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One-Shot Imitation Learning: A Pose Estimation Perspective

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arxiv 2310.12077 v1 pith:B3QCEON4 submitted 2023-10-18 cs.RO cs.CVcs.LG

Pietro Vitiello , Kamil Dreczkowski , Edward Johns This is my paper · ORCID
classification cs.ROcs.CVcs.LG
keywords imitationlearningposeestimationobjectone-shottaskunseen
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A robot policy trained on one real demonstration plus AI-generated 3D views succeeds from novel initial poses, including opposite-side starts, across six real manipulation tasks.

  2. Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A semantic keypoint graph matched to novel objects lets imitation-learned manipulation policies generalize with a quarter of the demonstrations.

  3. DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

    cs.RO 2024-12 conditional novelty 6.0 of 10

    DenseMatcher combines 2D image features with a 3D neural network and functional maps to compute dense semantic correspondences between textured 3D objects, enabling single-demo cross-category robot manipulation.

  4. P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies

    cs.RO 2024-12 conditional novelty 5.0 of 10

    P3-PO feeds robot policies human-prescribed semantic keypoints, propagated by correspondence and tracking, and reports strong generalization gains on real manipulation tasks.

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