Epigenetic sensorimotor pathways and its application to developmental object learning

Abstract

A pathway in the central nervous system (CNS) is a path through which nervous signals are processed in an orderly fashion. A sensorimotor pathway starts from a sensory input and ends at a motor output, although almost all pathways are not simply unidirectional. In this paper, we introduce a simple, biologically inspired, unified computational model - Multi-layer In-place Learning Network (MILN), with a design goal to develop a recurrent network, as a function of sensorimotor signals, for open-ended learning of multiple sensorimotor tasks. The biologically motivated MILN provides automatic feature derivation and pathway refinement from the temporally real-time inputs. The work presented here is applied in the challenging application field of developing reactive behaviors from a video camera and a (noisy) radar range sensor for a vehicle-based robot in open, natural driving environments. An internal model of the agent’s experience of the environments is created and refined from the ground-up using a cell-centered model, based on the genomic equivalence principle. The outputs can be imposed by a teacher, at the same time as the learning is active. At any time instant, sensory information from the radar allows the system to focus its visual analysis on relatively small areas within the image plane (attention selection), in a computationally efficient way, suitable for real-time training. This system was trained with data from 10 different city and highway road environments, and cross validation shows that MILN was able to correctly recognize above 95% of the radar-extracted images from the multiple environments. The in-place learning mechanism compares with other learning algorithms favorably, as results of a comparison indicate that in-place learning is the only one to fit all the specified criteria of development of a general-purpose sensorimotor pathway.