Learning geometry from sensorimotor experience

Abstract

A baby experiencing the world for the first time faces a considerable challenging sorting through what William James called the “blooming, buzzing confusion” of the senses [1]. With the increasing capacity of modern sensors and the complexity of modern robot bodies, a robot in an unknown or unfamiliar body faces a similar and equally daunting challenge. In order to connect raw sensory experience to cognitive function, an agent needs to decrease the dimensionality of sensory signals. In this paper a new approach to dimensionality reduction called sensorimotor embedding is presented, allowing an agent to extract spatial and geometric information from raw sensorimotor experience. This approach is evaluated by learning the geometry of Gridworld and RovingEye robot domains. The results show that sensorimotor embedding provides a better mechanism for extracting geometric information from sensorimotor experience than standard dimensionality reduction methods.