The evolution of task-oriented control for robots with complex locomotor systems is currently out of reach for traditional evolutionary computation techniques, as the coordination of a high number of locomotion parameters in response to the robot’s sensory inputs is extremely challenging. Evolutionary techniques have therefore mainly been applied to the optimization of specific locomotion patterns, such as forward motion. In this paper, we explore the Evolutionary Repertoire-based Control (EvoRBC) approach, which divides the synthesis of control into two steps: (i) the evolution of a repertoire of locomotion primitives using a quality diversity algorithm; and (ii) the evolution of a high-level arbitrator that leverages the locomotion primitives in the repertoire to solve a given task. We comprehensively study the main components of the EvoRBC approach using a four-wheel steering robot. We then conduct a set of experiments in simulation using a hexapod robot. Our results show that EvoRBC is robust to parameter variations, and for all the robots tested, it is able to evolve controllers for a maze navigation task and significantly outperforms the traditional evolutionary robotics approach.

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Evolutionary computation,Neural networks,Robot motion,Legged locomotion,Evolutionary robotics,Quality diversity,Hierarchical control

• Computer and Information Sciences - Natural Sciences