  Humans and animals are capable of learning a new behavior by observing others
perform the skill just once. We consider the problem of allowing a robot to do
the same -- learning from a raw video pixels of a human, even when there is
substantial domain shift in the perspective, environment, and embodiment
between the robot and the observed human. Prior approaches to this problem have
hand-specified how human and robot actions correspond and often relied on
explicit human pose detection systems. In this work, we present an approach for
one-shot learning from a video of a human by using human and robot
demonstration data from a variety of previous tasks to build up prior knowledge
through meta-learning. Then, combining this prior knowledge and only a single
video demonstration from a human, the robot can perform the task that the human
demonstrated. We show experiments on both a PR2 arm and a Sawyer arm,
demonstrating that after meta-learning, the robot can learn to place, push, and
pick-and-place new objects using just one video of a human performing the
manipulation.
