The robot baby and massive metacognition: Early steps via growing neural gas

摘要

We have initiated a long-term robotics project based on our previous work on metacognition as a powerful tool that can synergistically play machine learning and commonsense reasoning off one another. The new project involves a mobile robot that lives in a room and learns about the room and about itself. The robot is initially set up to have a standard set of facilities (vision, IR, limb, wheels, planners, learning modules, some modest NLP, a reasoner, etc.) but it does not know much about its capabilities or how to properly use them. It has a prime directive: to learn. This paper will focus on one of the first major questions of this project: can we use Growing Neural Gas (GNG) to discover the physical structure of an environment and, if so, what are the limits of its use? To answer this question, we have devised an experiment to test whether our robot can distinguish between two identical objects using only GNG. Preliminary results suggest that passing image data along with robotic control signal data is sufficient to autonomously detect the basic physical structure of a room.