A memory-based approach to sensory-motor coordination

In this work a visual-based autonomous system capable of memorizing and recalling sensory-motor associations is presented. The robot's behaviors are based on learned associations between its sensory inputs and its motor actions. Perception is divided into two stages. The first one is functional: algorithmic procedures extract in real time visual features such as disparity and local orientation from the input images. The second stage is mnemonic: the features produced by the different functional areas are integrated with motor information and memorized or recalled. An efficient memory organization and fast information retrieval enables the robot to learn to navigate and to avoid obstacles without need of an internal metric reconstruction of the external environment.