One-Shot Imitation Learning: A Pose Estimation Perspective

Authors: Pietro Vitiello, Kamil Dreczkowski, and Edward Johns
Institution: The Robot Learning Lab at Imperial College London
Published at: CoRL 2023
Paper: drive.google.com/file/d/1HyB-...
Webpage: www.robot-learning.uk/pose-es...
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 of camera calibration, pose estimation error, and spatial generalisation, on task success rates.