Towards a cognitive architecture for self-supervised transfer learning for objects detection with a Humanoid Robot

Robots are becoming more and more present in our daily life operating in complex and unstructured environments. To operate autonomously they must adapt to continuous scene changes and therefore must rely on an incessant learning process. Deep learning methods have reached state-of-the-art results in several domains like computer vision and natural language processing. The success of these deep networks relies on large representative datasets used for training and testing. But one limitation of this approach is the sensitivity of these networks to the dataset they were trained on. These networks perform well as long as the training set is a realistic representation of the contextual scenario. For robotic applications, it is difficult to represent in one dataset all the different environments the robot will encounter. On the other hand, a robot has the advantage to act and to perceive in the complex environment. As a consequence when interacting with humans it can acquire a substantial amount of relevant data, that can be used to perform learning. The challenge we addressed in this work is to propose a computational architecture that allows a robot to learn autonomously from its sensors when learning is supported by an interactive human. We took inspiration on the early development of humans and test our framework on the task of localisation and recognition of objects. We evaluated our framework with the humanoid robot iCub in the experimental context of a realistic interactive scenario. The human subject naturally interacted with the robot showing objects to the iCub without supervision in the labelling. We demonstrated that our architecture can be used to successfully perform transfer learning for an object localisation network with limited human supervision and can be considered a possible enhancement of traditional learning methods for robotics.