Analysis of Co-evolutionary Approach for Robotic Gait Generation

Recently, a new co-evolutionary approach for generating motion patterns for multi-legged robots which exhibit symmetry and module repetition was proposed. The algorithm consists of two evolutionary algorithms working in co-evolution. The first one, a genetic programming module, evolves a motion of a single leg. The second one, a genetic algorithm module, seeks for the optimal deployment of the single-leg motion pattern to all legs of the robot. Thus, the whole task is decomposed into two subtasks that are to be solved simultaneously. First proof-of-concept experiments proved such a decomposition helps to produce better solutions than a simple GP-based approach that tries to evolve individual motion patterns for all legs of the robot. This paper further analyzes the co-evolutionary algorithm focusing on two things – the way it handles the problem decomposition and the type of functions it uses to control joints of the robot. The experiments carried out in this work indicate that both design choices positively contribute to its performance.