F-SIOL-310 is a robotic dataset and benchmark for Few-Shot Incremental Object Learning, which is used to test incremental learning capabilities for robotic vision from a few examples.

A robot was used to actively capture household objects on a table. The dataset is specifically designed for FSIL with only a small set of training images and a larger set of test images per object category captured by the robot using its own camera and it considers various other robot vision challenges as well, such as different object sizes, object transparency and a clear distinction between objects in the train and test sets. It contains images of 310 objects from 22 categories.