彭博电视播放人形机器人行业专题，未披露新的公司级订单、合作或业绩催化

【实时直播转录 · Bloomberg TV+】
来源:Bloomberg TV+(bloomberg 直播间 btv_us) · 标题:Bloomberg TV+
时段:2026-07-19 03:30–04:00(Asia/Shanghai),共 214 段
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[03:30] 🎼This hand, his knee and this head are all small feats of engineering decades in the making and.
[03:30] 🎼Together, they're about to take a massive leap forward human robot.
[03:30] 🎼Exuded out of the lab and into the real world of late not always on the best, but I'm not just here to show you funny robot.
[03:30] 🎼For the next half hour, even though I could easily because behind silly hats and dancing.
[03:30] 🎼Is a very serious, very expensive and very unproven bet that AI powered robots could one day transform.
[03:30] 🎼as we know it the interest in human order has been for long long time. What's changed, of course, is now we see that it's viable to commercialize.
[03:30] 🎼And start really applying some good business models AI clearly plays a large part in this.
[03:30] 🎼Tracking the code of physical AI, taking specialized artificial intelligence and putting it in a body with senses.
[03:31] 🎼And aators, very expensive and technologically complex aators It's a paradigm shift.😊
[03:31] That could set humanoids up to become a multi trillion dollar market by 2050, but for these massive figures to be realized.
[03:31] 🎼It won't be about party tricks or dance routines. It'll be about work, in homes, in hospitals.
[03:31] 🎼Trees, especially factories. when you really work with robots, you realize how challenging it is. There is a lot of what I sometimes call.
[03:31] humanoid hype out there like a lot of hype coming from the most influential figures in the tech industry, but hype.
[03:31] 🎼rerite the global labor market to do that you need that's get into.
[03:32] This robot runs.
[03:32] 🎼On AIWell, sometimes for this demonstration film.
[03:32] 🎼In 2025, Neo needed some help from real humans and was operated remotely by a man named Tin.
[03:32] 🎼not that tuuring but don't worry. we'll get to that later. you looking beautiful Daniel you your head.
[03:32] U this particular humanoid is made by a company called1 x a robotic startup founded by.
[03:32] 🎼This humanSo this is Neo, this is the gamma version so this is what we've been doing that.
[03:32] In home tests among employees with, this is the one that's doing my laundry and this is the last, let's call like the last engineering.
[03:33] The version that we're working on right now that's about to go into production is the unit that will go into the first customer homes.
[03:33] 🎼Essentially the entire behavior of Neo is AI in the sense that it takes images.
[03:33] Forces, tactile, and in general, all information that it has about the world in there's a big neural network.
[03:33] 🎼And out comes the actual motor commands all the way down to the forces and toques that it's exerting to be able to do everything from balance and walk to,
[03:33] 🎼Doing manipulation unlike a traditional robot, that's programmed to do a specific task the same way every time.
[03:33] 🎼A robot that uses AI should, in theory, anyways, learn to do things over time in unpredictable environments.
[03:33] 🎼It's sometimes called physicalical AI, a category that besides humanoid includes other general intelligence robots and driver.
[03:34] 🎼Cars basically it's anything that uses motors, sensors, cameras and AI models to make sense of the world around it.😊
[03:34] 🎼We are seeing a very clear correlation between the amount of data and how many different tasks we can do and how successful we are at this.
[03:34] 🎼And that is really the beauty of how AI has changed robotics, because there is no longer this limit where you need to write.
[03:34] Specific code for every task Everything is just data is just one of many humanoids.
[03:34] that have the tech in recent years towel laundry.
[03:34] 🎼comes down to two little letters which come with big checks AI.
[03:34] We've seen explosive interest in development of and investment in AI and a lot of people are C.
[03:34] On this moment as a way to work on all kinds of related things, including robots, it's a packed scene.
[03:35] 🎼With billions of dollars in investments in 2025 alone, and companies globally racking up huge valuations.😊
[03:35] 🎼People are betting big on humanoid robots because very high profile people involved in the world of humanoid robotics are talking it up.
[03:35] There's so many humanoid robots here and the As that they need are physical As.
[03:35] Chhiip makers like NVviDdia are excited for their chips to power the brains of these humanoids it needs a brain and Nvidia has a chip.
[03:35] 🎼For that that can go into the robot itself, then they have the software NviDdia has largely a monopoly for AI at GP.
[03:35] 🎼Us that go into data centers for training, large language and other AI models, so they have a stronghold there.
[03:35] 🎼Because just like generative AI tools, humanoid robotics models, real world models are also trained using the same tank.
[03:36] 🎼We just announced a new deep collaboration in the Boston Dynamics. They've got some very exciting AI and tech giant.😊
[03:36] 🎼Open AI, Google and Tesla are also betting big that there's a lot of money to be made by taking AI off a screen.
[03:36] Bing realAnd then you have Elon Musk who talks endlessly about humanoid robots.
[03:36] Everyone on earth is going to have one going want one his projections are even more let's say far fetch Ellon Musk says.
[03:36] 🎼That Opimus, Tesla's own humanoid robot, will be a product that can bring in $30 trillion of.
[03:36] 🎼And numbers like thats hype that.😊
[03:36] Showing up in ads, you see them at tech conferences, there are lots and lots of videos of them.
[03:36] SometimesYou'll see them operating in person, but they might feel like tethered to something. So I feel like you're both seeing.
[03:37] gressAnd you're also seeing the sense of it's not quite there yetpoli.
[03:37] And wherever it happens, I'm and H and on Turkey. this is bloomloberg.
[03:37] 🎼Yeah.
[03:37] 🎼
[03:37] This is my students.
[03:38] In our lab here, we call it the auto labb for Auto Lab. and so students are working on various projects.
[03:38] We have a number of robots in here to armed robot by Profess Ken Goldberg at UC Bley has been interested in what makes robots.
[03:38] 🎼a timeIn the last few years, there's been a lot of excitement about the potential of artificial intelligence, specifically with the.
[03:38] 🎼through of chat GT, which is a huge paradigm shift in the field of AI. And so that's the speculation. What is the G moment.
[03:38] 🎼robotsBut here's the thing language models or Lms that the kind.
[03:38] 🎼Behind Chaty BT are more book smart than a robot smart in Chay BT you're looking at a string of words.
[03:38] 🎼And then you're trying to generate what would be the next appropriate string of words that goes with that large language.
[03:38] 🎼el on Well mostly language Think of it like having access to a massive library of trillions of words.
[03:39] 🎼from the internet but a robot doesn't just generate words, it has to create motion.
[03:39] In a robot, what we need to see is a string of images, a video, and then we need to see a string of motions of command.
[03:39] 🎼They go to the robot to get it to do the right do that to from.
[03:39] Of data when it comes to humanoid robots, data is so, so important without data A.
[03:39] 🎼They that relies on AI doesn't know anything. You want a robot to do something like folding the laundry. For instance, you're going to have to have a ton.
[03:39] 🎼Of information about how laundry is folded soda can learn, see patterns in that data. How do you pick up a piece of fabric.
[03:39] Do you use articulated fingers, which are extremely difficult to build and how do you also hold on to things without squ?
[03:40] 🎼Ro from ands the problem almost none of that physical experience.
[03:40] 🎼No one has captured the motions of robots that go with the input.
[03:40] That were generated coming from a camera. So we have a gap, what I call the robot data gap.
[03:40] That gap is a challenge and an opportunity, especially for the ones betting on physical AI.😊
[03:40] 🎼Better AIThe need for data for robots is so big companies are rushing to create libraries made of both real and.😊
[03:40] ynSo people today are trying to collect this data to be able to train robots.
[03:40] The first one is simulation to be able to simulate environments and robots moving in them think of it like creating a hyper.
[03:40] 🎼The robot can learn its superpower is scale, thousands of robots in thousands of worlds all.
[03:41] 🎼the problem is virtual worlds and virtual robots can lack real world detail.😊
[03:41] 🎼Another way to train humanoids is harvesting existing video of the real world We do have a huge amount of videos on YouTube.
[03:41] Places that have show people manipulating things It's very difficult to get the full three dimensional structure.
[03:41] 🎼Of a scene from a two dimensional video theres one option for delivering real human interactions in real world.
[03:41] 🎼Where VR glasses and stylish outfits can turn these robots into a very expensive puppet.😊
[03:41] So there's someone behind the scenes behind the curtain who is controlling two arms, moving them around, and as that happened.
[03:41] 🎼The video images and the motions of the robot are recorded, and that provides data, but that's very, very slow.
[03:42] 🎼People out there doing that as we speak tele operation has been criticized for its use in demonstrations to drum up excitement.
[03:42] 🎼And but is also a powerful training tool tele.
[03:42] 🎼There's another option to let the robots just on the job Gold it the flywheel work.
[03:42] It's collecting data from its images and its motions, and then use that data by filtering it to train a larger model.
[03:42] Eventually, that new model becomes better than the original model nailing an A model and a training data that works.
[03:42] Amount for any robotics company hoping to make it in the humanoid biz.
[03:42] He is a robot for the home.
[03:42] We want every part of Neo and to be inviting and made of materials that you would want to have around your family or in your living room.
[03:43] Using soft cover and this fabric for the face covering.
[03:43] Build and train humanoid prototypes many1.
[03:43] 🎼A combination of training techniques, but when it comes to AI, things change fast When we visited1X in 2025.😊
[03:43] They relied on a combination of tools to train their humanoids, including teleoperation and human input Hi tu.😊
[03:43] Since then,1X says it increasingly uses something called a world model to train and.
[03:43] 🎼Their humanoids, and it's pulling from many of those data gap filling tactics. The video here on the left is generated.
[03:43] 🎼By the model, it's almost like a future vision of what the robot should do and on the right is the robot completing the action.
[03:44] Similar to other AI video creation tools, the world model pulls for a vast library of images and video to generate.
[03:44] 🎼A visualization of an action each time it generates the prediction of an action, it can learn from the resulting real world action.
[03:44] 🎼And in theory, help improve those predictions with every generation, kind of like a flywheel, remember?😊
[03:44] Of course, the actual goal of all these models and data is to get robots out of the lab and into the real world.😊
[03:44] They can continue to improve and when it comes to doing that, there's one place that's been faster than the rest.
[03:44] 🎼This is the first World humanoid robot Game taking place in an old Olympic ice skating rink in Beijing.
[03:44] 🎼Look at all these humanoids running, jumping, dancing.
[03:45] Competitors here are gathered from all over the world to put humanoids to the mechanical test.
[03:45] I think the games are really interesting, there's tons of really cool events that you don't really see normally, it's very different, very unique, very special.
[03:45] When I first met our robot, when I first interacted with it, it was a little scary because, you know, you have this like very heavy.
[03:45] P of machinery just running around and walking around next to you, then you try to like keep your distance and stay away from it, but as I've gone to know it and work with it.
[03:45] Develop a sort of like connection with it these games, which evolve everything from.
[03:45] 🎼are part of a large push in China to dominate the humanoid.
[03:45] China's unique interest in robots has to deal with governmentmbit for most.
[03:46] Part the fact that they see it as a strategic priority back in 2015 to meet in China 2025 plan.
[03:46] I hadd mentioned robots and lot Es and semiconductors as future growth drivers, so a lot of money has been poured into this area and continues.
[03:46] 🎼In 2025, China pledged 1 trillion yuan, about $140 billion US to promote.
[03:46] 🎼The advancement of emerging technologies, a big part of that being robotics that kind of support helped.😊
[03:46] 🎼40 Chinese companies all making human rights, a number that has continued to grow so much that officials have warned of a potential.
[03:46] 🎼BStill, China is currently dominating the humanoid robototics market much like they've done with EVs.😊
[03:46] Companies like Ajibot, Ubititech and Uniary, among others ship the most humanoids out of any country in 20.
[03:46] 🎼That's down in part to some serious chops when it comes to manufacturing cheaply and quickly which a good.
[03:47] 🎼If you're trying to get robots out in the world so much of the electronics industry has its supply chain.
[03:47] 🎼From the sort of circuit level throughout China and they have scale.
[03:47] 🎼Scale means better economics and it means ability to move quickly It's really vertically integrated.😊
[03:47] Just like with EVs, they have a lot of companies that make components for these human robots, whether it's sensors, AI chip.
[03:47] Batter, the fact that they're able to sort of supply the components for these robots.
[03:47] 🎼That advantage in scale may come in handy China already leads the world in the deployment of industrial robots as it races.😊
[03:47] 🎼Against a shrinking workforce but for engineering, training and investment to pay off, they're going to have to do something more.
[03:48] 🎼They're going to need to get a job, sorry robots.
[03:48] 🎼Western countries, all of them are attacking funding to Africa, and some of these programs were supported by.
[03:48] Funding coming from Western countries, like for human resources, for testing for.
[03:48] 🎼Transportation of samples and suddenly African countries have to think how to do.
[03:48] 🎼To fund money and to cover this gap, but it takes some time and I think the commitment of all all of these factors.
[03:48] 🎼A fueling this mistrusts that we see at community level, we value the support we got.
[03:48] 🎼And we are still getting from the US and we think that if we continue to work together and there is no reason for the US not to.
[03:48] 🎼Africa CDC, Africa CDC is the leader in terms of public health in Africa.
[03:49] Africa, a region rife with potential for growth and opportunities, but not without its challenges.
[03:49] Join us as we get to the heart of African business, politics and economics and ask the question, what's next?
[03:49] We're trying to build AI infrastructure so that it's close to where the users are and where the demand is going to be and in the case of African war.
[03:49] The dual goal is both connectivity and Comp premiers July 24 only on bloomberg.
[03:49] Invest, top executives, global innovators join me for in depth conversations with the biggest newsmakers.
[03:49] On the day's top stories In with Ha Linda Arm only on Bloomback.
[03:49] Wall Street Week for stories of capitalism from business, markets, economics, tech and climate more than what you need to know, it's what you need to think.
[03:50] About.
[03:50] I am an economist and I am studying the wellbeing implications of technological change.
[03:50] Technological anxiety and fear that technologies are going to take over our jobs is nothing new.
[03:50] 🎼What do we need to think about before deploying humanoid robots at scale would be.
[03:50] Worker involvement in the process, so how these robots will be deployed.🤢
[03:50] It would have huge implications for the quality of work that people will have.
[03:50] 🎼Robots aren't new They've been in factories for decades, they're very good at performing routine.
[03:50] 🎼Tasks and physical tasks tasks like welding, painting and assembling pops, but they are not.
[03:51] M purposepo. And that's where the humanoids come in. Basically, a humanoid robot is designed to do the tasks that human.
[03:51] 🎼is estimated that almost 2 million manufacturing jobs in the US alone might go unfilled by 2033.
[03:51] 🎼But if humanoids are going to be part of closing that gap, they need to actually well move and problem solve.
[03:51] 🎼Like a human alongside people. And right now, that's only happening in a limited number of very controlleds.
[03:51] 🎼Test run by some very big companies, For example, the BMW is currently running.
[03:51] Pilot and is reporting early data Other big names testing these robots include Amazon and Hyundai.
[03:51] 🎼These trials are crucial when it comes to figuring out where humanoids work and where they don't.
[03:52] Yes, data to fuel development while things like technology and regulation catch up.
[03:52] 🎼This is Patrick his company is one of many testing third party.
[03:52] 🎼We have been working with robots for the last 10 years, and we are at the forefront.
[03:52] 🎼Of piloting humanoids in our warehouse operations. And it really is fascinating to see the new technology.
[03:52] Ft in production in the warehouse environment GXO has found some cases that humanoids might be useful for.
[03:52] We're able to deploy the humanoids in that environment where they can really work for an entire.
[03:52] Batter life completing tasks being directed by somebody who is outside of the freezer and getting work completed without output putting.
[03:52] A teammates in an uncomfortable situation working in that cold environment for a long time.
[03:53] they're not the humanoids so far are not perfect, they damage product, they draw product.
[03:53] They may put product back in the wrong location, it is so important that our teammates are working alongside of them to do that.
[03:53] There is corrective action and audits done to make sure that the activity done by the humanoid is done accurately.
[03:53] Humanoids still face a number of engineering challenges that can create serious bottlenecks in a warehouse, they're slow for one.
[03:53] They have a limited battery life and picking up a box or handling an object, things that we take for granted required dexterity.😊
[03:53] It's something humanoids, which move using motors and actuators have a hard time replicating We do not see humanoids.
[03:53] In the next couple years, taking away many of the tasks that our teammates perform, the tasks are too complex.
[03:54] Important from a quality perspective to go entirely to humanoids and it's not really understood yet what the return on investment is going to.
[03:54] 🎼based on the finalized technologies that are coming out places where human.
[03:54] 🎼Rots might be deployed first, like factories and warehouses are already built for industrial robots but them in environment.
[03:54] For people like hospitals and homes might prove to be the bigger test in manufacturing you have.
[03:54] A very fixed production process, you can shield off human beings and you can let.
[03:54] 🎼The humanoid robot perform while in a hospital setting, it's a semi structured environment.
[03:54] Their are protocols and how processes should be done and how patients should be cared for, but still there are so many uncertainties.
[03:54] 🎼That can arise, in fact, it's about life and death type of situations and the cost of making.
[03:55] 🎼are very, very high. So will humanoids one day overcome their technical.
[03:55] 🎼Recoup investments and become indispensable companions, folding our laundry, watering our plants and building our.
[03:55] 🎼Or just be a novelty, giving way to robots of other shapes and sizes.
[03:55] And it is fascinating to me how much it has had a resurgence again and again over the years.
[03:55] 🎼And I'm thrilled about that. Im excited because I feel that this field has got many years of questions and.😊
[03:55] 🎼Research that needs to be done. So in some sense, I don't want to sound discouraging. I'm very actually optimistic.
[03:55] 🎼About the future. but I think it's important again, to just be realistic about the time frame In the meantime, I'll leave you with this.
[03:56] 🎼A humanoid robot waving to you from a balcony as a drone pulls slowly and dramatically away.
[03:56] 🎼That feels like a decent way to end for now.
[03:56] Are you a different president from youralda?
[03:56] It's a different time, there's fundamental lessons that I still hold close to my heart, but in terms of the practice.
[03:56] Things that you do. very few of those things apply any longer. How is mom she.
[03:57] She's in her 90s, but I always say she's 95 years old, she didn't get the memo she doesn't believe she's 95 years.
[03:57] And she's still very active, I mean I still get calls from her and she says, you need to do this, you need to do that.
[03:57] She doesn't let her age hold her back, but she try to advise you even as president of the Philippines.
[03:57] You know, I always learned something new with my mom in my family, she's the real politician and she said,N.
[03:57] My father was the intellect, he was a statesman and we always said that he lived in his head, you know, it was very, very.
[03:57] Very deep thinker, that's what I learned, my mother on the other hand, she has this uncanny ability.
[03:57] To connect with any kind of person, any person she runs into industry, King's presidents, anyone, her child.
[03:58] Good friend, she doesn't know how to fight. She's very bad at conflict.
[03:58] 🎼In case you missed this on the opening trades, the history of technology.
[03:58] 🎼Has not been more leisure right we all have dishwashers at home, but we don't spend less time in the kitchen so my thesis is that there's aggregate.😊
[03:58] 🎼Job opportunity rampM does enterprise payments, expenses, revealve, looks at labor market data, and they actually find.
[03:58] Companies that spend more on AI are hiring more so the data shows about six to 12 month lag, but 10% more workers, there's an interesting story there.
[03:58] 🎼Curn in the labor market, but then maybe not lower employment absolutelyutely I think there will be shifts right and we can't get away from that but.
[03:58] In aggregate, I think there's more opportunity, I think we're going to be doing more types of work and there's a lot of latent work that's just not happening and you still need to hire the young people.
[03:58] Is that also the message absolutely views on this I mean, Re's analysis said they were hiring more young people so10% over.😊
[03:59] 🎼12% young people don't miss the opening trade live every weekday.
[03:59] 🎼In watch B real yield every Thursday at 12 p Eastern right here on bloom context changes.
[03:59] 🎼Every weekday Bloer put the spotlight on a different aspect of global finance flows funds and the forces shaping markets on E.
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