Multiresolutional schemata for unsupervised learning of autonomous robots for 3-D space operation

Abstract This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) that presents the ASR as a “baby”—that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allow it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment, and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a contantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolution nature. Experimenting with ASR is especially interesting because the rules of input control do not coincide with human intuitions. Actually, we want to see that the simulated device can learn unexpected schemata from its own experience. Although the traditional approach to autonomous navigation involves off-line path planning with a known world map (such as the potential fields algorithm), in most of the real tasks the environment is not well knowm because of everchanging conditions such as absence of gravity and because of sophisticated, hard-to-predict obstacles like components of the space station. Astro-baby gathers data from its sensors and then, by using a schema-discovery system, it extracts concepts, forms schemata, and creates a quantitative/conceptual semantic network. When the Astro-baby is first dropped into space, it does not have any experiences and its sensors and actuators are sets that do not have any distinction among elements. Then, by trial and error, the ASR learns the function of its actuators and sensors and how to activate them to achieve the goal given by its creator or the sub-goals that it finds. In our simulation, the initial goal is to minimize the distance to a beacon. The results of simulation are positive: Astro-baby displays the ability to learn a number of maneuvers.