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.
@article{arxiv.2310.12077,
title = {One-Shot Imitation Learning: A Pose Estimation Perspective},
author = {Pietro Vitiello and Kamil Dreczkowski and Edward Johns},
journal= {arXiv preprint arXiv:2310.12077},
year = {2023}
}
Comments
Published at the 7th Conference on Robot Learning (CoRL 2023). For more details please visit https://www.robot-learning.uk/pose-estimation-perspective