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Research » Imitation Learning

Imitation Learning

Humans and many animals do not just learn a task by trial and error. Rather they extract knowledge about how to approach a problem from watching other people performing a similar task. From the viewpoint of computational motor control, learning from demonstration is a highly complex problem that requires to map a perceived action that is given in an external (world) coordinate frame into a totally different internal frame of reference to activate motoneurons and subsequently muscles. Recent work in behavioral neuroscience has shown that there are specialized neurons ("mirror neurons") in the frontal cortex of primates that seem to be the interface between perceived movement and generated movement, i.e., these neurons fire very selectively when a particular movement is shown to the primate, but also when the primate itself executes the movement. Imaging studies with humans confirmed the validity of these results.

Research on learning from demonstration offers a tremendous potential for future autonomous robots, but also for medical and clinical research. If we can start teaching machines by showing, our interaction with machines would become much more natural. If a machine can understand human movement, it can also be used in rehabilitation as a personal trainer that watches a patient and provides specific new excercises how to improve a diminished motor skill. Finally, the insights into biological motor control developed in learning from demonstration can help to build adaptive prosthetic devices that can be taught to improve the performance of a prosthesis.

In several projects, we have started to study learning from demonstration from a the view point of learning theory. Our working hypothesis is that a perceived movement is mapped onto a finite set of movement primitives that compete for perceived action. Such a process can be formulated in the framework of competitive learning. Each movement primitive predicts the outcome of a perceived movement and tries to adjust its parameters to achieve an even better prediction, until a winner is determined. In preliminary studies with anthropomorphic robots we have demonstrated the feasibility of our approaches. Nevertheless, many open problems remain for future research. Collaborators of our laboratory in Japan also try to develop theories on how the cerebellum could be involved in learning movement primitives. In our future research we will employ the humanoid robot above to study learning from demonstration in a man-humanoid environment.

Contact persons: Stefan Schaal


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