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Autonomous Robot Evolution (ARE): Cradle to Grave

自主机器人进化（ARE）：从摇篮到坟墓

基本信息

批准号：
EP/R03561X/1
负责人：
Andy Tyrrell
金额：
$ 128.97万
依托单位：
University of York
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR03561X%2F1
关键词：
Autonomous Robot Evolution Cradle Grave

项目摘要

Robotics is changing the landscape of innovation. But traditional design approaches are not suited to novel or unknown habitats and contexts, for instance: robot colonies for ore mining, exploring or developing other planets or asteroids, or robot swarms for monitoring extreme environments on Earth. New design methodologies are needed that support optimising robot behaviour under different conditions for different purposes. It is accepted that behaviour is determined by a combination of the body (morphology, hardware) and the mind (controller, software). Embodied AI and morphological computing have made major progress in engineering artificial agents (i.e., robots) by focusing on the links between morphology and intelligence of natural agents (i.e., animals). While such a holistic body-mind approach has been hailed for its merits, we still lack an actual pathway to achieve this.While this goal is ambitious, it is achievable by introducing a unique methodology: a hybridisation of the physical evolutionary system with a virtual one. On the one hand, it is appreciated that an effective design methodology requires the use and testing of physical robots. This is because simulations are prone to hidden biases, errors and simplifications in the underlying models. Simulating populations of robots (rather than just simulating specific parts) leads to accumulated errors and a lack of physical plausibility: the evolved designs will not work in the real system. This is the notorious reality gap of evolutionary robotics. On the other hand, evolving everything in hardware is time and resource consuming. One of our major innovations is to run simulated evolution concurrently with the physical and hybridise them by cross-breeding, where a physical and a virtual robot can parent a child that may be born in the real world, in the virtual world or in both. The advantages of such a hybrid system are significant. Physical evolution is accelerated by the virtual component that can run faster to find good robot features with less time and resources; simulated evolution benefits from the influx of genes that are tested favourably in the real world. Furthermore, monitoring of and feedback from the physical system can improve the simulator, reducing the reality gap.

机器人技术正在改变创新的格局。但传统的设计方法并不适合新的或未知的栖息地和环境，例如：用于采矿的机器人殖民地，探索或开发其他行星或小行星，或用于监测地球上极端环境的机器人群。需要新的设计方法来支持在不同条件下为不同目的优化机器人行为。人们普遍认为，行为是由身体（形态，硬件）和思想（控制器，软件）的组合决定的。人工智能和形态计算在工程人工智能方面取得了重大进展（即，机器人）通过关注自然代理的形态和智能之间的联系（即，动物）。虽然这种整体身心方法因其优点而受到欢迎，但我们仍然缺乏实现这一目标的实际途径。虽然这一目标雄心勃勃，但它可以通过引入一种独特的方法来实现：物理进化系统与虚拟系统的混合。一方面，可以理解的是，有效的设计方法需要使用和测试物理机器人。这是因为模拟容易在基础模型中隐藏偏见，错误和简化。模拟机器人群体（而不仅仅是模拟特定的部分）会导致累积的错误和缺乏物理兼容性：进化后的设计在真实的系统中无法工作。这就是进化机器人的臭名昭著的现实差距。另一方面，在硬件中进化一切都是耗时耗力的。我们的主要创新之一是将模拟进化与物理进化同时进行，并通过杂交繁殖将它们杂交，物理和虚拟机器人可以养育一个可能出生在真实的世界、虚拟世界或两者兼而有之的孩子。这种混合系统的优点是显著的。物理进化被虚拟组件加速，虚拟组件可以运行得更快，以更少的时间和资源找到好的机器人特征;模拟进化受益于在真实的世界中被有利地测试的基因的涌入。此外，物理系统的监控和反馈可以改善模拟器，减少现实差距。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Practical hardware for evolvable robots.

DOI：
10.3389/frobt.2023.1206055
发表时间：
2023
期刊：
FRONTIERS IN ROBOTICS AND AI
影响因子：
3.4
作者：
Angus, Mike;Buchanan, Edgar;Le Goff, Leni K;Hart, Emma;Eiben, Agoston E;De Carlo, Matteo;Winfield, Alan F;Hale, Matthew F;Woolley, Robert;Timmis, Jon;Tyrrell, Andy M
通讯作者：
Tyrrell, Andy M

Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

使用控制器存档作为继承机制进行学习的 Morpho 进化