Robot gaits evolved by combining genetic algorithms and binary hill climbing

In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental approach combining a genetic algorithm (GA) with hill climbing is proposed. This combination interacts in an efficient way to generate precise walking patterns in less than 15 generations. Our proposal is compared to various versions of GA and stochastic search, and finally tested on a pneumatic biped walking robot. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics--Propelling mechanisms; I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search--Heuristic methods General Terms Algorithms Keywords Evolutionary robotics, Genetic algorithms, Machine learning

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Lena Mariann Garder, Mats Erling Høvin
• Keywords: Biped Walking Robot, GECCO 2006, Genetic Algorithms, Optimization, Precise Walking Patterns