In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a ‘newborn’ robot in order to better align it to its newly generated morphology. This can be accomplished via a learning mechanism applied to each individual robot: for practical reasons, such a mechanism should be both sample and time-efficient. In this paper, We investigate two ways to improve the sample and time efficiency of the well-known learner CMA-ES on navigation tasks. The first approach combines CMA-ES with Novelty Search, and includes an adaptive restart mechanism with increasing population size. The second bootstraps CMA-ES using Bayesian Optimisation, known for its sample efficiency. Results using two robots built with the ARE project's modules and four environments show that novelty reduces the number of samples needed to converge, as does the custom restart mechanism; the latter also has better sample and time efficiency than the hybridised Bayesian/Evolutionary method.

The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of both virtual and physical robots with evolving brains and bodies. One of the major challenges for such a vision is the need to construct many unique individuals without prior knowledge of what designs evolution will produce. To this end, an autonomous robot fabrication system for evolutionary robotics, the Robot Fabricator , is introduced in this paper. Evolutionary algorithms can create robot designs without direct human interaction; the Robot Fabricator will extend this to create physical copies of these designs (phenotypes) without direct human interaction. The Robot Fabricator will receive genomes and produce populations of physical individuals that can then be evaluated, allowing this to form part of the evolutionary loop, so robotic evolution is not confined to simulation and the reality gap is minimised. In order to allow the production of robot bodies with the widest variety of shapes and functional parts, individuals will be produced through 3D printing, with prefabricated actuators and sensors autonomously attached in the positions determined by evolution. This paper presents details of the proposed physical system, including a proof-of-concept demonstrator, and discusses the importance of considering the physical manufacture for evolutionary robotics.