Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
An Dinh Vuong Minh Nhat Vu Hieu Le Baoru Huang Binh Huynh Thieu Vo Andreas Kugi Anh Nguyen
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.