Combining neural networks and optimization techniques for visuokinesthetic prediction and motor planning.

Abstract

We present a method for motor planning based on visuokines- thetic prediction by a forward model (FM) and the optimization method differential evolution (DE) for a block-pushing task of a robot arm. The FM is implemented by a set of multi-layer perceptrons and used for the iterative prediction of future sensory states in an internal simulation pro- cess. DE is applied to determine via this internal simulation the movement sequences by which a target block can be successfully pushed from an ar- bitrary start to an arbitrary goal position. The presented method shows a good performance on the pushing task.