The human hand is one of the most staggeringly sophisticated and physiologically intricate parts of the body. It has more than 30 muscles, 27 joints alongside a network of ligaments and tendons that give it 27 degrees of freedom. There are more than 17,000 touch receptors and nerve endings in the palm alone. These features allow our hands to perform a dazzling array of highly complex tasks through a broad range of different movements.

人手是人體中最精密、生理構(gòu)造最復(fù)雜的部位之一。人手由30多塊肌肉、27個關(guān)節(jié)、以及韌帶和肌腱網(wǎng)絡(luò)構(gòu)成，使其具有27個自由度。僅手掌就有17000多個觸覺感受器和神經(jīng)末梢。這些特征使我們的雙手做出各種不同的動作，從而執(zhí)行數(shù)不勝數(shù)、非常復(fù)雜的任務(wù)。

But you don't need to tell any of that to Sarah de Lagarde.

但是，你沒必要讓莎拉·德拉加德知道這些了。

In August 2022, she was on top of the world. She had just climbed Mount Kilimanjaro with her husband and was supremely fit. But just one month later, she found herself lying in a hospital bed, with horrific injuries.

2022年8月，她處于人生的巔峰狀態(tài)。她剛剛與丈夫一起成功攀登了乞力馬扎羅山，身體狀況極佳。但僅僅一個月后，她發(fā)現(xiàn)自己躺在病床上，傷勢嚴重。

While returning home from work, De Lagarde slipped and fell between a tube train and the platform at High Barnet station in London. Crushed by the departing train and another that then came into the station, she lost her right arm below the shoulder and part of her right leg.

德拉加德在下班回家途中，在倫敦的高巴尼特站滑倒跌入站臺與地鐵的縫隙中，先后被離站和進站的地鐵碾壓，失去了整條右臂和部分右腿。

After the long healing process, she was offered a prosthetic arm by the UK's National Health Service, but it offered her little in terms of normal hand movement. Instead, it seemed to prioritise form over functionality.

在經(jīng)過漫長的康復(fù)治療后，英國國民醫(yī)療服務(wù)體系為她安裝了一個假肢，但對于實現(xiàn)手部的正?；顒記]什么幫助，似乎形式大于實用性。

"It doesn't really look like a real arm," she says. "It was deemed creepy by my children."

她說：“假肢看起來并不像真實的手臂，我的孩子們看到它會感到害怕”。

The prosthetic only featured a single joint at the elbow while the hand itself was a static mass on the end. For nine months she struggled to perform the daily tasks she had previously taken for granted, but then was offered something transformational – a battery-powered bionic arm utilising artificial intelligence (AI) to anticipate the movements she wants by detecting tiny electrical signals from her muscles.

這種假肢只有一個肘關(guān)節(jié)，其末端的手是固定不動的。在九個月的時間里，她一直在艱難地完成以前習(xí)以為常的日常工作，但后來用上了變革性的產(chǎn)品——電池供電的仿生手臂，它利用人工智能來捕捉微弱的肌電信號，以此預(yù)測她想做出的動作。

"Every time I make a movement it learns," De Lagarde says. "The machine learns to recognise the patterns and eventually it turns into generative AI, where it starts predicting what my next move is."

“每當(dāng)我做出動作時，它都會學(xué)習(xí)”，德拉加德說道。“仿生手臂學(xué)習(xí)識別我的動作模式，最終轉(zhuǎn)變成生成性人工智能，以此來預(yù)測我的下一個動作”。

Even picking up something as simple as a pen, and fiddling it in our fingers to adopt a writing position involves seamless integration between body and brain. Hand-based tasks that we perform with barely a thought require a refined combination of both motor control and sensory feedback – from opening a door to playing a piano.

就連撿起鋼筆這種簡單的物體，手指調(diào)整成握筆姿勢，都需要人體與大腦之間的無縫整合。我們的雙手不假思索地執(zhí)行任務(wù)，這需要運動控制與感覺反饋的精確結(jié)合，開門或彈鋼琴也是如此。

With this level of complexity, it's no wonder that attempts to match the versatility and dexterity of human hands have evaded medical professionals and engineers alike for centuries. From the rudimentary spring-loaded iron hand of a 16th-Century German knight to the world's first robotic hand with sensory feedback created in 1960s Yugoslavia, nothing has come close to matching the natural abilities of the human hand. Until now.

人手這么復(fù)雜，難怪幾個世紀(jì)以來，醫(yī)學(xué)專家和工程師們制造不出在功能和靈巧方面堪比人手的機械裝置。從16世紀(jì)德國騎士使用的裝有彈簧的簡陋鐵手，到20世紀(jì)60年代南斯拉夫發(fā)明的世界上第一只具有感覺反饋能力的機械手，人手與生俱來的能力始終無與倫比，直到現(xiàn)在依然如此。

Advances in AI are ushering in a generation of machines that are getting close to matching human dexterity. Intelligent prostheses, like the one De Lagarde received, can anticipate and refine movement. Soft-fruit picking bots can pluck a strawberry in a field and place it delicately in a punnet of other berries without squishing them. Vision-guided robots can even carefully extract nuclear waste from reactors. But can they really ever compete with the amazing capabilities of the human hand?

人工智能的進步催生出在靈巧方面不斷接近人類的新一代機械裝置。例如，德拉加德使用的智能假肢能夠預(yù)測和完善她的動作。軟果采摘機器人在農(nóng)田里采摘一只草莓，將其小心翼翼地放入盛有其他漿果的果籃中，而不會把它們壓扁。視覺引導(dǎo)機器人甚至能夠小心地從反應(yīng)堆中提取核廢料。但是它們真能比得上能力非凡的人手嗎？

Embodied AI

具身智能

I recently gave birth to my first child. Within moments of entering the world, my daughter's small hand wrapped softly around my partner's forefinger. Unable to focus her eyes on anything more than a few inches in front of her, her hand and arm movements are limited, on the whole, to involuntary reflexes that allow her to grip an obxt when it is placed in her palm. It is an adorable illustration of the sensitivity of our dexterity, even in our earliest moments – and hints at how much it improves as we mature.

我最近生下第一個孩子。女兒剛來到這個世界時，小手輕輕地握住了我對象的食指。由于她看不清楚幾英寸以外的任何物體，手和手臂的動作基本出于無意識的反射，使她能夠抓取手心的物體。這可愛的一幕生動展現(xiàn)了我們在生命之初就已具備靈敏的觸覺，也預(yù)示著我們成年后，這種能力有多么大的提升。

Over the coming months, my daughter's vision will progress enough to give her depth perception, while the motor cortex of her brain will develop, giving her increasing control over her limbs. Her involuntary grasps will give way to more deliberate grabbing actions, her hands feeding signals back to her brain, allowing her to make fine adjustments in movement as she feels and explores the world around her. It will take my daughter several years of determined effort, trial, error and play to attain the level of hand dexterity that adults possess.

在接下來的幾個月里，我女兒視覺的改善足以讓她具備深度知覺，大腦運動皮層的發(fā)育將增強她對四肢的控制力。她的無意識抓取將被有意識抓取所取代，她的手將向大腦反饋信號，使她在感知和探索周圍的世界時調(diào)整自己的動作。我女兒需要多年的努力、試錯、游戲，才能達到成年人的手所具有的靈巧度。

And much like a baby learning how to use their hands, dexterous robots utilising embodied AI follow a similar roadmap. Such robots must co-exist with humans in an environment, and learn how to carry out physical tasks based on prior experience. They react to their environment and fine-tune their movements in response to such interactions. Trial and error plays a big part in this process.

就像嬰兒學(xué)習(xí)如何使用雙手一樣，采用具身智能技術(shù)的靈巧機器人會遵循類似的成長路徑。這種機器人一定會與人類在環(huán)境中共存，學(xué)習(xí)如何根據(jù)先前的經(jīng)驗來執(zhí)行體力任務(wù)。他們會對周圍環(huán)境做出反應(yīng)，并根據(jù)這種互動調(diào)整自己的動作，試錯在這一過程中起著重要作用。
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"Traditional AI handles information, while embodied AI perceives, understands, and reacts to the physical world," says Eric Jing Du, professor of civil engineering at the University of Florida. "It essentially endows robots with the ability to 'see' and 'feel' their surrounding environments, enabling them to perform actions in a human-like manner."

佛羅里達大學(xué)土木工程系教授埃里克·杜晶表示：“傳統(tǒng)的人工智能處理信息，而具身智能感知、理解物理世界，并對此做出反應(yīng)。具身智能從根本上賦予機器人‘觀察’和‘感知’周圍世界的能力，使他們能夠以人類的方式行動”。
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But this technology is still in its infancy. Human sensory systems are so complex and our perceptive abilities so adept that reproducing dexterity at the same level as the human hand remains a formidable challenge.

但是，這項技術(shù)仍處于起步階段。由于人類的感覺系統(tǒng)十分復(fù)??雜，我們的感知能力十分發(fā)達，因此再現(xiàn)與人手同樣的靈巧能力仍然是個嚴峻的挑戰(zhàn)。
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"Human sensory systems can detect minute changes, and rapidly adapt to changes in tasks and environments," says Du. "They integrate multiple sensory inputs like vision, touch and temperature. Robots currently lack this level of integrated sensory perception."

杜教授說：“人類的感覺系統(tǒng)能夠覺察到細微的變化，并迅速適應(yīng)任務(wù)和環(huán)境中的變化。人類的感覺系統(tǒng)整合了視覺、觸覺、溫度等多種感覺信息。機器人目前尚不具備這么先進的感官知覺整合能力”。

But the level of sophistication is rapidly increasing. Enter the DEX-EE robot. Developed by the Shadow Robot Company in collaboration with Google DeepMind, it's a three-fingered robotic hand that uses tendon-style drivers to elicit 12 degrees of freedom. Designed for "dexterous manipulation research", the team behind DEX-EE hope to demonstrate how physical interactions contribute to learning and the development of generalised intelligence.

但是，它們的先進程度正在迅速提高。DEX-EE機器人應(yīng)運而生，這種三指機械手由Shadow Robot公司與谷歌DeepMind公司聯(lián)合研發(fā)，采用肌腱式驅(qū)動器來產(chǎn)生12個自由度。DEX-EE機械手是專為“靈巧操作研究”而設(shè)計，研發(fā)團隊希望證明，物理互動如何為通用人工智能的學(xué)習(xí)和發(fā)展提供幫助。

Each one of its three fingers contains fingertip sensors, which provide real-time three-dimensional data on their environment, along with information regarding their position, force and inertia. The device can handle and manipulate delicate obxts including eggs and inflated balloons without damaging them. It has even learned to shake hands – something that requires it to react to interference from outside forces and unpredictable situations. At present, DEX-EE is just a research tool, not for deployment in real-world work situations where it could interact with humans.

三根手指都裝有指尖傳感器，可提供它們所處環(huán)境的實時三維數(shù)據(jù)，以及有關(guān)自身的位置、力量、慣性的信息。該機械手能夠抓取和移動易碎的物體而不會造成損壞，例如雞蛋、充氣氣球。它甚至學(xué)會了握手，這需要它對外部力量和意外狀況造成的干擾做出反應(yīng)。目前，DEX-EE機械手只是一種研究工具，而不是用于部署在現(xiàn)實世界中可與人類互動的工作場景。

Understanding how to perform such functions, however, will be essential as robots become increasingly present alongside people both at work and at home. How hard, for example, should a robot grip an elderly patient as they move them onto a bed?

但是了解如何執(zhí)行這類功能非常重要，因為機器人越來越多地與人類一同出現(xiàn)在工作場所和家庭。例如：當(dāng)機器人將老年患者攙扶到床上時，應(yīng)該使用多大的抓取力？
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One research project at the at the Fraunhofer IFF Institute in Madgeburg, Germany, set up a simple robot to repeatedly "punch" human volunteers in the arm a total of 19,000 times to help its algorithms learn the difference between potentially painful and comfortable forces. But some dexterous robots are already finding their way into the real world.

德國馬格德堡的弗勞恩霍夫IFF研究所開展了一個研究項目，他們組裝了一個簡易機器人，反復(fù)“擊打”人類志愿者的手臂總計19000次，從而幫助機器人算法學(xué)習(xí)可能引起疼痛感和舒適感之間的力量差異。但是一些靈巧機器人已經(jīng)進入了現(xiàn)實世界。

The rise of the robots

機器人的崛起

Roboticists have long dreamed of automata with anthropomorphic dexterity good enough to perform undesirable, dangerous or repetitive tasks. Rustam Stolkin, a professor of robotics at the University of Birmingham, leads a project to develop highly dexterous AI-controlled robots capable of handling nuclear waste from the energy sector, for example. While this work typically uses remotely-controlled robots, Stolkin is developing autonomous vision-guided robots that can go where it is too dangerous for humans to venture.

機器人專家一直以來的夢想是，讓自動機器的仿人靈巧性足以執(zhí)行人類不愿意做、具有危險性、重復(fù)性的任務(wù)。例如，伯明翰大學(xué)的機器人技術(shù)教授魯斯塔姆·斯托爾金負責(zé)一個項目，旨在研發(fā)靈巧性高、受人工智能控制的機器人，它能夠抓取能源領(lǐng)域的核廢料。這項工作通常使用遙控機器人，但斯托爾金正在研發(fā)自主性視覺引導(dǎo)機器人，它們可以前往對人類來說太危險的地方去探險。

Perhaps the most well-known example of a real-world android is Boston Dynamics' humanoid robot Atlas, which captivated the world back in 2013 with its athletic capabilities. The most recent iteration of Atlas was unveiled in April 2024 and combines computer vision with a form of AI known as reinforcement learning, in which feedback helps AI systems to get better at what they do. According to Boston Dynamics, this allows the robot to perform complex tasks like packing or organising obxts on shelves.

現(xiàn)實世界中最著名的人形機器人典范可能是波士頓動力公司的Atlas，2013年它的運動技能令世界為之矚目。最新一代Atlas于2024年4月問世，它結(jié)合了計算機視覺與強化學(xué)習(xí)人工智能技術(shù)，其反饋機制有助于人工智能系統(tǒng)提高它們的工作能力。據(jù)波士頓動力公司透露，這能使機器人執(zhí)行復(fù)雜的任務(wù)，例如打包或整理貨架上的物品。

But the skills required to perform many of the tasks in human-led sectors where robots such as Atlas could take off, such as manufacturing, construction and healthcare, pose a particular challenge, according to Du.

杜教授表示，Atlas這類機器人可能在制造業(yè)、建筑業(yè)、醫(yī)療保健等人類主導(dǎo)的領(lǐng)域取得成功，但它們執(zhí)行各種任務(wù)所需要的技能是個嚴峻挑戰(zhàn)。

"This is because the majority of the hand-led motor actions in these sectors require not only precise movements but also adaptive responses to unpredictable variables such as irregular obxt shapes, varying textures, and dynamic environmental conditions," he says.

他說：“這是因為在這些領(lǐng)域，手部主導(dǎo)的大多數(shù)動運動動作不僅需要動作精確，還需要對不可預(yù)測的可變因素做出適應(yīng)性反應(yīng)，例如：不規(guī)則的物體形狀、富于變化的紋理、動態(tài)的環(huán)境條件”。

Du and his colleagues are working on highly-dexterous construction robots that use embodied AI to learn motor skills by interacting with the real world.

杜教授和他的同事正在研發(fā)高度靈巧的建筑機器人，它采用具身智能技術(shù)，通過與現(xiàn)實世界進行互動來學(xué)習(xí)運動技巧。
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At present, most robots are trained on specific tasks, one at a time, which means they struggle to adapt to new or unpredictable situations. This limits their applications. But Du argues that this is changing. "Recent advancements suggest that robots could eventually learn adaptable, versatile skills that enable them to handle a variety of tasks without prior specific training," he says.

目前，大多數(shù)機器人都會接受特定任務(wù)的訓(xùn)練（逐個進行），這意味著他們很難適應(yīng)新的或不可預(yù)測的情況，這限制了它們的用途。但杜教授認為，情況正在發(fā)生變化。他說：“最新的研究進展表明，機器人最終可能學(xué)習(xí)適應(yīng)性的通用技能，使他們不必事先經(jīng)過專項訓(xùn)練，就能勝任各種任務(wù)”。

Tesla also gave its own humanoid robot Optimus a new hand at the end of 2024. The company released a video of the bot catching a tennis ball in mid-air. However, it was tele-operated by remote manual control, rather than autonomous, according to the engineers behind it. The hand has 25 degrees of freedom, they claim.

2024年底，特斯拉也給自家的人形機器人擎天柱（Optimus）安裝了一只新型機械手，并發(fā)布了一段它在半空中接住網(wǎng)球的視頻。然而，負責(zé)研發(fā)的工程師指出，該機械手是由手動遙控器進行遙操控的，而不是自動運行的。他們聲稱該機械手有25個自由度。

But while some innovators have sought to recreate human hands and arms in machine form, others have opted for very different approaches to dexterity. Cambridge based robotics company Dogtooth Technologies has created soft fruit-picking robots, with highly dexterous arms and precision pincers capable of picking and packing delicate fruits like strawberries and raspberries at the same speed as human workers.

有些創(chuàng)新者試圖以機械的形式再造人手和手臂，但也有創(chuàng)新者采取了截然不同的方式來實現(xiàn)靈巧性?？偛课挥谟鴦虻臋C器人公司Dogtooth Technologies制造出軟果采摘機器人，它擁有高度靈巧的手臂和精密的鉗子，能夠以人工同樣的速度采摘和包裝草莓、覆盆子等嬌嫩的水果。

The idea for the fruit-picking robots came to co-founder and chief executive Duncan Robertson while he was lying on a beach in Morocco. With a background in machine learning and computer vision, Robertson wanted to apply his skills to help clean up the litter on the beach, by creating a low-cost robot which could identify, sort, and remove detritus. When he returned home, he applied the same logic to soft fruit farming.

水果采摘機器人是該公司的聯(lián)合創(chuàng)始人、首席執(zhí)行官鄧肯·羅伯遜躺在摩洛哥的海灘上時想出來的。羅伯遜擁有機器學(xué)習(xí)和計算機視覺方面的知識背景，他希望運用自己的技術(shù)來幫忙清理海灘上的垃圾，于是發(fā)明了一種可以識別、分揀、清理垃圾的低成本機器人。他回家后將同樣的邏輯應(yīng)用于軟果種植領(lǐng)域。

The robots he developed along with the team at Dogtooth use machine learning models to deploy some of the skills that we as humans possess instinctively. Each of the robot's two arms has two colour cameras, much like eyes, which allow them to identify the ripeness of the berries and determine the depth of each of the target fruits from its end "effector", or gripping device.

他與Dogtooth公司的團隊共同研發(fā)的機器人，利用機器學(xué)習(xí)模型來施展我們?nèi)祟惻c生俱來的部分技能。機器人的雙臂各有兩個彩色攝像頭，像眼睛一樣識別漿果的成熟度，測定每只目標(biāo)水果與末端“執(zhí)行器”（即抓取裝置）之間的距離。
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The robots map the dispersal and arrangement of ripe fruits on a plant and turn this into a sequence of actions, with precise route planning necessary in order to guide the picker arm to the fruit's stem in order to make a cut.

機器人繪制出成熟水果在植株上的分布和排列圖，并將其轉(zhuǎn)化為一系列動作，精確的路線規(guī)劃是必要的，以便引導(dǎo)采摘手臂對準(zhǔn)果柄進行切割。
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Dogooth's robot's arms each have seven degrees of freedom, the same as the human arm, meaning these appendages can manoeuvre well enough to find the optimal angle for reaching each berry without damaging others still on the plant. The grasping device then gently grips the fruit by the stem, passing it into an inspection chamber before carefully placing the berry in a punnet for distribution. Another strawberry-picking system, created by Octinion, uses soft grippers to grasp the fruit as it transfers it from plant to basket.

Dogtooth機器人的手臂就像人類手臂一樣各有七個自由度，這意味著這些機械臂可以靈活自如地調(diào)整到摘取每顆漿果的最佳角度，且不會損傷植株上其余的果實。隨后，抓取裝置會輕輕地握住果柄，把漿果送入檢測室，然后小心翼翼地把漿果放入果籃以便進行配送。還有一種由Octinion公司研發(fā)的草莓采摘系統(tǒng)，它利用柔性抓取器采摘水果并放入果籃中。