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A number of successful Computer Vision applications have been recently proposed based on Convolutional Networks. However, in most of the cases the system is fully supervised, the training set is fixed and the task completely defined a priori. Even though Transfer Learning approaches proved to be very useful to adapt heavily pre-trained models to ever-changing scenarios, the incremental learning and adaptation capabilities of existing models is still limited and catastrophic forgetting very difficult to control. In this talk we will discuss our experience in the design of deep architectures and algorithms capable of learning objects incrementally both in a supervised and unsupervised way. Finally we will introduce a new dataset and benchmark (CORe50) that we specifically collected to focus on continuous object recognition for Robotic Vision.