Title :
Fitness biasing for the box pushing task
Author :
Parker, Gary ; O´Connor, Jim
Author_Institution :
Comput. Sci., Connecticut Coll., New London, CT, USA
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;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4577-0652-3
DOI :
10.1109/ICSMC.2011.6083956
