Development of Multiple Behaviors in Evolving Robots

Abstract

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.

Genetic Optimization of a Manipulator: Comparison between Straight, Rounded, and Curved Mechanism Links

“…ER strategies were described in a survey by Back [3] and in a newer paper by Li [4]. ER is often used to synthesize robot controllers for a specific behavior [5] or multiple behaviors [6]. One of the use cases for evolutionary robotics is in determining the kinematics required for a robot to optimally perform a task or a set of tasks.…”