| Abstract: | In this paper, we propose an architecture for a cognitive robot based on tactile and visual information. Visual information contains various features such as location and area of various colored regions. Most of these features are irrelevant for object recognition to achieve the given task. In the architecture, tactile information plays a key role in the selection of visual features and discritization of selected features. In order to find appropriate visual features we use a correlation coefficient between the values of the features and action series. Then the ChiMerge algorithm is employed to discritize the value of the selected feature into a small number of intervals. Consequently, quantization of a state space to accomplish the given task is achieved. An appropriate behavior to the given task is acquired by using this state space with reinforcement learning algorithm. We give experimental results of computer simulation to show the validity of our method. |
