A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.

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 goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, skeleton parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are organs which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation.

Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new challenges, in that robot models that can be instantiated from an encoding in simulation might not be manufacturable in practice (due to constraints associated with the 3D printing and/or automated assembly processes). We introduce a representation for evolving (wheeled) robots with a printed plastic skeleton, and evaluate three variants of a novelty-search algorithm in terms of their ability to produce populations of manufacturable but diverse robots. While the set of manufacturable robots discovered represent only a small fraction of the overall search space of all robots, all methods are shown to be capable of generating a diverse population of manufacturable robots that we conjecture is large enough to seed an evolving robotic ecosystem.

Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. However, previous studies suggest that simultaneously evolving both controller and bodyplan could open up many interesting possibilities. However, the joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body-plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit 'types' of robots (where this is defined with respect to the features of the body-plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.