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Chelsea Finn

lines on the record: 41
first: 2025-03-20
most recent: 2025-03-20
sittings or episodes: 1
sources: podcast

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“I think a world like that is very possible, and I think that you can make a cheaper hardware piece of hardware if you are optimizing for a particular use case. And maybe it'd be also be a lot faster and so forth. Yeah, obviously very hard to predict.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Cooks the whole meal for us. And so I think we can envision a world where there's like one kind of robot arm that does things on the kitchen that has some hardware that's optimized for that and maybe also optimize for it to be cheap for that particular use case. And another hardware that's kind of designed for like folding clothes or something like that, dishwashing, those sorts of things. These are all like speculation, of course, but I think that a world like that is something where I think different from what a lot of people think about.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“I don't know exactly, but I think that my bet would be on something where there's actually a really wide range of different robot platforms. I think Sergei, my co-founder, likes to call it a Cambrian explosion of different robot hardware types and so forth once we actually can have the technology, the intelligence that can power all of those different robots. And I think it's kind of similar to We have all these different devices in our kitchen, for example, that can do all these different things for us. And rather than just like one device that can, that”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“And there's no way to categorize the breadth of the tasks, like how different one task is from another, how different one kitchen is from another, that sort of thing. But we can at least get a rough idea for that breadth by looking at things like the number of buildings or the number of scenes, those sorts of things.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“I mean, when we collect data, we have, it's kind of like puppeteering, like the original Aloha work, and then you can record both the actual motor command and the sensor, like the camera images. And so that is the experience for the robot. And then I also think that autonomous experience will play a huge role, just like we've seen in language models after you get an initial language model. If you can use reinforcement learning to have the language model bootstrap on its own experience, that's extremely valuable. Yeah, and then in terms of what's generalizable versus not, I think it all comes down to the breadth of the distribution. It's really hard to quantify or measure how broad the robot's own experience is.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“That data can have a lot of value, but I think that by itself it won't get you very far, and I think that there's actually some really nice analogies you can make where, for example, if you watch an Olympic swimmer, swim a race, even if you had their strength, just their practice at moving their own muscles to accomplish what they're accomplishing is essential for being able to do it. Or if you're trying to learn how to hit a tennis ball well, you won't be able to learn it by kind of watching the pros. Now, maybe these examples seem a little bit contrived because they're talking about like experts. The reason why I make those analogies is that we humans are experts at motor control, low-level motor control already for a variety of things and our robots are not. And I think the robots actually need experience from their own body in order to learn. And so I think that it's really promising to be able to leverage that form of data, especially to expand.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“The main advice that I would give someone trying to start a company would be to try to learn as much as possible quickly. And I think that actually trying to deploy quickly and learn and iterate quickly, that's probably the main advice and try to actually get the robots out there, learn from that. I'm also not sure if I'm the best person to be giving startup advice because I've only been an entrepreneur myself for 11 months. But yeah, that's probably the advice I say.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Google is an amazing place in many, many ways, but like, as one example taking a robot off campus was almost a non-starter just for code security reasons. And if you want to collect diverse data, taking robots off campus is Is valuable. You can move a lot faster when you're a smaller company when you don't have kind of restrictions, red tape, that sort of things. The really big companies, they have a ton of capital, and so they can last longer. But I also think that they're going to move slower too.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Year ago, it would be completely different. And I think that we've had so many new players recently. I think that the fact that self-driving was like that suggested that it might have been a bit too early 10 years ago. And I think that arguably it was. I think deep learning has come a long, long way since then. And so I think that that's also part of it. And I think that the same with robotics. Like if you were to ask me 10 years ago or even five years ago, honestly, I think it would be too early. I think the technology wasn't there yet. We might still be too early, probably know. I mean, it's a very hard problem and like how hard self-driving has been is I think it's a testament to how hard it is to build intelligence in the physical world. In terms of like major players, there's a lot of things that I've really liked about the startup environment and a lot of things that were very hard to do when I was at Google.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“I have no idea. Parts of robotics that make it easier than self driving, and some parts that make it harder. On one hand, it's harder because you're not just like it's just a much higher dimensional space. Even our static robots have 14 dimensions of seven for each arm. You need to be more precise in many scenarios than driving. We also don't have as much data right off the bat. On the other hand, with driving, I feel like you kind of need to solve the entire distribution to have anything that's viable. You have to be able to handle an intersection at any time of day or with any kind of possible pedestrian scenario or other cars and all that. Whereas in robotics, I think that there's lots of commercial use cases where you don't have to handle this whole huge distribution. And you also don't have as much of a safety risk as well. That makes me optimistic. And I think that also like all the results in self-driving have been very encouraging, especially.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Currently not a priority to look into these sensors because we think that the bottleneck right now is elsewhere, is on the data front, is on kind of the architectures and so forth. The other thing that I'll mention is actually right now we're most like our policies right now do not have any memory. They only look at the current image frame. They can't remember even half a second prior. And so I would much rather add memory to our models before we add other sensors. We can have Commercially viable robots for a number of applications without other sensors.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I've made a lot of arguments for smell to Sergei in the past because there's a lot of nice things about smell, although you've never actually attempted it before. Some ways the redundancy is nice. For example, and I think audio, for example, a human, if you hear something that's unexpected, it can actually kind of alert you to something. In many cases, it might actually be very, very redundant with your other sensors because you might be able to actually see something fall, for example. And that redundancy can lead to robustness. For us, it's not...”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“It's a great question. I mean, for the sandwich making, you could argue that you'd want the robot to be able to taste the sandwich to know if it's good or not.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“So we've gotten very far just with vision, with RGB images, even. Ends up being very, very helpful and probably giving you a lot of the same information that Tactile Sensors can give you.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Actually, got the robot to make a vegetarian sandwich or a ham and cheese sandwich or whatever. We also did a grocery shopping example and a table cleaning example. And I was excited about it first because it was just like cool to see the robot be able to respond to different prompts and do these challenging tasks. And second, because it actually seems like the right approach for solving the problem.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“But at the end of the day, we don't want robots just to be able to do that, we want them to be able to interact with us where we can say, like, oh, I'm a vegetarian. Can you make me a sandwich? Oh, and I like, I'm allergic to pickles. So maybe don't include those. And maybe also be able to interject in the middle and say like, oh, hold off on the tomatoes or something. It's actually kind of a big gap between something that can just follow like an instruction, like pick up the cup and something that could be able to handle those kinds of prompts and those situated corrections and so forth. And so we developed a system that basically has one model that takes us and put the prompt and kind of reasons through and is able to output like the next step that the robot should follow. And that might be, that's kind of like it's going to tell it to then the next thing will be pick up the tomato, for example. And then a lower level model that takes its input, pick up the tomato, and outputs the sequence of motor commands for the next like half second. That's the gist of it. It was a lot of fun because we”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Was a really fun project. There's two things that we're trying to look at here. One is that if you need to do like a longer Horizon task, meaning a task that might take minutes to do, then if you just train a single policy to output actions based on images, like if you're trying to make a sandwich and you train a policy that's just outputting the next motor command, that might not do as well as something that's actually kind of thinking through the steps to accomplish that task. That was kind of the first component. That's where the hierarchy comes in. And the second component is a lot of the times when we train robot policies, we're just saying like, we'll take our data, we'll annotate it and say like, this is picking up the sponge. This is putting the bowl in the bin. This segment is, I don't know, folding the shirt. And then you get a policy that can follow those basic commands of like fold the shirt or pick up the cup, those sorts of things.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“So those are a few examples. And so, yeah, I think we've seen a ton of progress in the field. It seems like after we started Pi, that was also kind of a sign to others that if the experts are really willing to bet on this, then maybe something will happen.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“You mentioned, I think, the Aloha work and later the mobile aloha work was work that showed that you can tell operate and get models to train pretty complicated dexterous manipulation tasks. We also had a follow-up paper with the shoelace tying that was a fun project because someone said that they would retire if they saw a robot tie shoelaces.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Do the Taylor Swift example that I mentioned earlier and be able to plug in kind of a lot of the web data and get better generalization on robots. A third was our RTX work where we actually were able to train models across robot embodiments and significantly we basically took all the robot data that different research labs had is a huge effort to aggregate that into a common format and train on it. And we also, when we trained on that, we actually found that we could take a checkpoint, send that model checkpoint to another lab halfway across the country. And the grad student at that lab could run the checkpoint on the robot and it would actually more often than not do better than the model that they had specifically iterated on themselves in their own lab. And that was like another big sign that like this stuff is actually starting to work and that you can get benefit across by pooling data across different robots. And then also like.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“At least for us, there were a few things that we felt like were turning points that felt like where it felt like the field was moving a lot faster compared to where it was before. One was The SACAN work where we found that you can plan with language models as kind of the high level part and then kind of plug that in with a low level model to get a model to do long horizon tasks. One was the RG2 work which showed that you could”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Of the AI community is very focused on just like language models, vision language models, and so forth. And there's like a ton of hype around reasoning and stuff like that. Oh, let's create the most intelligent thing. I feel like actually people underestimate how much intelligence goes into motor control. Many, many years of evolution is what led to us being able to use our hands the way that we do. And there are many animals that can't do it, even though they had so many years of evolution. And so I think that there's actually so much complexity and intelligence that goes into being able to do something as basic as like make a bowl of cereal or pour a glass of water. And yeah, so in some ways, I think that actually embodied intelligence or physical intelligence is very core to intelligence and maybe kind of underrated compared to some of the less embodied models.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Research and compute and evaluations. And so we're optimizing for, that's one of the things we're kind of optimizing for. And so we're using cheap robots. We're using robots that we can very easily develop teleoperation interfaces for, in which you can do teleoperation very quickly and collect diverse data, collect lots of data.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“On one hand, I dehumanizer really cool, and I have one in my lab at Stanford. On the other hand, I think that they're a little overrated. And one way it kind of to practically look at it is I think that we're generally fairly bottlenecked on data right now. And some people argue that with humanoids, you can maybe collect data more easily because it matches the human form factor. And so maybe it'd be easier to mimic humans that have actually heard people make those arguments. But if you've ever actually tried to tell they operate a humanoid.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Commanded arm position, for example, and then checking it and then validating it and so forth. And so I think we need to think about new ways of having some kind of tolerance for mistakes or scenarios where that's fine or scenarios where humans and robots can work together. That's, I think, one big challenge that will come up when trying to actually deploy these. And some of the language interaction work that we've been doing is actually motivated by this challenge where we think it's really important for humans to be able to kind of provide input for how they want the robot to behave and what they want the robot to do, how they want the robot to help in a particular scenario.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“As a company, we're really focused on the long term problem and not at any one particular application because of the failure modes that can come up when you focus on one application. I don't know where the first applications will be. I think one thing that's actually Challenging is that typically in machine learning, a lot of the successful applications of like recommender systems, language models, like image detection, a lot of the consumers of that model outputs are actually humans who could actually check it. And humans are good at the thing. A lot of the very natural applications of robots is actually the robot doing something autonomously on its own, where it's not like a human consuming the”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“I think robotics is very hard, and there have been many, many failures in the past. And unlike when you're like recognizing an object in an image, there's very little tolerance for error. You can miss a grasp on an object or not. The difference between making contact and not making contact in an object. Is so small and it has a massive impact on the outcome of whether the robot can actually successfully manipulate the object. And I mean, that's just one example. There's challenges on the data side of collecting data. Well, just anything involving hardware is hard as well.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“We think that having the best researchers and engineers will be necessary for solving this problem. The last thing that I'll mention is that I think the biggest risk with this bet is that it won't work. I'm not really worried about competitors. I'm more worried that No one will solve the problem.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“There's a couple reasons for it. One is that we think that the field is really just the beginning, and these models will be so, so much better, and the robots should be so, so much better in a year, in three years. And we want to support the development of the research, and we want to support the community, support the robots, so that when we hopefully develop the technology of these generalist models, the world will be more ready for it, will have better robust robots that are able to leverage those models, people who have the expertise and understand what it requires to use those models. And then the other thing is also like we have a really fantastic team of researchers and engineers and really, really fantastic researchers and engineers want to work at companies that are open, especially researchers, where they can get kind of credit for their work and share their ideas, talk about their ideas.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Definitely. So we've actually been quite open. Not only have we open sourced some of the weights and release details in technical papers, we've actually also been working with hardware companies and giving designs of robots to hardware companies. And some people have actually, like when I tell people this, sometimes they're actually really shocked that what about the IP? What about, I don't know, confidentiality and stuff like that? And we've actually made this made a very intentional choice around this.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Recognize where the shirt is and where the hamper is, and what you need to do to accomplish that task. That's useful. Or if you want to make a sandwich and the user has a particular request in mind, you should reason through that request if they're allergic to pickles. You probably shouldn't put pickles on the sandwich, things like that. So there's some basic things around there, although the number one thing is just more diverse for robot data.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“And so just trying to collect data in many more diverse places and with many more objects, many more tasks. So scaling the diversity of the data, not just the quantity of the data, is very important. And that's a big thing that we're focusing on right now, actually bringing our robots into lots of different places and collecting data in it. As a side product of that, we also learn what it takes to actually get your robot to be operational and functional in lots of different places. And that is a really nice byproduct because if you actually want to get robots to work in the real world, you need to be able to do that. So that's the number one thing. But then we're also exploring other things, leveraging videos of people. Again, leveraging data from the web, leveraging pre-trained models, thinking about reasoning, although more basic forms of reasoning in order to, for example, put a dirty shirt into a hamper.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so I think the number one thing in this kind of the boring thing is just getting more diverse robot data. So for that release that we had in late October last year, Collected data in three buildings technically? The internet, for example, everything that is fueled language models and vision models is way, way more diverse than that because the internet is pictures that are taken by lots of people and texts written by lots of different people.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Then you could actually get the robot to do tasks that require concepts that were never in the robots training data but were in the internet. Like one famous example is that you can pass the coke can to Taylor Swift or a picture of Taylor Swift and the robot has never seen Taylor Swift in person, but the internet has lots of images of Taylor Swift in it. And you can leverage all of the information in that data and then the weights of the pre-trained model to kind of transfer that to the robot. So we're not starting from scratch and that helps a lot as well. So that's a little bit about the approach happy to dive deeper as well.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“And generalization to different environments. So, what we showed in October was the robot in one environment. And it was trained, it had data in that environment. We were able to see some amount of generalization, so it was able to fold shirts that had never seen before, fold shorts it has never seen before, but the degree of generalization was very limited. And you also couldn't interact with it in any way. You couldn't prompt it and tell you what you want to do beyond kind of fairly basic things that it saw on the training data. And so being able to handle lots of different prompts and lots of different environments is a big focus right now. And in terms of the architecture, we're using transformers and we are using pre-trained models, pre-trained vision language models. And that allows you to leverage all of the rich information in the internet. We had a research result a couple years ago where we showed that if you leverage vision language models,”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“At the beginning, we were just getting off the ground. We were trying to scale data collection, and a big part of that is unlike in language, we don't have Wikipedia or an internet of robot motions. And we're really excited about scaling data on real robots in the real world. This kind of real data is what has fueled machine learning advances in the past. And a big part of that is we actually need to collect that data, and that looks like teleoperating robots in the physical world. We're also exploring other ways of scaling data as well, but the kind of bread and butter is scaling real robot data. We released something in late October where we showed some of our initial efforts around scaling data and how we can learn very complex tasks of folding laundry, cleaning tables, constructing a board box. Now where we are in that journey is really thinking a lot about language interaction.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Able to leverage all of the possible data is very important. And this comes down to actually not just leveraging data from one robot, but from any robot platform that might have six joints or seven joints or two arms or one arm. We've seen a lot of evidence that you could actually transfer a lot of rich information across these different embodiments and allows you to use data. And also if you iterate on your robot platform, you don't have to throw all your data away. I have faced a lot of pain in the past where we got a new version of the robot and then your policy doesn't work. It's a really painful process to try to get back to where you were on the previous robot iteration. So, yeah, trying to build generals robots. And essentially, kind of develop foundation models that will power the next generation of robots in the real world”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so we're trying to build a big neural network model that could ultimately control any robot to do anything in any scenario. And like a big part of our vision is that in the past, robotics has focused on like trying to go deep on one application and like developing a robot to do one thing and then ultimately gotten kind of stuck in that one application. It's really hard to solve one thing and then try to get out of that and broaden. And instead, we're really in it for the long term to try to address this broader problem of physical intelligence in the real world. We're thinking a lot about generalization, generalists. And unlike other robotics companies, we think that”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Year ago at this point. So I've been on leave from Stanford for that. And it's been really exciting to be able to try to execute on the vision that the co-founders that we collectively have and do it with a lot of resources and so forth. And I'm also still advising students at Stanford as well.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Really something that a lot of people are excited about since that beginning point, it was very clear to me that we could train robots to do pretty cool things, but that getting the robot to do One of those things in many scenarios with many objects was a major, major challenge. So 10 years ago, we were training robots to like screw a cup onto a bottle and use this fatula to lift an object into a bowl and kind of do a tight insertion or hang up like a hanger on a clothes rack. And so pretty cool stuff, but actually getting the robot to do that in many environments with many objects, that's where a big part of the challenge comes in. And I've been thinking about ways to make broader data sets, train on those broader data sets, and also different approaches for learning, whether it be reinforcement learning, video prediction, imitation learning, all those things. And so, yeah, move from spread to year at Google Brain in between my PhD and joining Stanford, became a professor at Stanford, started a lab there, did a lot of work along all these lines. And then recently started physical intelligence almost a year.”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, it's been a long road. At the beginning, I was really excited about the impact that robotics could have in the world, but at the same time, I was also really fascinated by this problem of developing perception and intelligence in machines. And robots embody all of that. And also there's sometimes there's some cool math that you can do as well that keeps your brain active, makes you think. And so I think all of that is really fun about working in the field. I started working more seriously in robotics more than 10 years ago at this point, at the start of my PhD at Berkeley, and we were working on neural network control, trying to train neural networks that map from image pixels to directly actually to motor torques on a robot arm. At the time, this was not very popular, and we've come a long way, and it's a lot more accepted in robotics and also just”
2025-03-20 · No Priors · The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn · IDENTIFIED FROM THE TRANSCRIPT