تقرير
Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
| العنوان: | Grasp-Anything: Large-scale Grasp Dataset from Foundation Models |
|---|---|
| المؤلفون: | Vuong, An Dinh, Vu, Minh Nhat, Le, Hieu, Huang, Baoru, Huynh, Binh, Vo, Thieu, Kugi, Andreas, Nguyen, Anh |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://grasp-anything-2023.github.io. Comment: Project page: https://grasp-anything-2023.github.io |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2309.09818 |
| رقم الأكسشن: | edsarx.2309.09818 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |