Improving recurrent CSVM performance for robot navigation on discrete labyrinths

This paper presents an improvement of a recurrent learning system called LSTM-CSVM (introduced in [1]) for robot navigation applications, this approach is used to deal with some of the main issues addressed in the research area: the problem of navigation on large domains, partial observability, limited number of learning experiences and slow learning of optimal policies. The advantages of this new version of LSTM-CSVM system, are that it can find optimal paths through mazes and it reduces the number of generations to evolve the system to find the optimal navigation policy, therefore either the training time of the system is reduced. This is done by adding an heuristic methodoly to find the optimal path from start state to the goal state.can contain information about the whole environment or just partial information about it. © 2009 Springer-Verlag Berlin Heidelberg