تقرير
One-Shot Imitation Learning: A Pose Estimation Perspective
| العنوان: | One-Shot Imitation Learning: A Pose Estimation Perspective |
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| المؤلفون: | Vitiello, Pietro, Dreczkowski, Kamil, Johns, Edward |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Published at the 7th Conference on Robot Learning (CoRL 2023). For more details please visit https://www.robot-learning.uk/pose-estimation-perspective |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.12077 |
| رقم الأكسشن: | edsarx.2310.12077 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |