| Title: |
Grasp-Anything: Large-scale Grasp Dataset from Foundation Models |
| Authors: |
Vuong, AD; Vu, MN; Le, H; Huang, B; Binh, HTT; Vo, T; Kugi, A; Nguyen, A |
| Publisher Information: |
IEEE |
| Publication Year: |
2024 |
| Collection: |
The University of Liverpool Repository |
| Description: |
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://airvlab.github.io/grasp-anything/. |
| Document Type: |
conference object |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://livrepository.liverpool.ac.uk/3188802/1/2309.09818v1.pdf; Vuong, AD, Vu, MN, Le, H, Huang, B, Binh, HTT, Vo, T, Kugi, A and Nguyen, A orcid:0000-0002-1449-211X (2024) Grasp-Anything: Large-scale Grasp Dataset from Foundation Models In: 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024-5-13 - 2024-5-17. |
| DOI: |
10.1109/icra57147.2024.10611277 |
| Availability: |
https://livrepository.liverpool.ac.uk/3188802/; https://doi.org/10.1109/icra57147.2024.10611277; https://livrepository.liverpool.ac.uk/3188802/1/2309.09818v1.pdf |
| Rights: |
cc_by_4 |
| Accession Number: |
edsbas.833D3064 |
| Database: |
BASE |