One-Shot Imitation Learning: A Pose Estimation PerspectiveDownload PDF

Published: 30 Aug 2023, Last Modified: 16 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: One-Shot Imitation Learning, Unseen Object Pose Estimation, Robot Manipulation
TL;DR: We study one-shot imitation learning from the perspective of unseen object pose estimation, providing valuable insights as well as showing this framework's potential in real world robotics tasks.
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
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