Robotics is evolving today toward applications in which robots enter into everyday life and thus need to be able to interact with non-engineers; robots that assist people with disabilities in their homes are an example [5]. For such applications, robots must be able to manipulate and use objects in the human environment that cannot be known and modelled beforehand by engineers. For reasons of safety and energy efficiency, the use of soft materials and soft actuators is flourishing in these applications [12]; difficult control problems arise, however, because the dynamics of these soft materials are difficult to model analytically. For these reasons, a crucial goal has become the development of learning methods that allow robots to acquire models of the dynamics of their own bodies and their interactions with new objects.