Movement learning in robotics

Matteo Curci
15 min readMar 19, 2021

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 processes that, with exercises and experience, determines a relative change in performance and potentiality of the behaviour [12]. Also motor learning is generally conceived as the acquisition of new skilled movements. Learning is not directly visible, because the processes that determine changes are internal. If we observe an individual learning of a new movement, we may notice that the target is not immediately reached during its first attempts, in which executions are coarse or wrong, because learning is not that simple. The more the movement is complex, the more time is needed to learn it, adding new movement segments. Thus, learning requires repetitions. In 1975 Schmidt said that “the number of repeats of the movement to learn represent a basic element in order to form and strengthen the schema of the action. Such executions are necessary to store information on initial conditions, the parameters used in the response, on sensory feedback and the achieved results” [14].

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Matteo Curci

Computer engineer passionate about everything called Coding