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 Pages(s): [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): [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):

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: Page(s): [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): ISBN [6] Mindstorms: not just a kid's toy Wallich, P.; Spectrum, IEEE, Volume: 38 Issue: 9, Sept Page(s): [7] [8]

Questions?