Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.

翻译：机器人设计因物理结构、感知与运动布局及行为之间复杂的相互依存关系而极具挑战性。尽管如此，迄今为止制造的每个机器人的几乎所有细节，都是由人类设计师经过数月或数年的迭代构思、原型制作和测试后手动确定的。受自然界进化设计的启发，利用进化算法自动设计机器人的尝试已持续二十年，但其效率仍然低下：需要数天的超级计算才能在仿真中设计出可制造并展现期望行为的机器人。本文首次展示：在单台消费级计算机上数秒内即可从头优化机器人结构以实现期望行为，且制造的机器人能保持该行为。与其他基于梯度的机器人设计方法不同，该算法无需预设任何特定解剖形态；相反，从随机生成的无足体形出发，它始终能发现腿部运动——已知最高效的地面移动方式。若结合自动制造技术并扩展至更具挑战性的任务，这一突破有望实现医疗、环境、交通及太空任务中独特实用机器的近乎即时设计、制造与部署。