Machines that can mate and produce offspring can help us clean up nuclear sites, explore asteroids and terraform distant planets – but could they prove a threat, asks Emma Hart, who is helping develop them

可以交配和产生后代的机器人可以帮助我们清理核设施、探索小行星和对遥远的行星进行地球化改造——但它们是否会被证明是一种威胁?开发它们的艾玛哈特问道。


ROBOTS have come a long way in the century since Czech writer Karel Čapek used the word to describe artificial automata. Once largely confined to factories, they are now found everywhere from the military and medicine to education and underground rescue. People have created robots that can make art, plant trees, ride skateboards and explore the ocean’s depths. There seems no end to the variety of tasks we can design a machine to do.

“ROBOTS”,自从捷克作家 Karel Čapek 使用这个词来描述人造自动机器以来,机器人在本世纪已经取得了长足的进步。他们曾经主要局限于工厂,现在从军队和医学到教育和地下救援无处不在。人们已经创造了可以创作艺术品、植树、骑滑板和探索海洋深处的机器人。我们可以设计一台机器来完成各种任务,这似乎没有尽头。

But what if we don’t know exactly what our robot needs to be capable of? We might want it to clean up a nuclear accident where it is unsafe to send humans, explore an unmapped asteroid or terraform a distant planet, for example. We could simply design it to meet any challenges we think it might face and then keep our fingers crossed. There is a better alternative, though: take a lesson from evolution and create robots that can adapt to their environment. It sounds far-fetched, but that is exactly what my colleagues and I are working on in a project called Autonomous Robot Evolution (ARE).

但是,如果我们不确切知道我们的机器人需要具备什么能力呢?例如,我们可能希望它清理一次核事故、探索未绘制地图的小行星或对遥远的星球进行地球化,在这种工作中派遣人类是不安全的。我们可以简单地设计它来应对我们认为它可能面临的任何挑战,然后保持我们的手指交叉(进行祈祷)。不过,还有一个更好的选择:创造能够从进化中吸取教训和适应环境的机器人。这听起来有些牵强,但这正是我和我的同事在一个名为机器人自主进化 (Autonomous Robot Evolution,ARE)的项目中所做的工作。

We aren’t there yet, but we have already created robots that can “mate” and “reproduce” to generate new designs that are built autonomously. What’s more, using the evolutionary mechanisms of variation and survival of the fittest, over generations, these robots can optimise their design. If successful, this would be a way to produce robots that can adapt to difficult, dynamic environments without direct human oversight. It is a project with huge potential – but not without major challenges and ethical implications.

我们还没有做到这一点,但我们已经创造了可以“配对”和“复制”的机器人,以生成自主构建的新设计。更重要的是,利用变异和适者生存的进化机制,经过几代人,这些机器人可以优化他们的设计。如果成功,这将是生产一种能够适应困难、动态环境而无需人工直接监督的机器人的方法。这是一个具有巨大潜力的项目——但并非没有重大挑战和道德影响。

The notion of using evolutionary principles to design obxts can be traced back to the early 1960s and the origins of evolutionary computation, when a group of German engineering students invented the first “evolution strategy”. Their novel algorithm generated a range of designs and then sexted a set of them, biased towards high-performing ones, to build upon in subsequent iterations. When applied to a real-world engineering problem, this not only optimised the design of a nozzle but also generated a final product that was so unintuitive that the process could be described as creative – one of the most prized properties of biological evolution.

使用进化原理设计物体的概念可以追溯到 1960 年代早期,进化计算的起源,当时一群德国工科学生发明了第一个“进化策略”。他们的新颖算法生成了一系列设计,然后选择了一组偏向于高性能的设计,以此为基础进行后续迭代优化。当将次技术应用于现实世界的工程问题时,这不仅优化了喷嘴的设计,而且还生成了一个非常不直观的最终产品。这个过程可以被描述为创造性的——这是生物进化中最珍贵的特性之一。
原创翻译:龙腾网 http://www.ltaaa.cn 转载请注明出处


Since then, there has been a step change in our ability to apply artificial evolution to designing obxts. The enormous increase in computational power allows computers to churn through generations of designs in short order and to generate high-fidelity simulations of real environments in which to test these. Meanwhile, advances in evolutionary computation theory have resulted in better ways to represent the information from which designs are built – their virtual DNA – and to manipulate this when generating “offspring” so that it mirrors processes found in nature. These include mutation and DNA recombination, which creates genetic diversity through breaking stretches of DNA and recombining them in novel ways. Examples of evolutionary design in practice now range from tables to new molecules with desired functions. As far back as 2006, NASA sent a satellite into space with a communication antenna created via artificial evolution.

从那时起,我们将人工进化应用于设计对象的能力发生了重大变化。计算能力的巨大提高使计算机能够在短时间内完成几代设计,并生成对真实环境进行高保真模拟来测试这些设计。与此同时,进化计算理论的进步带来了更好的方法来表示构建设计的信息——它们的虚拟 DNA——并在生成“后代”时对其进行操作,使其反映自然界中发现的过程。其中包括突变和DNA重组,它通过破坏DNA片段并以新的方式重组它们来创造遗传多样性。实践中进化设计的例子现在从表格到新 具有所需功能的分子。早在2006年,美国宇航局就将一颗卫星送入太空,其通信天线是通过人工进化产生的。

“A Raspberry Pi computer, which acts as a brain, is wired up to the sensors and motors”

“充当大脑的草莓派计算机(注:一种简易掌上电脑)连接到传感器和电机”

Yet designing robots brings a challenging new dimension to the field: as well as bodies, they require brains to interpret information from their environments and to translate this into a desired behaviour. Much of the early work in evolutionary robotics addressed this problem by simply adapting a brain to a newly evolved body design. But intelligence isn’t simply a property of the brain; it also lies in the body. And in the 21st century, there has been a shift to simultaneously evolve both the robot’s body and brain. Although this complicates the evolutionary process, there is a large pay-off: devolving some intelligence to the body can reduce the need for complexity in the brain.

然而,设计机器人为该领域带来了一个具有挑战性的新维度:与身体一样,它们需要大脑来解释来自环境的信息并将其转化为所需的行为。进化机器人的大部分早期工作通过简单地使大脑适应新进化的身体设计来解决这个问题。但是智力不仅仅是大脑的属性;它也在于身体。在21世纪,机器人的身体和大脑同时进化。尽管这使进化过程复杂化,但有很大的回报:将一些智能交给身体可以减少对大脑复杂性的需求。

In 2000, Hod Lipson and Jordan Pollack at Brandeis University in Massachusetts, used this approach to evolve small robots capable of forward motion, which self-built using automated assembly techniques. Since then, rapid advances in materials, simulation and 3D-printing methods have vastly increased the potential range of robot designs. A decade later, Lipson and Jonathan Hiller, then both at Cornell University, New York, used the same principles to evolve self-building “soft robots”, machines made from compliant materials rather than rigid ones. Another milestone came in 2020, when Josh Bongard at the University of Vermont and his colleagues used a similar method to design living robots, or xenobots, made from frog cells.

2000年,马萨诸塞州布兰代斯大学的Hod Lipson和Jordan Pollack使用这种方法进化出了能够向前运动的小型机器人,这些机器人使用自动组装技术自行构建。从那时起,材料、模拟和3D打印方法的快速发展极大地增加了机器人设计的潜在范围。十年后,当时都在纽约康奈尔大学的Lipson和Jonathan Hiller使用相同的原理来发展自我构建的“软机器人”,这些机器由柔顺材料而不是刚性材料制成。另一个里程碑出现在2020年,当时佛蒙特大学的 Josh Bongard和他的同事使用类似的方法设计了由青蛙细胞制成的活体机器人或异种机器人。

Although each of these examples represents a noteworthy landmark in evolutionary robotics, they all have two shortcomings. First, none of these robots have sensors, so although they are capable of directed motion, they lack the ability to acquire information from their environment and use it to adapt their behaviour. Second, the robots are evolved in simulation and then manufactured post-evolution. This introduces a “reality gap”, a phenomenon infamous in robotics that results from inevitable differences between a simulation and reality. In other words, regardless of the fidelity of the simulator, the behaviour of physical robots is different from that of their simulated counterparts.

尽管这些例子中的每一个都代表了进化机器人技术中一个值得注意的里程碑,但它们都有两个缺点。首先,这些机器人都没有传感器,因此尽管它们能够定向运动,但它们缺乏从环境中获取信息并使用它来调整其行为的能力。其次,机器人在模拟中进化,然后在进化后制造。这引入了“现实差距”,这是机器人技术中臭名昭著的现象,是由于模拟与现实之间不可避免的差异造成的。换句话说,无论模拟器的保真度如何,物理机器人的行为都与模拟机器人的行为不同。

An obvious way around this second shortcoming is to skip the simulation stage and build and uate new evolved designs directly in hardware. This was first demonstrated by researchers at ETH Zurich, Switzerland, in 2015. They used a “mother robot” equipped with an evolutionary algorithm to autonomously design and fabricate offspring. These were then tested, with only those achieving the best results being sexted as designs to feed into the next generation. In 2016, Guszti Eiben at Free University Amsterdam, the Netherlands, and his team described a different approach. They used physical robots programmed with rules allowing them to “meet and mate”, triggering a production process to create a new “robot baby”.

克服第二个缺点的一个明显方法是跳过仿真阶段,直接在硬件中构建和评估新的演进设计。2015年,瑞士苏黎世联邦理工学院的研究人员首次证明了这一点。他们使用配备进化算法的“母机器人”来自主设计和制造后代。然后对这些进行测试,只有那些取得最佳结果的才能被选为设计,以供下一代使用。2016 年,荷兰阿姆斯特丹自由大学的Guszti Eiben和他的团队描述了一种不同的方法。他们使用编写了规则的物理机器人,允许他们“见面和交配”,触发生产过程来创造一个新的“机器人宝宝”。

The triangle of life
These developments laid the ground for ARE, a project that envisions a fully autonomous system through which robots equipped with sensors can be manufactured, adapt and evolve in the real world. Launched in 2018 and funded by the UK’s Engineering and Physical Sciences Research Council, it is a collaboration between Edinburgh Napier University – where I lead the Nature-Inspired Intelligent Systems group, which develops algorithms based on biological evolution to discover novel solutions to challenging problems – the University of York, the University of the West of England, Bristol, the University of Sunderland and Free University Amsterdam.

人生的三角
这些发展为ARE奠定了基础,该项目设想了一个完全自主的系统,通过该系统,配备传感器的机器人可以在现实世界中制造、适应和发展。它于2018年启动,由英国工程和物理科学研究委员会资助,是爱丁堡龙皮尔大学之间的合作——我在那里领导自然启发的智能系统小组,该小组开发基于生物进化的算法,以发现具有挑战性的问题的新解决方案——约克大学、西英格兰大学、布里斯托尔大学、桑德兰大学和阿姆斯特丹自由大学。

In ARE, we use an artificial genetic code to define a robot’s body and brain. Evolution takes place in a facility dubbed the EvoSphere, by putting each robot through a three-phase cycle – fabrication, learning and testing – that we call the “triangle of life”. In the first phase, new evolved designs are built autonomously. A 3D printer initially creates a plastic skeleton. Then, an automated assembly arm sexts and attaches the specified sensors and means of locomotion from a bank of pre-built components. Finally, a Raspberry Pi computer is added to act as a brain. It is wired up to the sensors and motors, and software representing the evolved brain is downloaded.

在ARE中,我们使用人工遗传密码来定义机器人的身体和大脑。进化发生在一个名为EvoSphere的设施中,通过让每个机器人经历一个三阶段循环——制造、学习和测试——我们称之为“生命三角”。在第一阶段,新的进化设计是自主构建的。3D 打印机最初会创建一个塑料骨架。然后,自动装配臂从一组预先构建的组件中选择并连接指定的传感器和运动装置。最后,添加了一台Raspberry Pi计算机作为大脑。它连接到传感器和电机,并下载代表进化大脑的软件。

Next comes the all-important learning phase. In most animal species, newborns undergo some kind of learning to fine-tune their motor control. This is even more pressing for our robots because breeding can occur between different “species”. For example, one with wheels might reproduce with another that has jointed legs, resulting in an offspring with both types of locomotion. In such situations, the inherited brain is unlikely to provide good control over the new body. The learning phase runs an algorithm to refine the brain over a small number of trials in a simplified environment. The process is analogous to a child learning new skills in a kindergarten. Only those robots deemed viable proceed to the third stage: testing.

接下来是最重要的学习阶段。在大多数动物物种中,新生儿都会经历某种学习来微调他们的运动控制。这对我们的机器人来说更加紧迫,因为不同“物种”之间可能会发生繁殖。例如,一个有轮子的可能会与另一个有关节的腿一起繁殖,从而产生具有两种运动类型的后代。在这种情况下,遗传的大脑不太可能对新身体提供良好的控制。学习阶段运行一种算法,在简化的环境中通过少量试验来优化大脑。这个过程类似于孩子在幼儿园学习新技能。只有那些被认为可行的机器人才能进入第三阶段:测试。

“The learning phase is analogous to a child learning new skills in a kindergarten”

“学习阶段类似于孩子在幼儿园学习新技能”

Currently, we are using a mock-up of the inside of a nuclear reactor for testing, in which the robot must clear radioactive waste, which requires it to avoid various obstacles and correctly identify the waste. Each robot is scored according to its success, and these scores are fed back to a computer. A sextion process uses these scores to determine which robots are permitted to reproduce. Then, software that mimics reproduction performs DNA recombination and mutation operations on the genetic blueprints of two parents to create a new robot for fabrication, completing the triangle of life. Parent robots can either remain in the population, where they can take part in further reproduction events, or be broken down into their constituent parts and recycled into new robots.

目前,我们正在使用核反应堆内部的模型进行测试,其中机器人必须清除放射性废物,这需要它避开各种障碍物并正确识别废物。每个机器人都根据其成功进行评分,并将这些分数反馈给计算机。选择过程使用这些分数来确定允许哪些机器人进行复制。然后,模仿繁殖的软件对两个父母的基因蓝图进行DNA重组和突变操作,创造出一个新的制造机器人,完成生命的三角。父机器人要么留在种群中,在那里它们可以参与进一步的繁殖活动,要么被分解成它们的组成部分并回收成新的机器人。

By working with real robots rather than simulations, we eliminate any reality gap. However, printing and assembling each new machine takes about 4 hours, depending on the complexity of its skeleton, so limits the speed at which a population can evolve. To address this drawback, we also study evolution in a parallel, virtual world. This entails creating a digital version of every robot baby in a simulator once mating has occurred, then training and testing them in virtual kindergartens and test sites. Although these environments are unlikely to be totally accurate representations of their real-world counterparts, they do allow us to build and test new designs within seconds and identify those that look particularly promising. Their genomes can then be prioritised for fabrication into real-world robots. What’s more, we have a novel breeding process that permits reproduction between a physical robot and one of its virtual cousins, which enables useful traits discovered in simulation to quickly spread into the real-world population, where they can be further refined.

通过使用真正的机器人而不是模拟,我们消除了任何现实差距。然而,打印和组装每台新机器大约需要4个小时,具体取决于其骨架的复杂程度,因此限制了种群进化的速度。为了解决这个缺点,我们还研究了平行虚拟世界中的进化。这需要在交配发生后在模拟器中创建每个机器人婴儿的数字版本,然后在虚拟幼儿园和测试地点对它们进行培训和测试。尽管这些环境不太可能完全准确地代表现实世界的对应物,但它们确实使我们能够在几秒钟内构建和测试新设计,并确定那些看起来特别有前途的设计。然后可以优先考虑他们的基因组以制造成现实世界的机器人。更重要的是,我们有一种新的繁殖过程,允许物理机器人和它的虚拟近亲之间进行繁殖,这使得在模拟中发现的有用特征能够迅速传播到现实世界的群体中,并在那里进一步完善。

In principle, the system we are developing could operate completely autonomously in an inaccessible environment or distant location. The potential opportunities are great, but we also run the risk that things might get out of control, creating robots with unintended behaviours that could cause damage or even harm humans. We need to think about this now, while the technology is still being developed. Limiting the availability of materials from which to fabricate new robots provides one safeguard. We could also anticipate unwanted behaviours by continually monitoring the evolutionary process and the evolved robots, then using that information to build analytical models to predict future problems. Ultimately, we need the ability to shut down the whole process. The most obvious and effective solution is to use a centralised reproduction system with a human overseer equipped with a kill switch.

原则上,我们正在开发的系统可以在人迹罕至的环境或遥远的地方完全自主地运行。潜在的机会很大,但我们也冒着事情可能失控的风险,创造出具有可能造成伤害甚至伤害人类的意外行为的机器人。我们现在需要考虑这一点,而技术仍在开发中。限制用于制造新机器人的材料的可用性提供了一种保障。我们还可以通过持续监控进化过程和进化的机器人来预测不需要的行为,然后使用这些信息建立分析模型来预测未来的问题。最终,我们需要能够关闭整个过程。最明显和最有效的解决方案是使用一个集中的复制系统,并配备一个配备了终止开关的人类监督者。

Some of the applications of ARE, such as terraforming, may seem quite futuristic, but our research could also bring more immediate benefits. As climate change gathers pace, it is clear that robot designers need to radically rethink their approach to reduce their ecological footprint. They may, for example, want to create new types of robots that are built from sustainable materials, operate at low energy levels and are easily repaired and recycled. These probably won’t look anything like the robots we see around us today, but that is exactly why artificial evolution can help. Unfettered by the constraints that our own understanding of engineering science imposes on our designs, evolution can generate creative solutions we cannot even imagine.

ARE的一些应用,例如地形改造,可能看起来很有未来感,但我们的研究也可以带来更直接的好处。随着气候变化的步伐加快,机器人设计师显然需要从根本上重新考虑他们减少生态足迹的方法。例如,他们可能想要创建由可持续材料制成的新型机器人,以低能量水平运行并且易于维修和回收。这些可能看起来不像我们今天在我们周围看到的机器人,但这正是人工进化可以提供帮助的原因。不受我们自己对工程科学的理解对我们的设计施加的限制的束缚,进化可以产生我们甚至无法想象的创造性解决方案。

Insights into evolution
“So far, we have been able to study only one evolving system [life on Earth] and we cannot wait for interstellar flight to provide us with a second. If we want to discover generalizations about evolving systems, we will have to look at artificial ones.” It is 30 years since evolutionary biologist John Maynard Smith wrote these words. Today, the Autonomous Robot Evolution (ARE) project is taking up that challenge. Although conceived to create robots that can reproduce and adapt (see main story), it also has the potential to shed light on evolution itself.

洞察进化
“到目前为止,我们只能研究一个进化系统——地球上的生命,我们不能等待星际飞行为我们提供第二个。如果我们想发现关于进化系统的概括,我们将不得不研究人造的。” 进化生物学家约翰·梅纳德·史密斯写下这些话已经30年了。今天,机器人自主进化 (ARE) 项目正在迎接这一挑战。尽管旨在创造可以复制和适应的机器人(见主要故事),但它也有可能揭示进化本身。

“Robotic experiments can be conducted under controllable conditions and validated over many repetitions, something that is hard to achieve when working with biological organisms,” says evolutionary roboticist and ARE team member Guszti Eiben at Free University Amsterdam in the Netherlands. Evolution in robots is also much faster than in many biological systems, so ideas can be tested more rapidly. But the real advantage is that robots allow researchers to do things that life can’t. You can give a robot two brains, for example, and you can manipulate the “genetic language”, the code that describes how a robot should be formed. When two robots “mate”, for instance, you can control the rules governing how their “genomes” recombine to produce offspring.

荷兰阿姆斯特丹自由大学的进化机器人学家和ARE团队成员Guszti Eibe说:“机器人实验可以在可控条件下进行,并经过多次重复验证,这在使用生物有机体时很难实现。”机器人的进化也比许多生物系统快得多,因此可以更快地测试想法。但真正的优势在于,机器人可以让研究人员做一些生活无法做到的事情。例如,你可以给机器人两个大脑,你可以操纵“遗传语言”,即描述机器人应该如何形成的代码。例如,当两个机器人“交配”时,你可以控制它们的“基因组”如何重组产生后代的规则。

Studying robotic evolution could give new insights into the processes that drive – or perhaps limit – evolution. Take interspecies breeding, once viewed by biologists as an evolutionary dead end. ARE provides an ideal way to investigate it because, unlike in nature, very different “species” can breed: legged robots can reproduce with wheeled ones, for example. Biologists are only just beginning to uncover the importance of hybridisation to evolution and robot studies could prove invaluable in accelerating our understanding, with practical implications for biodiversity and conservation.

研究机器人进化可以为推动或限制进化的过程提供新的见解。以种间繁殖为例,生物学家曾将其视为进化的死胡同。ARE提供了一种理想的研究方法,因为与自然界不同,可以繁殖非常不同的“物种”:例如,有腿的机器人可以用带轮的机器人繁殖。生物学家才刚刚开始发现杂交对进化的重要性,而机器人研究在加速我们的理解方面可能是无价的,对生物多样性和保护具有实际意义。

ARE is also hoping to shine new light on another fundamental property of evolution: natural sextion. Biological evolution is driven purely by the need to survive and reproduce, with mate sextion informed by observable physical or behavioural properties. Artificial evolution, by contrast, can be driven by goals defined by researchers, such as the need for robots to be energy efficient or to have a low-carbon footprint. Studies can then explore how such guided sextion affects the efficiency of the evolutionary process – or whether imposing specific goals limits the essential creativity of evolution.

ARE 还希望为进化的另一个基本特性:自然选择带来新的曙光。生物进化纯粹是由生存和繁殖的需要驱动的,配偶选择是由可观察的物理或行为特性决定的。相比之下,人工进化可以由研究人员定义的目标驱动,例如机器人需要节能或低碳足迹。然后,研究可以探索这种引导选择如何影响进化过程的效率——或者强加特定目标是否会限制进化的基本创造力。

“Robot evolution provides endless possibilities to tweak the system,” says evolutionary ecologist Jacintha Ellers at Free University Amsterdam. “We can come up with novel types of creatures and see how they perform under different sextion pressures.” It offers a way to use evolutionary principles to explore a rich set of “what if” questions.

“机器人进化为调整系统提供了无限可能,”阿姆斯特丹自由大学的进化生态学家 Jacintha Ellers说。“我们可以想出新颖的生物类型,看看它们在不同的选择压力下的表现如何。” 它提供了一种使用进化原理来探索一组丰富的“假设”问题的方法。