Transcript Slide 1
An EA for Box Pushing with a LEGO Mindstorms Robot By Sara L. Skroh December 5, 2003 Introduction • What is a LEGO Mindstorms Robot? – LEGO parts & RCX (Robotic Command eXplorer) – Hitachi H8 CPU – 32KB of RAM – Light sensor – Push sensor – 2 motors Research Questions • How can an EA be used to optimize box pushing? – An EA can be used to optimize weights of a neural network controller • Can an EA for optimizing box pushing be implemented on a LEGO Mindstorms robot? – Not with just the software that is shipped with the LEGO Robotics Invention System – Thanks to Daniel Berger (Max Planck Institute for Biological Cybernetics [7] it can be done Motivation • To perform low-level functions needed in more complex applications • To better understand what goes into developing high-level robotic functionality • Robot Soccer • Military Applications • etc. Background • Spronck et al. [4,5] using a simulated robot with eight proximity sensors compared a feedforward neural network design with a recurrent neural network design evolving the connectivity of the networks and found that the recurrent neural network works better. • Sprinkhuizen-Kuyper et al. [3] explore several fitness functions that use global vs. local and external vs. internal fitness measures. They found that a a global, external measure performed the best for box pushing. Background (contd.) • Montana and Davis [1] developed a genetic algorithm to train a feedforward neural network that outperformed backpropagation on their data. Methodology • EA to optimize weights of a neural network • Neural Network – simple feedforward architecture – 2 inputs (light sensor & push sensor) [0, 1023] – 3 hidden nodes – 2 outputs (left motor & right motor) [-8, 8] The EA • Population of individuals representing weight matrices for the neural network • Fitness function – sets an individual's fitness value based on the amount of time it takes the robot to reach the goal pushing the box using the weight represented by the individual. • Genetic Operators – Cross-over – elements in parent weight arrays – Mutation – add, subtract, multiply, or divide elements in the child by a random factor Parameters • • • • • Chance of cross-over Frequency of crossover Chance of mutation Frequency of mutation Population size Results / Conclusions • NN results • No EA results yet • Can’t conclude much without results References • • • [1] Training Feedforward Neural Netwrks Using Genetic Algorithms Montana, David J., Davis, Lawrence; Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Volume: 1, Aug. 1989 Pages(s): 762-767 [2] Evolutionary Learning of a Robot Controller: Effect of Neural Network Topology Sprinkhuizen-Kuyper, I.G., Postma, E.O., and Kortmann, R. BENELEARN 2000, Proceedings of the Tenth Belgian-Dutch Conference on Machine Learning, Tilburg University, Tilburg, Page(s): 55-60. [3] Fitness functions for evolving box-pushing behaviour Sprinkhuizen-Kuyper, I.G., Kortmann, R., and Postma, E.O. Proceedings of the Twelfth Belgium-Netherlands Artificial Intelligence Conference 2000, Page(s): 275-282. References (contd.) • • • • • [4] Evolutionary Learning of a Neural Robot Controller Spronck, P.H.M., Sprinkhuizen-Kuyper, I.G., and Postma, E.O. International Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA'2001, ISBN: 0-858-89847-0 Page(s): 511-519. [5] Evolutionary Learning of a Box-pushing Controller Spronck, Pieter, Sprinkhuizen-Kuyper, Ida, Postma, Eric and Kortmann, Rens Computational Intelligence in Control. Idea Group Publishing 2002 Page(s): 96-113. ISBN 1-59140-037-6. [6] Mindstorms: not just a kid's toy Wallich, P.; Spectrum, IEEE , Volume: 38 Issue: 9 , Sept. 2001 Page(s): 52-57 [7] http://www.kyb.tuebingen.mpg.de/bu/people/berger/mindstorms.html [8] http://mindstorms.lego.com/eng/default.asp Questions?