The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents

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Evolutionary robotics and neuroscience

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Evolutionary robotics: model or design?

In this paper, I review recent work in evolutionary robotics (ER), and discuss the perspectives and future directions of the field. First, I propose to draw a crisp distinction between studies that exploit ER as a design methodology on the one hand, and studies that instead use ER as a modeling tool to better understand phenomena observed in biology. Such a distinction is not always that obvious in the literature, however. It is my conviction that ER would profit from an explicit commitment to one or the other approach. Indeed, I believe that the constraints imposed by the specific approach would guide the experimental design and the analysis of the results obtained, therefore reducing arbitrary choices and promoting the adoption of principled methods that are common practice in the target domain, be it within engineering or the life sciences. Additionally, this would improve dissemination and the impact of ER studies on other disciplines, leading to the establishment of ER as a valid tool either for design or modeling purposes

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour

Active shape discrimination with compliant bodies as reservoir computers

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive