تقرير
SceneScore: Learning a Cost Function for Object Arrangement
العنوان: | SceneScore: Learning a Cost Function for Object Arrangement |
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المؤلفون: | Kapelyukh, Ivan, 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: | Arranging objects correctly is a key capability for robots which unlocks a wide range of useful tasks. A prerequisite for creating successful arrangements is the ability to evaluate the desirability of a given arrangement. Our method "SceneScore" learns a cost function for arrangements, such that desirable, human-like arrangements have a low cost. We learn the distribution of training arrangements offline using an energy-based model, solely from example images without requiring environment interaction or human supervision. Our model is represented by a graph neural network which learns object-object relations, using graphs constructed from images. Experiments demonstrate that the learned cost function can be used to predict poses for missing objects, generalise to novel objects using semantic features, and can be composed with other cost functions to satisfy constraints at inference time. Comment: Presented at CoRL 2023 LEAP Workshop. Webpage: https://sites.google.com/view/scenescore |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2311.08530 |
رقم الأكسشن: | edsarx.2311.08530 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |