  Grasp detection is a persistent and intricate challenge with various
industrial applications. Recently, many methods and datasets have been proposed
to tackle the grasp detection problem. However, most of them do not consider
using natural language as a condition to detect the grasp poses. In this paper,
we introduce Grasp-Anything++, a new language-driven grasp detection dataset
featuring 1M samples, over 3M objects, and upwards of 10M grasping
instructions. We utilize foundation models to create a large-scale scene corpus
with corresponding images and grasp prompts. We approach the language-driven
grasp detection task as a conditional generation problem. Drawing on the
success of diffusion models in generative tasks and given that language plays a
vital role in this task, we propose a new language-driven grasp detection
method based on diffusion models. Our key contribution is the contrastive
training objective, which explicitly contributes to the denoising process to
detect the grasp pose given the language instructions. We illustrate that our
approach is theoretically supportive. The intensive experiments show that our
method outperforms state-of-the-art approaches and allows real-world robotic
grasping. Finally, we demonstrate our large-scale dataset enables zero-short
grasp detection and is a challenging benchmark for future work. Project
website: https://airvlab.github.io/grasp-anything/
