Bioinspired framework for general-purpose learning

Learning applications in a robotic system are normally developed for specific areas and is difficult to generalize them for use in many other applications. Constraining the learning process to limited domains is good for fast development, but, unfortunately, what is gained in time is lost in generalization. Some basic principles of learning, valid for any area, and a framework where, what is learnt in a lower level application can be used in a higher level one, are shown here as a solution to this problem. First, some principles of the learning process, based on the statistical occurrence of inputs and outputs are explained. Additionally, a number of "accelerators" of the learning process are presented, which reduce and facilitate this process, and again are valid for all applications. Finally, a system simulation of a learning application is presented.