Fitness biasing for the box pushing task

Author

Parker, Gary ; O´Connor, Jim

Author_Institution

Comput. Sci., Connecticut Coll., New London, CT, USA

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

1944

Lastpage

1949

Abstract

Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the controller for a robot learning the box pushing task. The robot that was built for this task, was measured to create an accurate model. The model was used in simulation to test the effectiveness of Anytime Learning with Fitness Biasing for the box pushing task. This work is the first step in new research where an automated system to test the viability of Fitness Biasing will be created, as well as the first application of Fitness Biasing to a high level task such as box pushing.

Keywords

learning (artificial intelligence); mobile robots; robot dynamics; anytime learning; automated system; box pushing task; fitness biasing; high level task; robot learning; Biological cells; Genetic algorithms; Mobile robots; Robot kinematics; Robot sensing systems; Training; anytime learning; evolutionary robotics; genetic algorithm; learning control;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6083956

Filename

6083956