Abstract
In this paper, we study a method of robot programming with view-based image processing. It can achieve more robustness against changes of task conditions than conventional teaching/playback without losing its general versatility. In order to reduce human demonstrations required in the method, we integrate reinforcement learning with the view-based robot programming. First we construct an initial neural network as a mapping from images to appropriate robot motions using human demonstration data. Next we train the neural network with actor-critic reinforcement learning so that it can work well even in task conditions that are not identical to those in demonstrations. Our proposed method is successfully applied to a box-pushing task in a virtual environment consisting of a 3D dynamics simulator.
