Improving motor skill transfer during dyadic robot training through the modulation of the expert role

In daily life it is necessary to learn skills that can be applied in different tasks and different contexts. Usually these skills are acquired by observation or by direct physical training with another expert person. The critical point is to know which is the best possible way to achieve this knowledge acquisition. In this work we have proposed a collaborative environment where subjects with different levels of expertise have to interact through the use of a robotic platform. A motor skill learning algorithm has been designed in order to allow the less skilled subjects-naïves-to explore the virtual environment and to exploit the advantages of working with a skilled partner. Results show that the correct trade - off between exploration and exploitation, provided by the implemented algorithm applied during the dyadic training, allows a group of naive subjects to learn the task and generalize better the acquired skills respect to subjects trained without the proposed algorithm.