does one-shot imitation learning. The network is something like a seq2seq model that uses multiple attention mechanisms in the style of "Neural Machine Translation by Jointly Learning to Align and Translate". There is a demonstration network, a context network and a manipulation network.