  In this paper, we study imitation learning under the challenging setting of:
(1) only a single demonstration, (2) no further data collection, and (3) no
prior task or object knowledge. We show how, with these constraints, imitation
learning can be formulated as a combination of trajectory transfer and unseen
object pose estimation. To explore this idea, we provide an in-depth study on
how state-of-the-art unseen object pose estimators perform for one-shot
imitation learning on ten real-world tasks, and we take a deep dive into the
effects that camera calibration, pose estimation error, and spatial
generalisation have on task success rates. For videos, please visit
https://www.robot-learning.uk/pose-estimation-perspective.
