| Project Detail |
Current robot technologies are still not enough autonomous and adaptive to safely and intuitively interact with people. Nowadays, artificial systems mostly rely on pre-engineered models of the world and of their embodiment. Defining such models a-priori can be very challenging and may result in robots lacking the capability to react to situations that are not foreseen by their designers. The forthcoming societal needs and economic opportunities for robotics require smarter, more adaptive and self-aware artificial systems. This project addresses this challenge by developing new methods: (1) for the autonomous acquisition of models of the robot’s body inspired on infant development, where online deep learning techniques are integrated within curiosity-driven exploration strategies for high-dimensional task spaces; (2) for the enhancement of robots’ perceptual skills based on predictive processes, such as visual input enhancement through the attenuation of expected perceptions of self-body movements; (3) for studying possibilities of an artificial Self and of providing subjective experiences to robots. The core of the proposed research lays on predictive models learned through exploration behaviours typical of infants’ development. Predictive robots will be capable of anticipating sensory consequences of intended actions. This research has a broad range of applications - such as low-cost improvement of current sensing technologies - and can advance the understanding of brain processes behind particular phenomena - such as the sense of object permanence, memory, self-awareness and sense of agency - beside providing insights for their implementation into artificial systems. The research project will be conducted on humanoid robots and marine drones at the BioRobotics Institute of the Scuola Superiore di Studi Universitari e di Perfezionamento SantAnna in Pisa, Italy. |
