Applying Natural Design Principles For Biomimetic Robotic Control

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Deep learning, which is loosely inspired by the layered information-processing architectures in primate brains, has proven to be a powerful tool in the context of autonomous robotic control. However, modern deep learning has deviated significantly from its biological origins, and yet animal brains can solve complex tasks with a combination of flexibility, robustness, and energy-efficiency that deep-learning systems cannot. It seems then that autonomous control architectures would benefit from further exploiting the natural design principles that govern the information-processing architectures of brains. The aim of this project is to apply such principles to develop novel control architectures for autonomous systems and apply them for the control of animal-like robots.One key principle of natural design is constraint closure, whereby dynamic processes in biological systems operating on different timescales contribute to and maintain each other [1]. One example of constrain closure is genetic assimilation, a process by which environmentally learnt behavioural phenotypes become genetically encoded through selection pressures. Similar dynamics have been described in the context of layered control architectures in brains as scaffolding [2], where processes operating on different layers and timescales can constrain each other to accelerate the acquisition of useful adaptations to neural circuitry.Recurrent neural networks are a powerful abstraction of the dynamic, layered architecture of brains, and preliminary work has shown that scaffolding can be exploited to accelerate the evolutionary search for a recurrent network topology that solves a given input-output mapping [3]. It has since been discovered that scaffolding can be harnessed to evolve complex chains of network states, each a point attractor for the network dynamics. This principle could be harnessed to store sequences of robot actions, such that actions can be stored as attractors that persist until the network is perturbed e.g. by an external signal detected by the sensor apparatus.The PhD project will build on this work to establish new practical applications of scaffolding for the design of cognitive systems and for the control of biomimetic robots. An important first step will be to study a recently developed algorithm for storing point attractors in asymmetrical non-binary neural networks, with a view to generalising the scaffolding concept in this direction, and developing this approach further to harness scaffolding for biomimetic control of biomimetic robot platforms [4].The project will ideally take advantage of the autonomous robotic systems developed by potential industry sponsor, Opteran.