One-Shot Imitation Learning: A Pose Estimation Perspective
Pietro Vitiello, Kamil Dreczkowski, Edward Johns
Abstract
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 www.robot-learning.uk/pose-estimation-perspective.
BibTeX
@inproceedings{
vitiello2023oneshot,
title={One-Shot Imitation Learning: A Pose Estimation Perspective},
author={Pietro Vitiello and Kamil Dreczkowski and Edward Johns},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=w5ONmpgnfG}
}