Spatio-temporal case-based reasoning for behavioral selection

Abstract

Presents the application of a case-based reasoning approach to the selection and modification of behavioral assemblage parameters. The goal of this research is to achieve an optimal parameterization of robotic behaviors in run-time. This increases robot performance and makes a manual configuration of parameters unnecessary. The case-based reasoning module selects a set of parameters for an active behavioral assemblage in real-time. This set of parameters fits the environment better than hand-coded ones, and its performance is monitored providing feedback for a possible reselection of the parameters. The paper places a significant emphasis on the technical details of the case-based reasoning module and how it is integrated within a schema-based reactive navigation system. The paper also presents the results and evaluation of the system in both in simulation and real world robotic experiments.