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：机器人的设计极为困难，这源于其物理结构、感知与运动布局以及行为之间复杂的相互依赖关系。尽管如此，迄今制造的所有机器人在几乎每个细节上，仍由人类设计师在数月或数年的迭代构思、原型制造和测试后手动确定。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，但其效率依然低下：需要数天的超级计算才能在仿真中设计出能展现预期行为的机器人，当这些机器人被实际制造时。本文首次展示了在单台消费级计算机上，于数秒内从头优化机器人结构以实现预期行为，且制造出的机器人能保留该行为。与其它基于梯度的机器人设计方法不同，该算法不预设任何特定解剖形态；相反，从随机生成的无足体形方案出发，它能持续发现腿部运动——这一已知最高效的陆地移动方式。若与自动化制造结合并扩展至更具挑战性的任务，这项进展有望在医疗、环境、车辆及太空任务中，实现独特且实用机器的近乎即时设计、制造与部署。