  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:
https://leobarcellona.github.io/showandgrasp.github.io/
