Much like humans, robots should have the ability to leverage knowledge from
previously learned tasks in order to learn new tasks quickly in new and
unfamiliar environments. Despite this, most robot learning approaches have
focused on learning a single task, from scratch, with a limited notion of
generalisation, and no way of leveraging the knowledge to learn other tasks
more efficiently. One possible solution is meta-learning, but many of the
related approaches are limited in their ability to scale to a large number of
tasks and to learn further tasks without forgetting previously learned ones.
With this in mind, we introduce Task-Embedded Control Networks, which employ
ideas from metric learning in order to create a task embedding that can be used
by a robot to learn new tasks from one or more demonstrations. In the area of
visually-guided manipulation, we present simulation results in which we surpass
the performance of a state-of-the-art method when using only visual information
from each demonstration. Additionally, we demonstrate that our approach can
also be used in conjunction with domain randomisation to train our few-shot
learning ability in simulation and then deploy in the real world without any
additional training. Once deployed, the robot can learn new tasks from a single
real-world demonstration.

该论文提出了一种基于测量学习的元学习方法：利用任务嵌入用于学习新的单个、多个实例任务，实现在真实世界中从单个演示中学习新任务的能力。