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

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'.

Automated summary

This article investigates how learning can enhance evolutionary approaches to optimizing the performance of robots. By studying the interaction between the evolution of the body and brain, and individual and cultural learning mechanisms, it explores how the 'evolution plus learning' pattern influences the diversity, performance, and rate of improvement of robotic systems.