  We introduce a simple new method for visual imitation learning, which allows
a novel robot manipulation task to be learned from a single human
demonstration, without requiring any prior knowledge of the object being
interacted with. Our method models imitation learning as a state estimation
problem, with the state defined as the end-effector's pose at the point where
object interaction begins, as observed from the demonstration. By then
modelling a manipulation task as a coarse, approach trajectory followed by a
fine, interaction trajectory, this state estimator can be trained in a
self-supervised manner, by automatically moving the end-effector's camera
around the object. At test time, the end-effector moves to the estimated state
through a linear path, at which point the original demonstration's end-effector
velocities are simply replayed. This enables convenient acquisition of a
complex interaction trajectory, without actually needing to explicitly learn a
policy. Real-world experiments on 8 everyday tasks show that our method can
learn a diverse range of skills from a single human demonstration, whilst also
yielding a stable and interpretable controller.
