  We present O2A, a novel method for learning to perform robotic manipulation
tasks from a single (one-shot) third-person demonstration video. To our
knowledge, it is the first time this has been done for a single demonstration.
The key novelty lies in pre-training a feature extractor for creating a
perceptual representation for actions that we call 'action vectors'. The action
vectors are extracted using a 3D-CNN model pre-trained as an action classifier
on a generic action dataset. The distance between the action vectors from the
observed third-person demonstration and trial robot executions is used as a
reward for reinforcement learning of the demonstrated task. We report on
experiments in simulation and on a real robot, with changes in viewpoint of
observation, properties of the objects involved, scene background and
morphology of the manipulator between the demonstration and the learning
domains. O2A outperforms baseline approaches under different domain shifts and
has comparable performance with an oracle (that uses an ideal reward function).
