Autonomous Robot Evolution (ARE): Cradle to Grave

机器人技术正在改变创新的格局。但传统的设计方法不适合于新颖或未知的栖息地和环境，例如：用于采矿、探索或开发其他行星或小行星的机器人殖民地，或用于监测地球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)和头脑(控制器、软件)共同决定的。体现人工智能和形态计算通过关注自然智能体(即动物)的形态和智能之间的联系，在工程人工智能体(即机器人)方面取得了重大进展。尽管这样的整体身心方法因其优点而受到称赞，但我们仍然缺乏实现这一目标的实际途径。尽管这个目标雄心勃勃，但它可以通过引入一种独特的方法来实现：物理进化系统和虚拟进化系统的混合。一方面，人们认识到，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底层模型中存在隐藏的偏差、错误和简化。模拟机器人群体(而不仅仅是模拟特定的部件)会导致累积的错误和缺乏物理合理性：进化的设计将不会在真实系统中工作。这就是进化机器人学臭名昭著的现实鸿沟。另一方面，在硬件中发展一切都是耗费时间和资源的。我们的主要创新之一是同时运行模拟进化和物理进化，并通过杂交使它们杂交，其中物理和虚拟机器人可以养育可能出生在现实世界、虚拟世界或两者都有的孩子。这种混合系统的优势是显著的。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；模拟进化受益于在现实世界中得到有利测试的基因的涌入。此外，对物理系统的监控和反馈可以改进模拟器，缩小现实差距。

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.