Developmental Robots - A New Paradigm

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming

Vision-Guided Navigation Using SHOSLIF

This paper presents an unconventional approach to vision-guided autonomous navigation. The system recalls information about scenes and navigational experience using content-based retrieval from a visual database. To achieve a high applicability to various road types, we do not impose a priori scene features, such as road edges, that the system must use. But rather, the system automatically derives features from images during supervised learning. To accomplish this, the system uses principle component analysis and linear discriminant analysis to automatically derive the most expressive features (MEF) for scene reconstruction or the most discriminating features (MDF) for scene classification. These features best describe or classify the population of the scenes and approximate complex decision regions using piecewise linear boundaries up to a desired accuracy. A new self-organizing scheme called recursive partition tree (RPT) is used for automatic construction of a vision-and-control da..