  Reservoir Computing is a relatively new framework created to allow the usage
of powerful but complex systems as computational mediums. The basic approach
consists in training only a readout layer, exploiting the innate separation and
transformation provided by the previous, untrained system. This approach has
shown to possess great computational capabilities and is successfully used to
achieve many tasks. This review aims to represent the current
'state-of-the-art' of the usage of Reservoir Computing techniques in the
robotic field. An introductory description of the framework and its
implementations is initially given. Subsequently, a summary of interesting
applications, approaches, and solutions is presented and discussed.
Considerations, ideas and possible future developments are proposed in the
explanation.
