On Applying Neuroevolutionary Methods to Complex Robotic Tasks

In this paper, we describe possible methods of solving two problems encountered in evolutionary robotics, while applying neuroevolutionary methods to evolve controllers for complex robotic tasks. The first problem is the large number of evaluations required to obtain a solution. We propose that this problem can be addressed by accelerating neuroevolutionary methods using a Kalman filter. The second problem is the difficulty of obtaining a desirable solution that results from the difficulty of defining an appropriate fitness function for a complex robotic task. The solution towards this problem is to apply the principles of behavior based systems to decompose the solution space into smaller subsolutions with lower number of intrinsic dimensions, and incrementally modify the fitness function. We present two case studies towards the solutions to the stated problems.