Sergey Levine: Current State of Humanoid Robotics, China & Future Predictions

23:19I think it's a great idea. And I think that's like a really nice way to move towards elevating the importance of generalization in people's kind of consciousness. consciousness. You know, when we were working on the Pi Star 06 project, the RL project that we did late last year, we wanted to do some longer horizon experiments. In some cases, it's obvious, like we had our robot assembling boxes at Dandelion Chocolate Factory. So they're like, you know, it's an actual chocolate factory, so they need the boxes. So we run it for several days. But we had this coffee task, which was the robot was using an espresso machine to make espresso.

23:56So what we did is we ran it for 13 hours making espresso drinks. And we are, you know, we tried to be like very, I guess, environmentally conscious about this. So we didn't want to like throw out the coffee. So after 13 hours, everyone in the office was like a little wiry because someone has to like drink the coffee. But it like it ran for 13 hours and it was pretty cool. It screwed up a few times, like it'll spill all the coffee grounds and then it needs to go and get like a cloth and wipe it down. But like it does it and it's, you know, nothing exploded. 13 hours went by. Probably the most negative consequence was like loss of sleep from too much caffeination.

24:29Sergey Levine:But when it spills the coffee grounds and cleans it up, it did that by itself. Well, so the way that that experiment was done is that there is a high level prompting. So like roughly the prompt is updated maybe every like five minutes or so, like in between semantically coherent tasks. So you tell it like, make espresso, clean up the machine, et cetera. So those steps, the actual like clean up the machine was commanded by a person. In principle, we could automate that. In fact, one of the things we're spending a lot of effort now is improving our high-level policy that does those commands. But for that experiment, every five minutes, somebody basically updates what it's being asked to do.

25:03The way we intended it was the commands would be like, if you go to an actual coffee shop, you say, oh, I want a latte, I want an espresso. That was supposed to be the prompt, except then you also have to tell it, I want you to clean it up before you do the next one.

25:15Sergey Levine:That makes sense. OpenAI, Anthropic, Cursor, and Vercel all use this product to make their lives better. And the problem it solves is when you're building SaaS or an AI product and you wanna sell to other companies, there's all these requirements you need to meet. There's SSO, there's SCIM, there's RBAC, there's audit logs. These are all things that take time to integrate, but aren't the main focus of your app. WorkOS is an API layer that lets you meet all of these requirements in just a few lines of code. So let's say you have a new SaaS product and you wanna sell to other companies, Work OS will solve all of these critical feature gaps for you.

25:54Sergey Levine:You can check them out at workos.com to learn more and get started. And I appreciate them for supporting my work and sponsoring this podcast. It sounds like on the way to generalizing, data is a very important part of that. And I was reading there's different types of data. There's simulated data. You could collect physical interactive data. and I wanted to hear your take on what's the best data to get, what's the worst, and what are the pros and cons. This is, by the way, like a question that is, I guess, quite... There's a lot of discussion in the robotics community about this question and some people have, like, very opposite opinions on it.

26:36My own take on this is that a lot of different data sources are easier for the model to internalize if it can ground them in a thorough physical understanding of the world. So let me try to explain what I mean with a few examples. If you want to learn to fly an airplane, you will probably use a simulator, at least during part of your training. But the simulator makes a lot of sense to you because when you start using the simulator, you have a lot of world knowledge that you can use to ground what's going on. Like, you know that when you are using the flight simulator to learn how to fly the airplane, You're not just like playing a video game.

27:15You're trying to acquire knowledge that you will then use with a real airplane. And you understand that there's sort of an abstraction there. Same thing if you're playing like a really cartoony, like, you know, Atari game or something, right? Like, you know that all the symbols on the screen, you can sort of connect them to things that you've experienced in your life, and you can make an analogy there. So a lot of that, like, even though it kind of seems like these simulated environments reflect aspects of the real world, To us, they make a lot of sense because we kind of bring to bear a lot of our own prior experience and we fill in the blanks that the simulation has.

27:52And also, if you want to use data yourself as a person of somebody else doing something, if you watch someone, let's say, cooking a meal, right, even though you don't experience every movement they're experiencing, you have a lot of that knowledge that you bring to bear. And you're like, okay, I see they're picking up the salt shaker like I've put salt on things before So I kind of roughly know what's going on there And I can file it away at this level abstraction of like add salt without having to like figure out all their muscle movements So my point with this is that once you have that Understanding of how you do things physically with your own body and how you experience the physical world now All these other sources of knowledge can be connected up to it because that Foundation you get from your experience helps you ground everything So where I'm going with this is that if we have a robotic foundation model that is trained on lots of real embodied data that provides that grounding, it might actually be much better able to absorb other sources of knowledge.

28:46And this is actually like a little bit upside down relative to how some people think about it because it's very tempting looking at the success of like internet data for LLMs to say, well, maybe we should do the opposite. Maybe we should like start with like YouTube videos and then put robot data on top of that. But I think it's actually the other way around. And I even have a little bit of evidence for this. So my colleague, Suraj Nair, together with Simar from Georgia Tech, they had a project together a while back where they took our robot foundation model and they added human video data. But they didn't start with human video data.

29:22They actually started with a model trained on robot data and then added video data on top of it. And what they did is they looked at the representations inside the model. Basically, how does the model represent human experience versus robot experience? And they found that if you use a small model with a small amount of robot data, predictably the human experience and the robot experience are fully separated, meaning that the feature representations are different. But if you train on lots of robot data from lots of different robots, then the features are grouped much more by what task is being done rather than by whether it's a human or a robot.

29:52And when we looked at the feature plots, it was just mind-boggling because literally when you crank up the amount of robot data to 100%, they just line up perfectly. Like you see the, you know, you do this T-SNE embedding, you see the shapes of the embeddings, and it's just all task identity and like minimal sensitivity to embodiment. And to me, that's kind of mind-blowing because like the base model wasn't trained on any human data at all. But once you start adding human data, it represents it exactly the same way. And I think that's really exciting. I think that to me is like one of the strongest indicators is that if you have that good foundation of robot experience, you can put everything else on top of it.

30:27It's actually better at absorbing that.

30:29Sergey Levine:Is it important that that base model has data that was collected using that specific set of motors, specific set of joints? So far, we've obviously put a lot of effort into cross-embodiment models that can handle many different robot types. But generally, you do need data of the robot you're going to be deploying on to get good performance. So kind of the metric of Generalization there is not can you zero shot a new robot? But it's mostly can you get away with less experience from the new robot and transfer skills from other robots? Okay, so that's the current state of things now There is a little bit of a kind of surprisingly positive read on that which is even though you need data from these robots the amount of special stuff that the model is doing is is kind of minimal.

31:16When we started doing all this, I had a big long list of all the cool research I wanted to do to better accommodate different morphologies. Can you factorize the model's representation in some way so that there's a six-degree freedom arm head and a seven-degree head and a gripper head, etc.? We didn't do any of that. The model just outputs a big vector of numbers. If the robot has less degrees of freedom than the number it outputs, it just zero-pads it. There's nothing fancy, and that's it. and then just train on all the robots and outputs the correct actions based on what is seen through the camera essentially.

31:49But now, to your point about whether you can handle new robots. So far, the thing that we focused on, and I think this is showing some promise, is to be able to transfer skills between robots. And this is actually where the particular choices in how the model works seem to matter. For example, you can have a model that does some intermediate thinking. And that thinking can be done in different modalities. So you can think in text. And thinking in text is really good for transferring high-level behavioral structure. So that's basically how you understand that, hey, if I want to clean the kitchen and put away the silverware, first open the drawer.

32:34That's kind of a semantic inference. You can transfer that very well because obviously that's largely agnostic to any embodiment or anything like that. But even lower level things can be transferred if you use the right representation. So one experiment we did is we had a thinking stage that is expressed in images, where you basically dream up an image of the next milestone in the task. And with that, we could actually get a robot, the UR5 robot, to fold a t-shirt, even though we didn't have any t-shirt folding data on the UR5. Because while getting the R motions correct is very hard, because the robot basically requires very different joint angles to do the task, cooking up an image of what it looks like for it to fold a shirt is not that hard because like you've seen the robot arm in all sorts of different poses, you've seen the shirt in all different stages of being folded and unfolded, you know roughly where it should hold it.

33:21So getting a good generative model to cook up that image is pretty straightforward. And once you have the image, then from that backing out the correct actions is easy too because you can just look at the synthesized arm angle and just like back out what the angle should be. So it's not changing the problem, but it's just introducing this intermediate step that makes it easier to solve. Just like if you're solving a math problem, if you figure out like the right intermediate step, kind of the answer is obvious from that intermediate step. And I think that's really exciting because now that shows that this level of generalization across robots, and I'm sure other generalization too, can be facilitated with thinking, just like in LLMs, but with a twist that you have to think in the right modality.

34:00Sergey Levine:Interesting. So it outputs a, I guess that image is what its video sensor is seeing, and it's like the next step. Yeah, you can almost think of it like image editing. You can do the same thing with video. You can do it with video prediction. But the key is to like imagine what it would look like to progress on this task. That seems like a very human thing to do, right? Yeah, yeah, absolutely. Some things you plan semantically and some things you plan spatially. Like if you're doing rock climbing, you're probably not thinking like, hey, left arm to rock 37 centimeters to the left. You're probably more like imagining your arm reaching for the rock.

34:34Sergey Levine:Earlier in the conversation, you mentioned that Waymo was very inspiring and their kind of path to productionization is proof that you can do real world generalized robotics. And if I recall correctly, when I was a lot younger, it was kind of this early promise of this is going to happen. And then in reality, it took a lot longer. And so I guess my question is, in the case of humanoid robotics, what would make you say single-digit years it's coming versus a long tail and policy challenges as well, I could imagine? I think one big difference between how robotic foundation models address the problem and how more traditional engineered systems address the problem is that the stack is really thin.

35:27So it's not easy to train a foundation model. You need to obviously get the right data. There's a lot of work that goes into curating, labeling, all that other kind of stuff. But the actual software that runs on the robot is very, very simple. So, you know, you might have some kind of thinking or reasoning stage. You might have the model produce actions. It needs to be fast enough. But the like just if you think about in terms of raw lines of code, it's much, much lower than a more traditional AV stack. And, you know, partly that's because modern autonomous vehicles, the work on that started a lot earlier with very different technologies and evolved over time.

36:10Partly it's also because the problem is more safety critical. Like, yes, you don't want a robotic manipulator to, like, drop a fragile object. But at the end of the day, that's a lot less bad than having a car hit somebody. So that is not to say that the safety challenges with robots are not real. They're very real and it's very important to tackle in the fact It's probably one of the harder ends of the problem, but they are not as much of a hard stop to practical deployments because you can Come up with tasks and environments and domains and also physical hardware where those problems are a lot less severe so I think that that combination radically simpler software stack plus less drastic software challenges, I think, actually make it a lot easier.

36:57And because, you know, to your earlier point, that there's this kind of flywheel effect, that there's a positive feedback loop that, you know, starting to get things out in the world, even under some constraints, will actually facilitate getting them out more and more.

37:09Sergey Levine:I think a lot of people are familiar with this idea of post-mortem, you know, looking back on why something failed. But in this case, I'm curious, what would you say to a pre-mortem, And in the sense of if humanoid robotics did not succeed in single-digit years, what do you think would be the most likely reason why humanoid robotics failed? Ultimately, for these things to be truly useful, they do need to reach a level of reliability and robustness and generalization that is higher than what we typically expect from LLMs, for example, or generative AI for images and video. Because typically like these tools they are very much human interactive tools Like you get an LLM to do something and it doesn't do quite what you want So you sort of revise your prompt and you basically like you iterate with it and and that's why like even the earlier LLM tools like the first version of chat GPT Even though they were much more primitive than what we have now They were still already useful because somebody could just like keep hammering out until it basically solves their problem You know just like if you're using a search engine like you type something in the search and you don't get quite what you want You revise your query and then you get what you want.

38:20Whereas with a robot, like the full value of it is unlocked when it's actually doing the thing autonomously. So it's having to have somebody like constantly iterate for every single task is almost like antithetical to the benefit that you're getting. So I think a lot of the risk has to do with how easy is it to get that level of reliability and robustness. And that's, again, where some of the demos might be like a little bit misleading because if someone shows a demo of the robot doing something cool, I mean, obviously, if everything is presented in a forthright way, that could still be a very good indicator of progress.

38:52But it doesn't make it obvious how far or how close it is to reaching that practically relevant level of robustness. So I'm personally a big believer in using techniques like reinforcement learning that can actually benefit from autonomous experience to kind of fine tune that last few percentage points to make it go from like 95 to actually 100%. But that's really important and it's not yet a solved problem.

39:18Sergey Levine:If it did take longer than expected, it's because the bar is higher. Because the bar is higher and in particular, like those last few, the kind of the last inch so to speak, is something that requires not just really good models, but also new innovations in technology. I mean, I don't think that, you know, I'm not the kind of person that would say like, oh, we should like throw out everything that we know about foundation models to start over. I don't think it's that at all. I think that roughly the puzzle pieces that we have are actually very good puzzle pieces. But still, we should acknowledge that right now, the methods and the models need more work to cross that level of robustness.

39:55Sergey Levine:In LLMs, it feels like everyone is doing kind of the same thing, but different flavors. In the robotics industry, is everyone doing kind of the same thing? Are there any hot take architectures that are different direction? I actually think that there's a lot more heterogeneity than it might seem. One big dividing line that I think is maybe not as obvious from just kind of looking at the results is the distinction between kind of fully embracing the foundation model ethos, so to speak, versus focusing on specific like kind of vertical areas. And I think this is like kind of hard to tease out sometimes because obviously, like, you know, because that's the cool thing.

40:40But the foundational model ethos fundamentally is something like this, that if you have a particular problem you want to solve, it is better to train a more general model that can use data from a breadth of problems. And if you do it right, it'll actually be better at the specialized problem you want to solve than a narrow specialist. So, again, to come back to the LM analogy, if you want to do machine translation, don't build a machine translation system. Build a language model that understands all language tasks and throw it at machine translation. And in robotics, I think that is actually very deeply uncomfortable to people.

41:14Because if someone is actually working on an application, like they're doing warehouse automation, it is very awkward to think, oh, if I want to do warehouse automation, let me collect data of putting away silverware in kitchens. It just sounds bizarre. But that is the foundation model lesson, that if you have enough breadth, if you collect data from a wide range of different tasks, Then you will acquire those generalizable skills, and if your model is built correctly, it will repurpose those skills for whatever situation it encounters. So I think it is actually true that even if you want to build a warehousing robot, you're better off collecting a breadth of data, and it will be better at handling all the weird edge cases you might encounter even in that warehouse domain.

41:55But this is not something that's easy for people to accept, because it's just so antithetical to the principle of building kind of a traditional vertically integrated robotic system.

42:05Sergey Levine:I noticed this new interesting phenomenon with these AI companies, which is if they're wildly successful, it creates this, I guess, worry or new set of things. So, for instance, when Anthropic had a very powerful model, then the government comes in and there's these worries about safety and risk and all that. And I'm curious how you think about that. Like if physical intelligence this year had a phenomenal, incredibly capable generalized model, how do you think about those kinds of topics that might come up? Working on AI safety is not a new thing. My colleague at UC Berkeley, Stuart Russell, was talking about this stuff like over a decade ago, and lots of people spent a lot of time working on it.

42:57It's just that the trouble is when the technology moves so fast, the important problems are not just a function of like, you know, the core principles. It's also a function of how society reacts to it, what kind of tools are adopted and so on. And I think that's very, very hard to anticipate. So I don't have like a very satisfying answer here. In terms of how we are approaching it our philosophy around all this stuff is Basically one of empirical experimentation like let's get stuff out there. Let's see what happens in the real world Let's see what goes right and what goes wrong so that we have as much of a preview for You know what the technology can do what are its weaknesses?

43:40What are its strengths? and and so on but You know at the end of the day you kind of have to just like keep your eyes open see what happens and adjust as you go. It's very hard to anticipate. And I think your question, though, is very spot on, even though I don't have a great answer for you. Because yeah, if we're having this much concern and issues with AI systems that are basically limited to using computers, we're presumably going to have strictly more concerns and issues with AI systems that can do everything in the physical world that we can do. So the issues are real. It's just, you know, you kind of have to like see what happens and then adjust.

44:18Sergey Levine:And that's kind of in the scary path. In the happy path, if everything goes well and we have incredibly capable models and robots, in 10 years, is the North Star that that's the end of human labor? I believe it's a mistake to think of robots as mechanical people, right? Like, Like, you know, computers at some level are kind of like mechanical brains. But when personal computers like really took off in the 90s, early 2000s, etc., it's not like the first thing that happened is that, you know, people replaced their brains with computers. Rather, what we saw is actually a proliferation of very different kinds of computers.

45:01We saw kind of like ubiquitous computing. So you would have a computer on your desk, but you might also have one in your pocket. You might have one in your refrigerator and in your car. Like because computing became so accessible, you could have a little bit of computing in everything. And, you know, I don't think that that's what like the people that first started thinking about this stuff in the 40s and 50s would have imagined. They would have imagined like, you know, room-sized computers whose job it is to like control the, you know, the policy of an entire country or something rather than like a little bit of computer in everybody's refrigerator.

45:32So I think it's, you know, by analogy that we might imagine there might be like a little bit of physical actuation in everything and it might just be like lots of everyday things that you have to do yourself. Now you get like a little bit of help with it. I think the other example that's worth thinking about is modern coding agents, right? So I think that, you know, this is something where, of course, the jury is still out as to what the end game of coding agents is. But certainly, from the experience of software engineers today, it kind of seems like probably fair to say that most would consider coding agents to be more empowering them rather than somehow causing them to have a panic.

46:14I mean, some people might have a panic. But in general, at least from the software engineers that I've talked to and from my own experience, it's more empowering to kind of be able to amplify how much work you can do with AI tools. So I think from that, and that maybe is like a pretty direct analogy because that is straight up an example of an actual real job where AI has entered into it and has actually provided more leverage to the people doing that job. So I think that's another example that we can look to. But the truth is that I think it remains to be seen.

46:44Sergey Levine:So in LLMs, there's a few seminal papers that if you read those papers, you kind of get a sense of the lineage of the breakthroughs that mattered and understanding where we are today. In the robotics industry, are there a set of top papers that you really think kind of show the breakthroughs that people should know about if they're curious about the state of the art in terms of humanoid robotics? One thing I would point out, and this is partly a shameless plug because I am a co-author on that paper, though candidly, 99.9 % of the work on this was done by Tony, who was the lead author, is the original ACT paper, the Aloha paper.

47:27It's an interesting example because in some ways, the ideas weren't really that new, but they were illustrated in a really nice way. And the idea was that, hey, if you set up the right kind of low-cost robot setup, in his case it was based on these robot arms from Trosson Robotics. They're like$7 ,000 hobbyist arms. He set them up in a bimanual setup with a leader follower teleoperation device. And he showed that actually if you do it right without really any particularly fancy tricks, you could easily collect teleoperation data of extremely dexterous tasks that people had previously thought would require like very sophisticated hardware and all sorts of like really expensive stuff, and then set up like a fairly straightforward transformer-based model, and it could actually do a lot of those tasks.

48:13And it's kind of like an interesting thing because usually in academic research, we put a big premium on like, you know, do you have some like sophisticated new mathematical thing or some sophisticated like technical insight? And in that paper, which I think at this point has been hugely influential, the insight is really just like, yeah, just put together the right pieces and have a little bit more faith in what a simple robot could do, so to speak, equipped with a good intent learning system. And he showed things like replacing batteries in a remote control. He even got like a little mannequin foot, and he showed that you could put a shoe on it for an assistive task, sort of.

48:51Some people need help getting their shoes on, so that's good. But what people found, I think, so interesting about that paper is just how far you could get with relatively simple building blocks. And at this point, he open-sourced the code for it, and the ACT code has been used by lots of people. If someone wants a very basic starter kit for robotic learning, that's usually what they grab. And I think that it's worth for somebody who wants to get into the field to go through that paper and really understand what's going on there, because even though in some ways it's not that sophisticated, I think it provides a bit of calibration on what matters.

49:30Like, you know, the details matter, but the details don't have to be complicated.

49:35Sergey Levine:Before all this large model robotics kind of wave, prior to that, Boston Dynamics had these really impressive demonstrations and tons of mindshare. I guess I wasn't even in the field, but I think, wow, they're really doing incredible robotics. and then in the last, I don't know how many years, I don't really hear about them much anymore. Is there some shift in the industry that made that so or, you know, is that something you could explain? So the way I would explain it is this, that there are, you know, robotics at some level is about building complex systems. So even though it's very tempting to say like, oh, there's different areas of AI, there's like LLMs and vision and robotics, one of those is not like the others.

50:30Because for robots, you actually need like all the parts, everything from like how you wire up the robot, what the power source is, what does the actuator look like, all the way to how does it do like high-level planning to determine like what tasks to do next. And even though we could look at these things and say, like, oh, all of these different videos and different companies and different demos, they're all robotics, they're really kind of different parts of the stack. A lot of what the classic Boston Dynamics results show is very sophisticated hardware, very carefully designed hardware, with a traditional control approach, with very smart controls engineers setting everything up, but with comparatively less emphasis on the kind of decision-making aspect.

51:20And I think that at a particular point in time, that actually made a lot of sense because we can't build the physical body. It doesn't matter what kind of decision-making system is running on it. But to our earlier discussion about generalization, at this point, we're at a stage in the development of these things that even though we can do more on hardware, in many ways it's good enough. And the big challenge is how to have the decision-making loop that actually works and that reacts intelligently to everything in the environment. And the place where I would draw the dividing line between those is, like, decision-making loop doesn't mean symbolic decisions.

51:59It could mean low-level decisions. The question is, do you need to take the rest of the environment into account, or are you just dealing with a robot? So if you want to do a backflip on flat ground, you must have to deal with a robot. But if you want to pick up a coffee cup off of a table, even though that's maybe in some ways simpler than doing a backflip, you really have to understand what's going on in the rest of the world rather than just your own body. And that dividing line, I think the way that technology has panned out, I think it's fair to say that that is the dividing line between AI and controls.

52:29Like controls is when you have to control the robot body. AI is when you have to take into account what goes on outside of the robot. And I think that's why you see this divide because I think a lot of the demos where you mostly needed to deal with the robot itself and not the rest of the world, really good controls could allow you to, could admit a very good solution there. And another thing I would say here is, okay, if there's a lot of controls work that goes into doing some particular skill, well, there is actually something to learn from that because if you can hand design a controller that performs a sophisticated behavior, very likely you can also learn that controller.

53:08So just that proof of existence that the thing is possible, and not only possible but also simple enough that a person could build it, Because remember, people are, you know, at the end of the day, even with code these days, the kind of complexity that people can handle is not as high as the kind of complexity that AI can handle. So if a person can hand design something to do a backflip or do some acrobatics, that's a really great proof of existence that there exists some relatively parsimonious control law for doing that skill. And parsimonious does mean generalizable. So if it's simple enough for a person to design, probably there's something fairly general in there.

53:40And if you can learn it and automate it without having to have the human controls engineers in the loop, So that's good news.

53:47Sergey Levine:And then last question for you is, if you could go back to when you just entered the industry and give yourself some advice, knowing everything you know now, what would you say? One thing that I've learned over the last few years, which I think is a little different than kind of my original mindset, is I think that addressing robotics effectively requires using very broad prior knowledge. And I think that there's this idea that a lot of people in robotic learning have, which I think I shared initially, that since people learn things kind of from scratch, maybe robots should learn things from scratch too.

54:25So, for example, in some of our early work on large-scale robotic learning at Google, we had this, what we call the arm farm project. We set up a bunch of robot arms in a conference room, actually, because we didn't have a proper lab, but it was a conference room. And we had them all grasping objects. And the idea was, well, if they grasp millions of objects, they'll learn very general grasping strategies. And it basically worked. Like they could learn to grasp objects, but it was very hard to like take it further from that to the next level. So, okay, now I can pick up anything, but like, so what?

55:00It didn't serve as a very good stepping stone for more complex skills. And I think part of that was that we were approaching this like very blank slate. Like let's start from zero and see if knowing nothing in advance, the robot could start picking up behaviors. But I think that it's much, much more practical to get all this to work if you can combine robot experience with knowledge that you can pull in from other sources. Like, for example, I was very skeptical initially about the utility of language. And I think scientifically this is defensible, which is that like, hey, you know, like animals can do some pretty impressive things, like monkeys can do really cool stuff.

55:34But monkeys, as far as I know, can't speak, at least not very eloquently. So maybe our robots should also be able to do stuff and they don't necessarily need to understand language. But I think the subtlety there is what's important is not language, it's prior knowledge. that you can put in as a scaffold in your learning process. And you can pull in that knowledge in all sorts of ways. Humans don't necessarily pull it in entirely through language. And monkeys certainly don't. They do it from observation, from observing other people, other creatures, and so on. So there's lots of sources of power knowledge.

56:06But the point is that you've got to get that power knowledge in there. Otherwise you're actually faced with a harder problem than what humans and animals have to solve. Because if a person had to figure out how to assemble IKEA furniture, but they've never actually encountered any article of furniture in their entire life, like, okay, that would be like pretty difficult because they don't even know what like, what the point of this is or what the end game looks like. So yeah, prior knowledge is important. And while I'm still a big fan of learning things through experience, I think that, you know, my advice to myself would have been take prior knowledge more seriously.

56:38Sergey Levine:Awesome. Well, thank you so much for your time, Sergey. I really appreciate it. Yeah, thank you for your questions. Hey, thank you for watching this podcast. If you liked it and you want to see the show grow, please support with a comment or a like also if you have any recommendations for people you want me to bring on please drop a comment guests like barbara liskov mike stonebreaker mark brooker these were all people that i brought on because someone left a comment on another note aside from the podcast i'm working on building the ergonomic keyboard that i wish existed here's a glance at the prototype it's a split keyboard so there's two sides this is in the case but yeah we launched on kickstarter and we hit our goal within eight hours of launching i really appreciate it if you were one of the people who grabbed one of the early units we're now working on the long journey of building the tooling now and so if you still want to pick one up i've left the late pledges open on kickstarter so you can grab one there i'll put a link in the description thank you again for watching the podcast and i'll see you in the next episode