  In order for a robot to be a generalist that can perform a wide range of
jobs, it must be able to acquire a wide variety of skills quickly and
efficiently in complex unstructured environments. High-capacity models such as
deep neural networks can enable a robot to represent complex skills, but
learning each skill from scratch then becomes infeasible. In this work, we
present a meta-imitation learning method that enables a robot to learn how to
learn more efficiently, allowing it to acquire new skills from just a single
demonstration. Unlike prior methods for one-shot imitation, our method can
scale to raw pixel inputs and requires data from significantly fewer prior
tasks for effective learning of new skills. Our experiments on both simulated
and real robot platforms demonstrate the ability to learn new tasks,
end-to-end, from a single visual demonstration.
