Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

Abstract

We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolve the body and brain of a single robot to optimize performance on a specified task. The review is grounded in a general framework for evolution which permits the interaction of artificial evolution acting on a population with individual and cultural learning mechanisms. We discuss examples of variations of the general scheme of ‘evolution plus learning’ from a broad range of robotic systems, and reflect on how the interaction of the two paradigms influences diversity, performance and rate of improvement. Finally, we suggest a number of avenues for future work as a result of the insights that arise from the review. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.