Neural Network Architecture for Cognitive Navigation in Dynamic Environments

Abstract

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

“…Within this theory, spatiotemporal relations between objects in the environment are encoded as static (cognitive) maps and represented as elements of an n-dimensional space (n 1). The cognitive maps as information items can be learnt, classified, and retrieved on demand [48]. However, the questions concerning how the brain or individual neurons can distinguish among a huge number of different maps and select an appropriate one remain unknown.…”

Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs**This work was supported by the Russian Science Foundation (project 15-12-10018).

“…to a state with the same connectivity pattern obtained by averaging the initial patterns of all network motifs in the ring. We foresee that the reported mechanism of learning can be useful for replication of scenarios of cognitive navigation in dynamic environments (Villacorta-Atienza and Makarov, 2013).…”

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

“…According to the hypothesis, the brain transforms "time into space" (Villacorta-Atienza et al, 2010; Villacorta-Atienza and Makarov, 2013). Such a functional mechanism, called time compaction, allows representing a dynamic situation as a static map, similar to a standard CM.…”

“…However, its power goes far beyond effective or "applied" cognition. The static representation of the subject's actions as mere points in a configuration space enables building memories of static images of motor-motifs, instead of memorizing the whole spatiotemporal situations (Villacorta-Atienza and Makarov, 2013). Then, the subject can establish causal relationships among such images and build high-level cognitive strategies in complex dynamic situations.…”

“…Although robots strive to dexterous object handling and gradually improve skills in orientation in space (Billard and Kragicet, 2019), they still undergo difficulties in handy and safe interactions with humans in time-evolving situations. Such cooperation requires the implementation of motor cognition at different levels of decision-making, including the abstract one (Villacorta-Atienza and Makarov, 2013). The latter, in particular, can be approached through studies of brain structures and functions involved in cognitive phenomena (Sporns, 2011;Calvo et al, 2020).…”