Abstract-In order to anticipate dangerous events, like a collision, an agent needs to make long-term predictions. However, those are challenging due to uncertainties in internal and external variables and environment dynamics. A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. We demonstrate that multi-modal predictions reduce to simpler distributions once actions are taken into account.

In this paper we propose an active learning approach applied to a music performance imitation scenario. The humanoid robot iCub listens to a human performance and then incrementally learns to use a virtual musical instrument in order to imitate the given sequence. This is achieved by first learning a model of the instrument, needed to locate where the required sounds are heard in a virtual keyboard layed out in a tactile interface. Then, a model of its body capabilities is also learnt, which serves to establish the likelihood of success of the actions needed to imitate the sequence of sounds and to correct the errors made by the underlying kinematic controller. It also uses selfevaluation stages to provide feedback to the human instructor, which can be used to guide its learning process.

Abstract-Gaussian Mixture Models have been widely used in robotic control and in sensory anticipation applications. A mixture model is learnt from demonstrations and later used to infer the most likely control signals, or is also used as a forward model to predict the change in sensory signals over time. However, such models often are too big to be tractable in real-time applications. In this paper we introduce the Context-GMM, a method to learn sparse priors over the mixture components. Such priors are stable over large amounts of time and provide a way of selecting very small subsets of mixture components without significant loss in accuracy and with huge computational savings.