Leading thinkers at Davos 2026 discuss the rise of physical AI in cars, drones, and digital companions, questioning which tasks should stay human‑directed.
At Davos 2026’s “Living Autonomously,” robotics leaders argued that physical AI is shifting from hype to measurable value, but real-world deployment remains the hard part. Jake Loosararian said customers are forcing a new discipline: “AI, AI, AI—what’s the ROI?” That pressure elevates robotics that can create “data sets the customers never had before,” such as infrastructure health, enabling predictive maintenance and then making robots smarter through feedback loops.
MIT’s Daniela Rus emphasized co-designing robot “body and brain” for specific tasks, using AI to rapidly create custom architectures. She distinguished physical AI from large language models: it embeds physics, learns transferable skills (not “task in context”), and can keep adapting after deployment. Rus also cautioned that manipulation lags mobility: without skin-like sensing, dexterous household robotics remains expensive and unscaled—“I can give you a robot that will fold your laundry…but it might cost you half $1 million.”
Mech-Mind’s Tianlan Shao highlighted accelerating adoption: 10,000 robots shipped in one year, surpassing the prior eight years combined. He argued robots need not match human dexterity to be useful, advocating “graduate autonomy” and clear safety boundaries, plus anomaly detection and human intervention. The panel agreed near-term wins are task-specific robots in controlled settings, not general humanoids.
Hello, I'm Jamie Heller. I'm the editor in chief of Business Insider, and we're here today for a panel called Living Autonomously. It's about robotics, robots and living with them. And we have three terrific panelists who are in the thick of it. Here is Jake Lucier, co-founder and chief executive officer of Gecko Robotics. We have Daniela Rus, who's the director of computer science and artificial Intelligence Laboratory at MIT, and we have Tianlin Xiao, who's founder and chief executive officer of robotics. So thank you all for being here. Thank you. And let's just get started. So many advancements on robotics in the last many years, in the last few years. I'd like to each of you, please tell me what you think is just the most exciting thing in in very recent last 12 months. Jake, why don't we start with you?
Yeah. The most exciting thing for us has been a hyper focus from the actual users of these robots. And and the AI models that help to power the robots effectiveness and what to do with the information data. It's been a hyper focus on okay, AI, AI, AI, what's the ROI of AI? And that actually leads you down a path of interrogating the information and data sets that feed the model, which then allows for you to interrogate, do we actually have the information data sets that we need to drive the ROI for all the investments that are being made right now, into into artificial intelligence. And so that actually leads you down a path to more and more of a physical realm. And so this is why physical AI has been a topic that you've just heard so much about. And so for us, you know, at at gecko, what we focus on is gathering information and data sets from a bunch of different robots that we make, and then also that we integrate with and pulling all that data set into one source of truth called cantilever, that helps to make different kinds of decisions using information. Data sets never existed before. But this is a this is a trend that's occurred because AI has honed this question of what sorts of decisions can I make or can I not make? And what's missing for me to be able to take advantage and have lots of ROI? From the promise that is, that we've all been hearing about with artificial intelligence.
And is the data, the data that helps robots think and behave and or the data that robots can gather to help companies.
We've we've, it's been both, but it's actually been the, the data sets that the customers never had before. So in our case, it's diagnosing the health of the built world is what we call it. So it's how healthy is your bridge or your power plants or your, your, your ship or whatever the physical structure is you're building, and then using that information data to help to drive optimizations, longevity of the asset, predicting failures, those sorts of data sets. And then, you know, and then we'll use the information data sets that our robots are collecting while they're gathering that data that's driving, you know, billions of dollars of returns for customers to actually make the robots smarter, make the robots, be able to live more autonomously and, and, and, that creates a very interesting possibility for foundation models in these environments that you typically just don't get information from. So we start with the, the data that can drive ROI. And then how that can make our robotic systems more powerful than anyone else's.
Got it. Daniela, what's been most exciting for you in your lab?
Very hard to choose one thing, but I will say we are focused on expanding robot capabilities. And that means expanding both the robot body and the robot brain. Because the body is important, the robot can only do what it's it's body is going to be able to do. And then for that body to do things, it needs to have a good brain. And so, right now we have, new, advances in designing or co-designing robot bodies and brains together, according to task specifications using AI. And so that means that in a few hours, you can actually get a custom robot that could then be manufactured, at industrial grade in a few days. And this solves a very important problem, which is that right now we have certain fixed architecture robots, and we kind of have to adapt the tasks that we assign the robots to the architecture. Now we can get the robot architecture to be customized and adapted to the task. And along these lines, I would say we're working with new materials, we're working with soft materials, we're working with some AI design materials for the body part and for the brain part. We are developing physical AI, which is a different kind of AI than the large language models you all are using today. It's an AI solution that has embedded in it and understanding of the physics of the world, which large language models do not, and also an AI solution that provides other properties. It allows the robot to learn the task rather than a task in context. When you learn a task in context with much of today's solutions, that means that for every new context, you have to redo the training. If you learn the skill, which is how humans learn, then you can apply that skill in many different contexts. And we also have more adaptive solutions. So in other words, today's models are frozen after training. That means when you deploy them in the wild, they cannot continue to learn. But with our physical AI solutions, we can get AI solutions that continue to adapt after training based on the inputs that they see. So this is extraordinary. It's opening so many opportunities.
And is it something like we're just at the cusp of it, or is it really starting to happen? Like do you just where are we on the arc of this science?
Well, it's happening. In fact, in my lab, we have been studying these questions for many years now. And, our company, liquid AI is already bringing these physical AI models to business. And, it's it's providing small, compact models that use a technology we call liquid networks that run on device. That means you don't you don't need to do the cloud calls. So that means energy efficiency. It means privacy. And it also means, capability without the risk of latency.
Wow. Okay. So Tianlin, let's hear from you. How fast are things going and what's what's most exciting in your company right now?
Yeah. Over the last 12 months, we delivered more than 10,000 intelligent robots. So that number is more than the first eight years of our company combined. So I see a very clear trend of acceleration, of adoption of intelligent robots. So physical AI, is turning from future vision or just some concept to a real world, very helpful product. So, I think, it's always very difficult to start. So the first, the first 10,000 units, actually took us eight years, but then the second 10,000 units took us only one year. Yeah. So I see a very clear trend. And in technology, we, last year. So we found out that something, we thought was very, very difficult now become reachable. Yeah, because of many reasons, like improvements in infrastructure. Yeah. So including simulator, more available real world data. Yeah. And advancements in, AI models. Yeah. So, so I'm very, very optimistic and, confident that physical AI and not just humanoid, but physical AI empowering all kinds of robots, will do, very impactful thing in several hundred days.
Can you, when you say you did something that very that seemed very difficult and you got it done, did this have to do with the 3D vision? Can you just it might be a little complex, but can you share? Yes.
Okay. Yeah. Say we want to we want a robot to grab something from a door. They open the door, take a look at what's inside and bring it up. Yeah. So this thing, we we humans get that ability? Probably at three. Yeah, but that's quite, difficult, so. But now. So we can, train the so-called, world model like thing. Yeah. So, aligning every everything, including vision, including robot vision, robot motion. Yeah. Aligning everything in one specific space and, controlling the, so tagging robot, what to do. So, so that's something that thing is not so, imaginable just a few years ago. But but now I think it's within our reach already.
Okay. Did you want to get in here?
I just wanted to add that there are so many extraordinary applications and deployments that we already have in the world today, and are bringing value and contribution to people. So, for instance, our company invented technologies, is automating port operations. It's moving containers. And we have entire fleets of robots that operate 24 over seven without without the need of, human drivers. Yet human drivers are also in the loop, to step in when the weather is bad or when there is, when there is a lot of need for, for movement. And, this is a very interesting, way to think about robots as collaborators, as, as tools that we bring into the work environment that can take on some of the work. And in particular, ports are really notorious for not having enough drivers, ports. Also, in general, there is a lack of truck drivers around the world. So bringing autonomy, bringing robotic concepts, to this industry, is important. It means that goods move much faster. And I also wanted to give the example of the company symbiotic. Symbiotic, automates storage systems. And, and they move boxes, they depoliticise and palletize, and they move, millions of boxes every day. And, through this operation, they're lowering the cost of food. They're speeding. How? Food gets from one place to another.
It's fascinating industrial applications. Let's get into, like, maybe more where, like, people not working are are like, whether it's hospitals or hotels or, like, workers in the field, like, can how how how safe are they like how what are the problems still facing like semi-structured environments as opposed to super structured environments?
Yeah, I guess I can start. Yeah. A lot of times maybe we focus on, you know, physical physical intelligence and robotics and in like very like specific. You're doing like, certain functions or like, you know, it's pretty easy. Like they've been autonomous vehicles in mining, for example, with vehicles taking, you know, ore and material from a mine like to, to, to refining, like, you know, these kind of systems have been around for a long time, I think. I think the big change is happening is in these sorts of, because because robotic systems are becoming, way more intelligent. And I think the right, the right way to think about this is not generalized, but actually more specific. I call it droid over humanoid. It touches on this like really big problem of, deployment. And I think Andreessen Horowitz actually came out with a really good article about deployment is the big problem right now for robotics in terms of the ability for it to begin to, to make really large impacts. And for there to be a clear roadmap. I think that's a big problem. Typically, robotics companies will make a single product and want to make a bunch of that single product, and then they they end up creating a path towards, basically, they create a potential path towards just like, you lose your unfair advantage. It just becomes commoditized. So the key is actually, how do you know what's the next sorts of robotic systems to make on that droid onto like that, that leads you into the the more generalized humanoid. And I think this is where like very specific. And we talked about this very like more specific robotics, that feed into systems that help companies, whether it's like make kilowatts or make barrels per day or, or or, you know, basically oriented towards maybe it's like moving shipping containers faster or for Amazon, it was like, how do I get two day shipping, robotics that can provide information and data sets that feed into helping a customer and a worker be able to accomplish the main job of that, of that, the business outcome that perpetuates the cycle of, okay, if I had a robot and data set that could do this or get this or get that, it creates a really good roadmap for making more and more robotics. So for our company, we actually don't sell robots. We make we make our robots and we deploy them into the environments for our customers. We figure out how to make them smarter, how to make them better and collect better information and data set without locking in. A type of robotic platform and then having to, you know, continue to upgrade that as systems and hardware get smarter. So I think that's, you know, that's a really important I think paradigm shift for most roboticists is you have to learn about the environment. So you have to forward, deploy and build your robots as close to the environment as possible. And that gives you the information and data set that doesn't exist anywhere on the internet, anywhere on YouTube about what these environments are like. And so that's that's the key.
What do you see as.
So I would just agree and add to that that there are some real technical challenges that have to do, with if you're in a, in an unstructured environment, there's a long tail of situations that you haven't, you haven't, tuned the robot for. And so learning about what the long tail is and learning how to deal with each of those situations is a challenge. From a technical point of view. Perception, the ability of the robot to correctly understand its work, its world, is a challenge. We're pretty good at getting robots to move in the world. We still have challenges with manipulation, with handling the world, the world. And, and in order to make progress there, we need better sensors. Our robots don't have the analogous of skin like sensing. And so there are plenty of challenges of technical challenges. And yet if we look at what the machines can do today, we can come up with a wide range of applications where technology is suitable. So an important thing to keep in mind when deploying and developing robotic solutions is to ask, what does the application need? And is the technology a good fit for that application? Or are there situations where that that the application needs but the technology is not ready for?
So when you put these 10,000 products out into the world, what's your biggest concern of the risk? How could it go wrong? What could what could actually hurt your customer? And how do you protect against that?
Yeah. So, sometimes I like to compile intelligent robots with more familiar tools that we use in daily life, like cars or chainsaw. Yeah. So, we have a very clear rules about where should we use them? Say, who should be able to use that? And they, what do we do if something goes wrong? Yeah. So, so so when it fails, how how do we minimize the consequences? Yeah. So. So, of course, the cars or chainsaws or electric trimmers can also cause others. Yeah, but that doesn't prevent them from being very useful. So I would say, we need the same thing. Clear boundary. Very well definitions. And, they rules. Yeah. So for example, in factory floor, in, logistic centers, our systems are already helping, workers. They're moving around curtains, moving around sets and also doing many, assembly, welding, screw driving, many of these things. So the safety standard. So it's very clear who should be able to touch it. Yeah. And such application doesn't involve direct human interaction. Yeah. It's not like helping the elderly or raising up the child. Yeah. So,
You're saying you're you you're not doing that currently. Not currently.
So currently not. Yeah. So actually we don't have to wait until humanoid are working among us and interact directly with all human beings. Before we deploy the hundreds of millions useful robot, especially, in, manufacturing, in logistics and some service industry. If we think about the physical work carried out today by the human, the vast majority of them actually, happen in a relatively controllable environment. The definition of the task is quite clear and doesn't involve direct interaction of, human. Yeah. So so that's the task, I think, in next few hundreds of days, say robots can be very helpful.
I mean, I do think I want to just to hold the point is just that I think there is a big debate. Oh, is AI just another tool? Let's just or is AI just like super powerful on another level? And it's not just like, oh, a car. Like we have to be extra careful in how much power are we giving these robots? So just something to think about. But go ahead, Jake.
Yeah, well, I think I think one thing I'd love to get to as well is like, what's the role of Teleoperation? And how should the average person that is being told by these tech leaders, potentially on this, on this panel here to that robot revolutions are coming in three years and they're going to have robots folding your laundry and dishes. ET cetera, et cetera, et cetera. And the dirty little secret, of course, is that you know. Well, sure, if you want to pay $40,000 for that and you want someone to be able to see what's going on in your house, maybe you're getting out of the shower and you know, there's someone tele operating your robot. So I think I think there's like an importance, like there's there's a lot of there is autonomy for certain tasks. But for the majority of the case, for humanoids, it's learning in the environment. And it has to do that with Teleoperation. And there's a funny little video.
What's teleoperation just.
Okay, so so basically it's someone with a headset that's like helping to maneuver. Like when you see these videos online, it's there's someone operating the robots for the most part. And there's even a funny video where someone takes the headset off, like somewhere behind a closed door. And then the, the robot, you know? Yeah. It's just like it goes like that and it like, falls over, you know, so I think it's just like, important, the average individual that's like thinking about living autonomously and robots among us. It's like they don't really realize, you know, how teleoperation is a prerequisite for full autonomy because we're still learning the environment. And I also think it also should should begin to ask the question, okay, if you if you have to understand the environment that the robots are being deployed in, that actually should that actually should allow for roboticists to begin to think of themselves not just as how can I get information about how the robots performing, but how can I get information about how, what sorts of actions are being taken at the at the, at the customer? So how do I make steel, for example, should be a thing that roboticists begin to think more about. You know, I'm not thinking about.
Like, really?
Yeah. Like, you have to understand the environment. And why am I turning this valve during because of this alarm or, you know, like, I'm thinking more industrial applications. But of course, this applies to, to, you know, the more normal, more normal things. But, you know, as robots begin to understand and learn the environment that they're in, they're also learning, understanding the things that humans and the subject matter expertise by which a lot of them are retiring in these sectors that I'm referring to, what sort of actions they take and why. And that actually is very important for roboticists to understand. There's a lot of power in understanding the physical world and creating the brain, the world model, the foundational model. And so I think that should drive industries. Actually.
The other thing I would add to that is that there is a real gap between getting a robot to do a task in a research setting. And so, I can give you a robot that will fold your laundry and load your dishwasher, but it might cost you half $1 million. Right? So then to get from that to a, a deployment where you have a few tens of robots that operate in some in some, specified settings, and to really fully scaling up the solutions. And so, it takes it takes a lot of hard work to go from research lab to physical demonstration and to a truly scaled up solution, especially when it comes to manipulating the world.
So talking about the manipulation, again, I'm you're the experts, not I. But coming to Davos for years, you see that little dog robot walking on the street. And that that doesn't have a brain isn't isn't the breakthrough here that we're building robots that are going to be able to think and reason more on their own.
Well, exactly. So, we are going to so we need robots that look at the world and understand that there is a step here and the the surface might be slippery and could adapt in situ. That requires that the robots have a brain that has the capability to perceive the the world and to very quickly adjust its motions in order to respond to the world. And so we're beginning to develop the brains of robots that can do this kind of exceptional navigation. In fact, the there are some exceptional examples of robots that can hike a trail. And so if you think about how complicated it is to hike a trail, you have to look at where you put your step. How do you balance the rest of your body. And so these kinds of, mobility advances are already happening. What's difficult for manipulation is that we don't actually have skin like sensors that are able to tell us the kind of detailed information that my fingers get when I, I rotate this glass, I don't even look at it, but I feel it. I sense forces and torques. I sense them really fast and with a high degree of, of accuracy. And, and then I can I can do very dexterous, tasks. So we're lacking the sensors that give us this level of dexterity for robot hands. We can do other things so we can take the glass with robots and put it from here to there. And this is enabling all the warehousing and logistics progress we have seen.
I'm going to come to questions in just a minute. But now, just today, it's that Elon Musk is going to be here. Obviously robotics are a huge part of Tesla. If you were each able to ask him one question, what would you ask him?
I would ask him, when can I buy all your robots?
Like.
Yeah, I think I think, well, can I expand that for just a second? Okay. So what I, what I mean by that is all the things we're talking about, there's like acceleration of learning that's going on and the infrastructure that's coming. I think we're underestimating the impact that has in terms of the compute and the energy. By the way, I don't think we're modeling those inappropriately, or other sorts of rare materials, rare earth materials. But I think that, it's just super important to understand that, you know, all the things we're talking about, about the the really exciting problems to overcome with robotics, teleoperation, etc., etc., etc. there is an incredible importance to figuring out how to get the ROI from the robot and a system that can get that, and then like the ones that are quickest at the adoption and deployment, earlier than you probably should, you know, you should expect you don't need like a million miles. You know, you don't need the, the you you, you can get away with, not perfect, actions by robots, if you do it on specific tasks. So. Yeah. So I think there's from a labor perspective, from a demand, of, whether it's energy or materials, robotics and humanoids in particular are going to be game changing and it's going to completely upend, you know, the, the, the economic landscape, for companies, you know, and then and then also the production and how effective and efficient and how much growth we have as nations. And so.
Your first question would be how many sign up?
Yeah. No. Yeah. How many I want to be I want to I want to buy the most robots than any other company.
Just have. Okay.
Well, I would just I would just say that. Yes, I might, debate your point, because, robots are actually the humanoids are not actually capable of delivering their full potential. And so even though, Elon Musk said that by next year he will have or by this year he will have several million robots in factories. I would say, well, what what is the plan? What is the technical plan for the manipulation, for the hints of the robot? And I will also remark that, well, Elon Musk also told us in 2017 that we will be falling asleep at the wheel in 2019, and we're still not falling asleep at the wheel. So what I might say is that I actually, love the vision, but I think he sometimes gets the timing wrong.
I think.
I'd like to comment a little bit about the robot hand. I personally have tried to live with only three fingers and only do this action. I personally have tried that for several hours. Yeah, life is just fine. Yeah, of course I fail to use chopsticks, but. Use a knife instead. Yeah. So seriously, you can also try. Actually, I encourage that. Yeah. I mean, so this, I think justify my opinion that we don't need a human level dexterity to enable robots to do many useful things. Yeah. So you can try that to say, at home it's not that dangerous. Yeah. So, so that's why say I'm, depicting a so-called graduate, autonomy and intelligent robot. It's not like they suddenly we got robots that can do every action we human can carry out. It's like, okay, robot of all kind, maybe with three fingers or, say, with one arm or, say, one arm on a dog. Who knows? So all kinds of robot in certain applications, performing certain tasks. Tasks very reliably. Yeah. So that's the near future. I mean, future in several hundred days. So. So I'm visioning. Yeah. So, yeah, of course, they also share the further future vision. Yeah. But but, I think we don't need to wait, say, until they the whole humanoid or, robot hand to be, very, very advanced. And I like to comment to that, robot dog thing. There are three major directions in robot abilities, say navigation, locomotion and manipulation. Yeah. Locomotion.
Navigation, locomotion.
And manipulation. Manipulation. Locomotion is basically about dancing. Parkour thing. Yeah. Navigation is about, you know, working around. Yeah. They find find out where I am and how to go to somewhere else. And manipulation is what Professor Ross mentioned. Yeah. So to to to move around things to, to do operations. Yeah. So currently what's missing. Well, what prevents robots to be super useful in our daily life? Our manufacturing and logistics is mainly about manipulation, but we see very, concrete and fast, advancement in recent days.
The manipulation. So you.
Just gave me an idea, for all of you to understand why manipulation is difficult, just try manipulating your phone with your fingernails. Not with a not with your skin. And, to see how how difficult that is. So robots are made, primarily from hard plastics and and metal. And those materials are more like fingernails, than the skin and flesh we have on our fingertips. So on the research side, we are advancing new materials that will give us, skin like interaction. But then we have to take that from the research lab to the demonstration and scale, scaling it up. So it's coming, but it's not today.
It's not good news is that we don't need Einstein level of intelligence because they are really like visiting zoos. When I lived in Munich, I was in the zoo of Munich every, every maybe two months. Yeah. So. So we can find, say, animals. Doesn't know any idea about language doing perfect manipulation. Yeah. So like squirrel monkey. This big, probably this 30 gram brain can do perfect manipulation. So. So it's not that far away from us, I would say.
Just listening to you all, though, it is the human, the human being doing what you do must find that the human being is remarkable. Like all the different ingredients, the brain, the skin. Okay, questions right here in the front. Just if you could stand and say your name where you're from.
Sure. Vanessa mendez.
I think they have a mic for you. Yeah. Thank you.
Thank you for the session. I think it was very insightful. And I come from Houston, Texas, as well. I'm also representing the global community. I'm currently running a startup that focuses on drone automation and AI for the solar industry and how we can improve asset integration for solar assets. So very close to what you're doing. And, you know, you put an interesting aspect, which is some of these sites, most of these sites are in remote areas, meaning the labor, thing. It's actually a big problem. So. Oh. Thank you. So, as I was saying, some of these sites are in remote areas, so, the cost of deploying these projects, is it's higher than what it could be with automation. So my question to you is, you know, given the the current, autonomous, state in, in terms of, like, drones and how capable there are, we see as skydio as a big example. How close are we for from seeing, broader automation in other sectors of, of infrastructure management, in remote areas? Thank you.
Yeah. Maybe I'll interpret that question, as like a, in the in the future, you want to be able to have a path towards, especially like very remote assets, being able to run them autonomously. And the reduce the amount of one safety issues and then also, reduce the increase the margin for basically operating these, especially in environments where there's like not as much skilled labor force. Well, I do think, I think if, if we get the model right of being able to move towards automation by small steps and one, one, one case could be these, these autonomous drones, then I think it's actually like, you know, for, for maybe less complex environments like a solar field or, or a wind turbine. Like you can begin to get that in the next like three, four years. The problem is, you know.
Remote control basically.
Is, yeah, just like mostly autonomous or completely autonomous and. Yeah, and but but I think, I think the question is just, it comes down to just like is, what's the bang for buck, I guess is like it's really consequential, in certain industries, to be able to have more tasks, be autonomous. I think it's, it's going to take more than three years to, to get those sorts of tasks, like whether it's at a, at like a shipyard, like, like having to do a turnaround of like a, an aircraft carrier or something like that. So, so I think like for I think most of the robotics and most of the software and AI is going to be focused on the highest ROI applications. And so that's going to take probably another five, six, seven years, as long as companies shift in a big way, their thesis on getting their.
Okay. So in remote environments you need really good perception system. And you might not have GPS. So you can solve that problem if you adopt on device physical AI models. Because those models will allow you to adapt. It will allow you to do the navigation. It will allow you to to respond to whatever the environment looks like. And the good news is that we are beginning to develop these types of models. They're called, state space models. And check out the liquid AI open source models, which might be very effective for your problem.
Could you just repeat that? What is the name of the models?
Liquid AI.
Liquid AI what kind of models did you say?
State space models.
State space.
This means that they are built on an underlying, mathematics that, that captures the physical world through differential equations. So it's a different approach to building the model then. The transformer model.
Okay. Another question here firsthand I saw here. Thank you. Let us know who you are.
Thank you for your interesting discussion. My name is Miguel. I'm from Spain. I'm a PhD student, and. But I'm really interested. Sorry. In the brain. In the robotic brain thing. And this, how this thing that you've been discussing about, reacting to and not programming events or to accidents. And I think the key for that is the constance. So my, my, my question is, which level of conscience are we, are we able to reach at the at the current moment? And which level of conscience can we reach in the future? Is it? Are we even is it even possible to reach the human conscience or and how long would it take? Of course.
Well, first of all, we have to define what conscience is. And, and so, I think that we don't fully understand the mechanics that make humans conscious, so it's difficult to think about applying that to a machine. But what we can say is that maybe conscious means being aware of your environment, being able to respond to your environment. And if we adopt this simple definition, then at some level we can build machines with certain level of awareness. However, we have to be careful when we build our technical solutions. We have to be able to characterize them. We have to be able to say, when do they work? When do they not work? What is the uncertainty that we expect from the predictions and recommendations that we get from from the systems? So an exciting topic that would get us to, more capable robots is really to build more uncertainty understanding within the robot brain, because through understanding the uncertainty of your predictions, the machine will be much better at doing tasks.
I'd like to add to that, actually, the, we can think about, say, how we human brain works. Yeah. So many research, say that we human brain are super efficient, actually. So we are not using our brain that much when things are normal. Yeah. So. So you probably have the experience that you listen to music or talking on the phone and doing your daily laundry or preparing food without even memory. So even you cannot sometimes recall, for example, say how you take the subway or bus to home. Yeah, because it's very normal to you. But when things become abnormal, when surprise things happen. Yeah. So so we start using our brain. So I think this ability is super important if we want to use robot reliably and safely. Yeah. So robot need to know what is normal and what is not so normal. So, so we can, have a fallback strategy, for example, human operator to intervene. Yeah. So, so that's so-called kind of consciousness. Yeah. So basically the ability to evaluate risk and to, detect, say what is abnormal. Yeah.
I mean, isn't that the whole risk with AI and robotics? That was I guess, more talked about when ChatGPT first came around of like, we're going to create these robots that are we think we're going to have it under control and then it won't be,
Well, it is a it is definitely a challenge. And, again, I want to go back to my definition of robot body and brain. We can build robot brains without AI, so we can put algorithms, in the brains of the robots. And, then we will have some limitations over what those robots can do. The attraction of bringing data and physical AI models to the brain, is that we we give the robots the ability to be more adaptive. We give the robots the ability to do tasks that cannot be modeled from first principles. I can model this task from first principles. I don't need an AI solution to tell me how to pick something from here to here. But if I want to, I don't know. What's your favorite task? In the kitchen? If I want to.
About least favorite, do the dishes.
If I want to do the dishes. It's so difficult to model all your body movements from first principles. Which is why collecting data from humans and teaching robots how to do those tasks in a human like fashion, is important, but that requires that this data gets passed through systems that understand more than statistical correlations, that can really do the spatiotemporal correlations that are involved in in doing the dishes.
I think we have time for 1 or 2 more questions. We have one in the front. Yes.
Hello, my name is Julia. I'm from Italy. I'm also a global shaper and I am a researcher in human robot interaction. I have so many questions actually, but I'll choose my favorite one, which is about human. So I was wondering what what was your opinion about, what makes human human interaction natural? And what are the biggest challenges, in your opinion, to make robots capable of interacting naturally with us?
Yeah, I would say our demonstrations. Yeah. So basically, that's how we humans learn most of the things. Yeah. So we humans have several different intelligence. Say, we basically learn, say, the ability of grasping something or throwing or say, say dribbling ball, say, by watching and practicing. Yeah, but we cannot learn that, to be able to calculate like 25 times 52. Yeah. So, so, so the that kind of intelligence is another thing. So, if we want to deploy a robot, for example, on factory floor, on logistics, I would say, demonstration is the most intuitive way to tell robot what to do. And that's exactly how we humans tell others, for example, to, to to to to do work in kitchen. Yeah.
The vast majority of the machines that we have built, so far are such that we adapt to them rather than the other way around. So an important question here is, can we build machines that adapt to humans? That means that those machines have to be really good at perception and situation awareness. They have to be able to do activity recognition and not just on the visual side. They have to understand, oh, you know, I'm trying to move a box and I'm struggling. It's too heavy. Can it come in just like a teammate to help me pick up the box? And then once we have the once we're both holding the box. Carrying a big box requires that the machine understands the forces and torques that I put on the box to respond in the same way, and also gives me feedback to understand that, it's doing the right thing and it's working as a, as a teammate rather than as a, as an inert tool.
So there's been this thing on Instagram, what it was like in 2016, if you could go back to 2016 ten years ago, would you think where we are today on robotics is farther than you predicted we would have come or not as far as we predicted we would have come. Like, where are we in terms of our aspirations versus our results in the last ten years? If you went back ten years.
Ten years, I ten years ago, I was when I went through Y Combinator in Silicon Valley after bootstrapping gecko for three years. When I went to Y Combinator, if you were doing robotics company, it was basically the same as, you know, choosing to die a slow death or a fast death. And, and especially, like, robots that weren't, like, throw $1 billion at research project that ends up dying, right? So, I think it was pretty bleak ten years ago, actually. And we thought, I think we, I think when I was growing up, I was expecting maybe, like some of you were a world where robots were among us and helping us. And, you know, it was, it was a beautiful, symbiotic relationship. We're helping each other. And that reality was not coming at all. And so I am actually exceptionally excited about where we are ten years later.
Good. Okay. Just a couple more minutes. So just do you feel like you're. We're farther or not as far along as you?
In some ways.
We're farther. In other ways we're not. So ten years ago, we were not talking about AI. We're not talking about physical AI. Self-driving projects, looked like science projects. And now we have companies that have deployed mobility and autonomous mobility in so many settings. We have robots that deliver, on sidewalks. We have self-driving cars in geofenced environments. We have robots that move containers and packages. So we have a lot of machines. But we have not made as much progress on the manipulation side. So this vision of the of Rosie, the robot assistant in your home that will do all of your chores, remain some way away. However, I think I can give you a self-driving garbage can. If, if you don't like to take your garbage out, that is possible. But the more general humanoid in-home robot will be a long way away. And I I'm also very excited about where we are because we made huge advances on the material side, on the hardware side motors and sensors on the computational side, on the data side and the AI side. And so we're waiting for the young generation to take all this wealth of capabilities and turn it into, a magical future where machines will support us in the future.
Young generation. You got 22 seconds.
Yeah. So I would say just one sentence. Say the hardest thing are behind us. Not in front.
Hardest is behind.
Hardest is already behind us. So I'm very confident today.
That's great. Thank you all so much. Thank you. Thank you all.