The Semantic Hierarchy in Robot Learning

Abstract

We have been exploring an approach to robot learning based on a hierarchy of types of knowledge of the robot's senses, actions, and spatial environment. This approach grew out of a computational model of the human cognitive map that exploited the distinction between procedural, topological, and metrical knowledge of large-scale space. More recently, the semantic hierarchy approach has been extended to continuous sensorimotor interaction with a continuous environment, demonstrating the fundamental role of identification of distinctive places in robot spatial learning. In this paper, we describe three directions of current research. First, we are scaling up our exploration and map-learning methods from simulated to physical robots. Second, we are developing methods for a tabula rasa robot to explore and learn the properties of an initially uninterpreted sensorimotor system to the point where it can reach the control level of the spatial semantic hierarchy, and hence build a cognitive map..