Psychologically inspired planning method for smart relocation task

TL;DR

This paper introduces a cognitively inspired hierarchical planning method for robot navigation, combining symbolic and subsymbolic approaches, and demonstrates its potential through preliminary experiments.

Contribution

It presents a novel behavior planning method integrating cognitive experiment insights into a hierarchical model for robot control.

Findings

Hierarchical two-level model effectively combines symbolic and subsymbolic planning.

Preliminary experiments show promising results in smart relocation tasks.

Abstract

Behavior planning is known to be one of the basic cognitive functions, which is essential for any cognitive architecture of any control system used in robotics. At the same time most of the widespread planning algorithms employed in those systems are developed using only approaches and models of Artificial Intelligence and don't take into account numerous results of cognitive experiments. As a result, there is a strong need for novel methods of behavior planning suitable for modern cognitive architectures aimed at robot control. One such method is presented in this work and is studied within a special class of navigation task called smart relocation task. The method is based on the hierarchical two-level model of abstraction and knowledge representation, e.g. symbolic and subsymbolic. On the symbolic level sign world model is used for knowledge representation and hierarchical planning algorithm, PMA, is utilized for planning. On the subsymbolic level the task of path planning is considered and solved as a graph search problem. Interaction between both planners is examined and inter-level interfaces and feedback loops are described. Preliminary experimental results are presented.