  We present DOME, a novel method for one-shot imitation learning, where a task
can be learned from just a single demonstration and then be deployed
immediately, without any further data collection or training. DOME does not
require prior task or object knowledge, and can perform the task in novel
object configurations and with distractors. At its core, DOME uses an
image-conditioned object segmentation network followed by a learned visual
servoing network, to move the robot's end-effector to the same relative pose to
the object as during the demonstration, after which the task can be completed
by replaying the demonstration's end-effector velocities. We show that DOME
achieves near 100% success rate on 7 real-world everyday tasks, and we perform
several studies to thoroughly understand each individual component of DOME.
Videos and supplementary material are available at:
https://www.robot-learning.uk/dome .
