  We cast visual imitation as a visual correspondence problem. Our robotic
agent is rewarded when its actions result in better matching of relative
spatial configurations for corresponding visual entities detected in its
workspace and teacher's demonstration. We build upon recent advances in
Computer Vision,such as human finger keypoint detectors, object detectors
trained on-the-fly with synthetic augmentations, and point detectors supervised
by viewpoint changes and learn multiple visual entity detectors for each
demonstration without human annotations or robot interactions. We empirically
show the proposed factorized visual representations of entities and their
spatial arrangements drive successful imitation of a variety of manipulation
skills within minutes, using a single demonstration and without any environment
instrumentation. It is robust to background clutter and can effectively
generalize across environment variations between demonstrator and imitator,
greatly outperforming unstructured non-factorized full-frame CNN encodings of
previous works.
