The ability of a robot to pick an object, known as robot grasping, is crucial
for several applications, such as assembly or sorting. In such tasks, selecting
the right target to pick is as essential as inferring a correct configuration
of the gripper. A common solution to this problem relies on semantic
segmentation models, which often show poor generalization to unseen objects and
require considerable time and massive data to be trained. To reduce the need
for large datasets, some grasping pipelines exploit few-shot semantic
segmentation models, which are capable of recognizing new classes given a few
examples. However, this often comes at the cost of limited performance and
fine-tuning is required to be effective in robot grasping scenarios. In this
work, we propose to overcome all these limitations by combining the impressive
generalization capability reached by foundation models with a high-performing
few-shot classifier, working as a score function to select the segmentation
that is closer to the support set. The proposed model is designed to be
embedded in a grasp synthesis pipeline. The extensive experiments using one or
five examples show that our novel approach overcomes existing performance
limitations, improving the state of the art both in few-shot semantic
segmentation on the Graspnet-1B (+10.5% mIoU) and Ocid-grasp (+1.6% AP)
datasets, and real-world few-shot grasp synthesis (+21.7% grasp accuracy). The
project page is available at:
this https URL

我们提出了一个新颖的方法，结合了基础模型的出色广化能力和高性能的少样本分类器，作为一个评分函数来选择与支持集更接近的分割，从而克服了现有性能限制，并在 Graspnet-1B 和 Ocid-grasp 数据集以及真实世界的少样本抓取合成中取得了最先进的效果。