Abstract:We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment.Evolutionary Robotics: A new scientific tool for studying cognition The recent history of Evolutionary RoboticsEvolutionary Robotics (ER) is a term that has gained currency since the early 1990s for the study and application of an artificial analogue of natural Darwinian evolution to the design of robots or simulated agents; usually to the design of their control systems or 'artificial brains', but sometimes also to their bodily and sensorimotor design [1,2]. This was not a new idea -nearly 50 years earlier Alan Turing talked of designing brain-like networks through "genetical search" [3] -but a combination of factors perhaps made the conditions friendly to the re-emergence of such an approach.After decades of dominance by the computational paradigm of Good Old Fashioned Artificial Intelligence (GOFAI), in the 1980s there was a resurgence of interest in Artificial Neural Networks (ANNs), Admittedly, as the phrase "Parallel Distributed Processing" indicates [4], this was thought of by most of its proponents as some new form of "biologically plausible" computational processing, and for the most part went along with similar Cartesian assumptions to GOFAI. But this did at least open some people's eyes to the possibility that brains, both real and artificial, were possibly not doing anything like computation at all -computation in the sense that Turing defined. At the same time in the 1980s the development of personal computing power made it possible for many more people to be ambitious in their simulations and experimentation.Turning from simulated brains to real robots, also in the 1980s Brooks developed a behaviour-based approach to robotics using subsumption architecture [5]. He designed minimal "insect-like" robots in an incremental fashion explicitly modelled on the process of natural evolution. A simple robot was constructed with sensors, motors and just about the smallest conceivable amount of "artificial nervous system" so as to perform in real time the simplest possible of behaviours; for instance, forward movement avoiding obstacles. Only after this simplest level of behaviour was tested and debugged on the real robot was the next stage attempted: adding a next simple layer of behaviour that interacted with the environment and the pre-existing behaviour so as to slightl...

We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots were evolved to perform a formation-movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful system in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.

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