Applying Natural Design Principles For Biomimetic Robotic Control

深度学习在很大程度上受到灵长类大脑中分层信息处理架构的启发，已被证明是自主机器人控制环境中的一种强大工具。然而，现代深度学习已经大大偏离了它的生物学起源，然而动物的大脑可以解决复杂的任务，具有深度学习系统无法做到的灵活性、健壮性和能效。看来，自主控制体系结构将受益于进一步利用支配大脑信息处理体系结构的自然设计原则。这个项目的目的是应用这些原理来开发自治系统的新型控制体系结构，并将它们应用于类动物机器人的控制。自然设计的一个关键原则是约束闭合，即在不同时间尺度上运行的生物系统中的动态过程相互促进和维护[1]。限制闭合的一个例子是遗传同化，通过选择压力，环境习得的行为表型变得遗传编码的过程。在大脑分层控制结构的背景下，类似的动力学被描述为脚手架[2]，其中运行在不同层和时标上的过程可以相互约束，以加速对神经电路的有用适应的获得。当前的神经网络是大脑动态的、分层的体系结构的强大抽象，初步工作表明，脚手架可以被用来加速求解给定输入-输出映射的递归网络拓扑的进化搜索[3]。自那以后，人们发现脚手架可以用来进化复杂的网络状态链，每个状态都是网络动态的点吸引子。这一原理可以被用来存储机器人动作的序列，这样动作就可以作为吸引子存储，直到网络被例如传感器设备检测到的外部信号扰动。博士项目将在这项工作的基础上建立新的脚手架实际应用，用于认知系统的设计和仿生机器人的控制。一个重要的第一步将是研究最近开发的在非对称非二进制神经网络中存储点吸引子的算法，以期在这个方向推广支架的概念，并进一步开发这种方法来利用支架来仿生控制仿生机器人平台[4]。该项目将理想地利用潜在的行业赞助商Opteran开发的自主机器人系统。

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.