Movement learning in robotics

Today robotics is more and more inspired by the behaviour of the human being. If we want robots to interact with the real world, we have to look toward the smartest creature, us. Thanks to different researches in science, now we can try to reproduce, although with limitations, all the ability of the human in a robot. The results of these researches can be seen in developmental robotics that merge robotics, biology and neuroscience. The best known example of developmental robotics is iCub [11] that is a humanoid robot used for research into human cognition and artificial intelligence. The iCub has demonstrated capabilities in successfully perform human-like tasks: crawling, vision processing functions, progressive language learning, manipulation functions and so on. One of the most controversial point in this field is the movement learning. While efficient movement planning in typical low dimensional industrial robots, usually characterized by three to six DOFs, is a complex problem, optimal planning in 30 to 50 DOF systems, like iCub, with uncertain geometric and dynamic models is really hard, especially if we aim at real-time performance in a robotic system.

In everyday life we are required to move in a changing environment. Despite these variations we are able to achieve our behavioural goals easily, thanks to the process of motor learning. Motor learning can be defined as a set of…