The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources, or need fast adaptation to dynamically changing environments. In this article, we focus on the instance segmentation task and provide a comprehensive study of different techniques that allow adapting an object segmentation model in the presence of novel objects or different domains. We propose a pipeline for fast instance segmentation learning designed for robotic applications where data come in stream. It is based on an hybrid method leveraging on a pre-trained convolutional neural network for feature extraction and fast-to-train Kernel-based classifiers. We also propose a training protocol that allows to shorten the training time by performing feature extraction during the data acquisition. We benchmark the proposed pipeline on two robotics datasets and we deploy it on a real robot, i.e., the iCub humanoid. To this aim, we adapt our method to an incremental setting in which novel objects are learned online by the robot. The code to reproduce the experiments is publicly available on GitHub.