
54 segments available
Sergey Levine is one of the world’s top robotics researchers and co-founder of Physical Intelligence. He thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030. If Sergey’s right, the world 5 years from now will be an *insanely* different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/sergey-levine * Apple Podcasts: https://podcasts.apple.com/us/podcast/fully-autonomous-robots-are-much-closer-than-you-think/id1516093381?i=1000726524050 * Spotify: https://open.spotify.com/episode/1WpOKLN3vSvvWjrVDIJAyy?si=DPxf6K5BSBy5R4OYIqk8pQ 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 * Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at https://labelbox.com/dwarkesh * Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to https://hudson-trading.com/dwarkesh * Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: https://ai.studio/banana To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00:00) – Timeline to widely deployed autonomous robots (00:17:25) – Why robotics will scale faster than self-driving cars (00:27:28) – How vision-language-action models work (00:45:37) – Changes needed for brainlike efficiency in robots (00:57:59) – Learning from simulation (01:09:18) – How much will robots speed up AI buildouts? (01:18:01) – If hardware’s the bottleneck, does China win by default?
Sergey Levine introduces Physical Intelligence, a robotics foundation model company, and discusses its mission to create general-purpose robotic models capable of performing various tasks. He emphasizes the importance of building foundational technologies that can lead to significant advancements in robotics.
"Today I'm chatting with Sergey Levine, who is a co-founder of Physical Intelligence, which is a robotics foundation model company, and also a professor at UC Berkeley and just generally one of t..."
Levine outlines a year-by-year vision for the development of robotics, highlighting the importance of dexterity and the ability to perform intricate tasks. He discusses the initial successes in robotic capabilities, such as folding laundry, and the broader goal of creating robots that can manage complex household tasks autonomously.
"Physical Intelligence aims to build robotic foundation models. That basically means general-purpose models that could in principle control any robot to perform any task. We care about this becaus..."
Levine envisions a future where robots can handle various home tasks autonomously, emphasizing the need for continuous learning and common sense. He describes the ideal interaction with a robot that can manage household chores and adapt to user preferences over time.
"It's just putting in place the basic building blocks, on top of which we can then tackle all these really tough problems. What's a year-by-year vision? One year in, I got a chance to watch some o..."
Discussing the timeline for robotics, Levine explains the concept of a 'flywheel' effect, where early deployments of robots can lead to rapid improvements as they learn from real-world experiences. He suggests that significant advancements could occur within single-digit years.
"As you mentioned, folding a box, folding different articles of laundry, cleaning up a table, making a coffee, that sort of thing. That's good, that works. The results we've been able to show are..."
Levine addresses the challenges in developing fully autonomous robots, including the need for effective representations and the ability to handle edge cases. He emphasizes that while the technology is progressing, there are still significant hurdles to overcome.
"I like to do my laundry on Saturday, so make sure that it's ready. This and this and this. By the way, check in with me every Monday to see what I want you to pick up when you do the shopping." ..."
Levine compares the development of robotics to that of large language models (LLMs), discussing the importance of human-in-the-loop systems for improving robotic capabilities. He highlights the potential for robots to learn from human feedback and adapt their behaviors accordingly.
"There's a lot more that goes into this. But the principles there are: you need to leverage prior knowledge and you need to have the right representations. This grand vision, what year? If you had ..."
Exploring the dynamics between humans and robots, Levine explains how human supervision can enhance robotic learning. He discusses the advantages of having robots work alongside humans to improve their performance and adaptability in real-world tasks.
"care about, that you would want to see… I don't know but single-digit years is very realistic. I'm really hoping it'll be more like one or two before something is actually out there, but it's har..."
Levine discusses the varying scopes of responsibility that robots may have in the future, emphasizing that as robots become more capable, their responsibilities will expand. He draws parallels to coding assistants and how their roles have evolved over time.
"many organizations are working on exactly this. In fact, arguably there is already a flywheel. It’s not an automated flywheel but a human-in-the-loop flywheel. Everybody who's deploying an LLM is o..."
Levine provides insights into the timeline for achieving fully autonomous robots capable of managing household tasks. He suggests that significant advancements could occur within five years, with robots gradually taking on more complex responsibilities.
"I don't think there's a profound reason why robotics is that different. There are a few small differences that make things a little bit more manageable. Especially if you have a robot that's doing..."
Discussing the economic implications of robotics, Levine reflects on how robots could augment human productivity rather than replace jobs. He emphasizes the gradual integration of robots into various sectors and the potential for increased efficiency.
"It's actually not that different from what we've seen with LLMs in some ways. It's a matter of scope. Think about coding assistants. Initially the best tools for coding, they could do a little b..."
Levine speculates on the future role of robots in the workforce, suggesting that they will complement human workers rather than fully replace them. He discusses the gradual rollout of robotic systems and their potential to enhance productivity across various industries.
"rather than a double-digit thing. The reason it's hard to really pin down is because, as with all research, it does depend on figuring out a few question marks. My answer in terms of the nature of..."
Levine examines the relationship between robotics and the broader AI boom, discussing how advancements in AI will influence the development and deployment of robotic systems. He highlights the importance of understanding the scope and limitations of robotic capabilities.
"able to do most blue-collar work in the economy. There's a nuance here. It becomes more obvious if we consider the analogy to coding assistants. It's not like the nature of coding assistants today..."
Levine emphasizes the gradual integration of robotics into various tasks, drawing parallels to the evolution of coding assistants. He discusses how robots will initially take on specific tasks before expanding their responsibilities as they improve.
"what LLMs are in now, or is it more like we have robots deployed everywhere and they're actually doing a whole bunch of real work, et cetera? It's a very subtle question. What it probably will co..."
Levine predicts that by 2028 to 2030, robots will reach a level of capability comparable to advanced AI systems. He discusses the potential for robots to handle a wider range of tasks and the implications for various industries.
"But there's so many things which increase productivity. Like wearing gloves increases productivity or I don't know. You want to understand something which increases productivity a hundredfold v..."
Sergey Levine discusses the evolving dynamics of human-robot interactions, emphasizing the importance of learning from both actions and verbal feedback. He highlights the potential for robots to take on increasing responsibilities in various tasks, depending on advancements in handling complex scenarios. This segment explores how the scope of robotic tasks will expand as technology progresses, particularly by 2028.
"it's also learning from words. Eventually it’ll be learning from observing what people do from the kind of natural feedback that you receive when you're doing a job together with somebody else. T..."
In this segment, Levine compares the expected timeline for robotics advancements to the development of self-driving cars. He argues that the current technological landscape is more favorable for robotics than it was for autonomous vehicles in 2009, suggesting that by 2028 to 2030, we could see robots capable of performing basic tasks autonomously. This discussion sheds light on the differences in challenges faced by both fields.
"will depend on our ability to handle all of the complex edge cases and handle them correctly. Now construction is something where in some places it's extremely delicate, where you really have to ..."
Levine elaborates on the challenges of scaling robotic applications, emphasizing the need for a balance between expanding capabilities and ensuring robustness. He discusses the importance of understanding the right axes of scale to enhance robotic functionality, which is crucial for practical deployment in real-world scenarios. This segment highlights the ongoing efforts to refine robotic systems for better performance.
"is a very reasonable timeline for that. Just to better explain the parameters of this, scope means a few things. Scope means how much responsibility you're willing to delegate to the machine. But ..."
In this segment, Levine explains the concept of a self-sustaining data flywheel in robotics, where robots learn from their experiences and improve over time. He emphasizes the significance of automation and productivity in achieving this goal, suggesting that as robots become more prevalent, they will contribute to their own learning and enhancement, leading to a cycle of continuous improvement.
"The reason I care about this so much is…obviously automation is good, productivity is good. But I also care about this because the more these things get out there, the closer we are to getting th..."
Levine contrasts the learning processes of robots with those of human drivers, noting that while driving mistakes can have severe consequences, robotic manipulation tasks allow for correction and learning from errors. This segment discusses the implications of common sense reasoning in robotics and how it can facilitate learning and adaptation in various tasks.
"In terms of robotics progress, why won't it be like self-driving cars, where it's been more than 10 years since Google launched its… Wasn't it in 2009 that they launched the self-driving car initi..."
Levine addresses the challenges faced by companies in developing effective robotic foundation models, highlighting the need for industrial-scale efforts beyond fundamental research. He discusses the importance of real-world data collection and the focus required to transition from research to practical applications, emphasizing that successful robotics development is akin to large-scale engineering projects.
"To give you an example, if you're learning how to drive, you would probably be pretty crazy to learn how to drive on your own without somebody helping you. You would not trust your teenage child ..."
In this segment, Levine explores the complexities of scaling data collection for robotics. He explains that simply increasing the number of operators and robots is not sufficient; understanding the right types of data and settings is crucial for enhancing robotic capabilities. This discussion highlights the ongoing efforts to identify effective strategies for data scaling in robotics.
"it's not just a laboratory science experiment. It also requires industrial scale building effort. It's more like the Apollo program than it is a science experiment. The excellent research that was ..."
Levine discusses the potential for robots to learn on the job through reinforcement learning (RL) and other methods. He emphasizes the importance of mixed autonomy, where robots can learn from human interactions and feedback, contributing to their development. This segment highlights the innovative approaches being explored to enhance robotic learning and adaptability.
"Just to give an order of magnitude, how does the amount of data you have collected compare to internet-scale pre-training data? I know it's hard to do a token-by-token count, because how does vid..."
In this segment, Levine explains how recent advancements in AI allow robotics to leverage prior knowledge from various sources. He discusses the integration of vision-language models with motor control, emphasizing the significance of this approach in enhancing robotic capabilities. This discussion underscores the interconnectedness of AI advancements and their application in robotics.
"How does the π0 model work? The current model that we have basically is a vision-language model that has been adapted for motor control. To give you a little bit of a fanciful brain analogy, a VL..."
Levine addresses the difficulties of transfer learning between different modalities, particularly between text and video. He explains how the semantic representation of text differs from the pixel-based representation of images, which complicates the learning process for robots. This segment underscores the need for robots to learn from both visual and action data to improve their performance.
"One theme here that is important to keep in mind is that the reason that those building blocks are so valuable is because the AI community has gotten a lot better at leveraging prior knowledge. A..."
In this segment, Levine explains how a robot's purpose influences its perception and decision-making. He argues that robots, unlike humans, can focus their sensory input on relevant tasks, which enhances their efficiency. This focus is crucial for developing robots that can perform complex tasks in dynamic environments.
"I was talking to this researcher, Sander at GDM, and he works on video and audio models. He made the point that the reason, in his view, we aren't seeing that much transfer learning between diffe..."
Levine discusses the limitations of current video models compared to language models, emphasizing that while video generation has advanced, it hasn't led to a deep understanding of the world. He contrasts this with language models that have demonstrated significant capabilities, highlighting the challenges in developing robust video-based AI systems.
"You had a really interesting blog post about why video models aren't as robust as language models. Sorry, this is not a super well-formed question. I just wanted to get a reaction. Yeah, what’s up ..."
This segment introduces Moravec's Paradox, which states that the easy tasks for humans, like perception and object manipulation, are the hardest for AI. Levine explains how this paradox manifests in robotics, where cognitive tasks are often easier for machines than physical tasks, and discusses the implications for future robot development.
"Whereas with language, clearly it has. This point about representations is really key to it. One way we can think about it is this. Imagine pointing a camera outside this building, there's the sky..."
Levine elaborates on the importance of context in robotic tasks, explaining that robots need to remember past actions and their outcomes to perform effectively. He discusses the challenges of maintaining context over time and how this affects a robot's ability to adapt to new situations.
"The representations are already there. They're not just good representations, they focus on what really matters. That's the bad news. Here's the good news. The good news is that we don't have to..."
In this segment, Levine emphasizes the potential of foundation models that learn through interaction with their environment. He argues that robots can better absorb data from various sources when they have a clear purpose, which enhances their learning and adaptability.
"The fact that video models aren't as robust, is that bearish for robotics? So much of the data you will have to use… I guess you're saying a lot of it will be labeled. Ideally, you just want to be..."
Levine discusses the concept of emergent capabilities in robotics, where robots develop unexpected skills through extensive training. He shares examples of how robots have demonstrated compositionality in their actions, leading to innovative solutions that were not explicitly programmed.
"robots really does help with generalization. I have the suspicion that in the long run, it'll make it easier to use those sources of data that have been tricky to use up until now. Famously, LLMs ..."
This segment explores the complexity of tasks that robots must perform, such as those in service industries. Levine highlights the need for robots to manage multiple subtasks and the challenges of automating these processes, emphasizing the importance of extensive training and data collection.
"out-of-distribution capabilities. I wonder if the trek over the next 5-10 years will be like this: Each subtask, you have to give it thousands of episodes. Then it's very hard to actually automate..."
Levine discusses the significance of representation in robotics, particularly how robots need to maintain context for effective task execution. He highlights the potential for multimodal models to improve how robots process and utilize information from their environment.
"Or is there some reason to think that it will progress more generally than that? There's a subtlety here. Emergent capabilities don't just come from the fact that internet data has a lot of stuff..."
In this concluding segment, Levine reflects on the future of robotic learning and the innovations needed to overcome current limitations. He emphasizes the importance of developing models that can generalize from diverse experiences and adapt to new challenges in real-world environments.
"That's actually where the emergent capabilities come from. Because of this, in principle, if we have a sufficient diversity of behaviors, the model should figure out that those behaviors can be ..."
Sergey Levine discusses the necessity for household robots to maintain awareness of past events to effectively plan their tasks. He highlights the limitations of current models in terms of parameter size and context retention compared to human capabilities, emphasizing the need for significant advancements in these areas to achieve true autonomy.
"For the kind of robot which is just cleaning up your house, I think it has to be aware of things that happened minutes ago or hours ago and how that influences its plan about the next task it's ..."
Levine explores the technical challenges of representing context in robotics, comparing human cognitive processes to current robotic models. He emphasizes the importance of multimodal models that can effectively integrate various forms of information, such as spatial and symbolic representations, to enhance robotic task execution.
"try to unpack this a little bit. There's a lot going on in there. One thing is a really interesting technical problem. It's something where we'll see perhaps a lot of really interesting innovati..."
In this segment, Levine compares the efficiency of the human brain to current AI models, discussing the brain's ability to process vast amounts of information in real-time. He raises questions about whether the limitations of AI stem from hardware capabilities or algorithmic efficiency, suggesting that the brain's parallel processing might offer insights for future robotics.
"Do you mean in terms of how we represent? How we represent both context, both what happened in the past, and also plans or reasoning, as you call it in the LLM world, which is what we would like ..."
Levine speculates on the advancements needed in hardware and algorithms to achieve human-like efficiency in robots. He discusses the potential for improved GPUs and innovative encoding methods to enable robots to process real-time video information and make decisions rapidly, hinting at a future where robots can operate autonomously in complex environments.
"That's a really good question. I definitely don't know the answer to this. I am not by any means well-versed in neuroscience. If I had to guess and also provide an answer that leans more on things..."
This segment delves into the implications of connectivity for robotic systems. Levine discusses the potential for robots to operate in both low-cost, off-board inference modes and more sophisticated systems that rely on real-time data processing, highlighting the balance between cost and capability in future robotic deployments.
"If in five years we have a system which is as robust as a human in terms of interacting with the world, then what has happened that makes it physically possible to be able to run those models? T..."
Levine addresses the challenges of using simulation for training robots, contrasting it with human learning experiences. He emphasizes the importance of goal-directed training and the need for robots to effectively leverage simulations to enhance their real-world performance, suggesting that the right objectives are crucial for successful learning.
"In this robotics world, should we just be anticipating something where you need connectivity everywhere? You need robots that are super fast. You're streaming video information back and forth, or..."
In this segment, Levine discusses the potential for integrating robotics with broader knowledge work, speculating on the future of models that can perform both physical tasks and abstract reasoning. He highlights the advantages of co-training and the importance of understanding the physical world to enhance cognitive capabilities in AI.
"Again, this is not a new idea. This is exactly what we've seen with LLMs. LLMs start off being trained purely with next token prediction. That provided an excellent starting point, first for all s..."
Levine concludes with a forward-looking perspective on the capabilities of AGI in creating simulations for skill rehearsal. He questions whether future models will overcome the limitations of current simulation training, pondering the implications for advanced robotics and the potential for unprecedented learning experiences.
"et cetera, which might be different from the considerations for knowledge work. Maybe it's still the same model, but then you can serve it in different ways. The advantages of co-training are high..."
Levine delves into the significance of counterfactual reasoning in decision-making processes, both for AI and humans. He posits that optimal decision-making hinges on the ability to consider alternative scenarios and outcomes. This segment underscores the importance of developing mechanisms that allow AI systems to evaluate counterfactuals effectively, which is crucial for their advancement and application in real-world situations.
"Once we have, in 2035 or 2030, basically this sci-fi world, are you optimistic about the ability of true AGIs to build simulations in which they are rehearsing skills that no human or AI has eve..."
This segment discusses the potential future of robotics and AI, particularly in the context of a rapidly evolving economy. Levine speculates on the deployment of a robot economy by 2030 and the implications for AI development. He emphasizes the need for significant capital investment in infrastructure to support this growth and the role of robots in enhancing productivity across various sectors.
"Do you have a sense of what the equivalent is in humans? Whatever we're doing when we're daydreaming or sleeping. I don't know if you have some sense of what this auxiliary thing we're doing is, ..."
Levine addresses the challenges associated with scaling robotics to meet future demands. He discusses the importance of understanding the economic implications of deploying a large number of robots and the need for efficient manufacturing processes. This segment highlights the complexities of integrating robotics into existing industries and the potential for robots to revolutionize production and labor.
"You can just crunch numbers on having 100-200 gigawatts deployed by 2030. The marginal capex per year is in the trillions of dollars. It's $2-4 trillion dollars a year. That corresponds to actual ..."
In this segment, Levine reflects on the decreasing costs of robotic arms and the factors contributing to this trend. He discusses the impact of economies of scale, technological advancements, and the role of AI in reducing hardware requirements. This analysis provides insight into the future landscape of robotics and the potential for widespread adoption as costs continue to decline.
"It can be a big productivity boost for real people and it can allow you to solve problems that are very difficult to solve. For example, I'm not an expert on data centers by any means, but you co..."
Levine speculates on the future of robot manufacturing and the potential for a robust robot economy by 2030. He discusses the importance of having a sufficient number of robots to support the growing demands of AI and the challenges of scaling production. This segment emphasizes the need for strategic planning and investment to ensure that the infrastructure can support the anticipated growth in robotics.
"Interesting. Do you think the learning rate will continue? Do you think it will cost hundreds of dollars by the end of the decade to buy mobile arms? That is a great question for my co-founder, Ad..."
In this concluding segment, Levine discusses the vision for a diverse and innovative robotics ecosystem. He emphasizes the importance of balancing software and hardware development to create effective robotic solutions. This segment highlights the potential for collaboration and innovation in the robotics field, paving the way for a future where robots play a crucial role in various industries.
"demand when there's a lot of demand. How many iPhones were in the world in 2001? There's definitely a challenge there. It's something that is worth thinking about. A particularly important questio..."
Sergey Levine discusses the transformative potential of automation in enhancing workforce productivity. He emphasizes that automation, akin to LLM coding tools for software engineers, can significantly amplify the productivity of all workers. However, he acknowledges the complexities involved in achieving this ideal state, including the need for strategic investments in both software and hardware innovation.
"One broader theme here is that if you want to have an economy where you get ahead by having a highly educated workforce—by having people that have high productivity, meaning that for each person..."
Levine outlines the challenges and decisions necessary to transition society towards a highly automated future. He expresses optimism about achieving a productive society through robotics, but stresses the importance of long-term vision and balanced investment in robotics ecosystems. The conversation highlights the geopolitical dimensions and the need for thoughtful planning in the face of rapid technological advancement.
"All of that stuff is pretty complicated. It requires making a number of really good decisions. Good decisions about investing in a balanced robotics ecosystem, supporting both software innovation..."
In this segment, Levine raises concerns about the hardware bottleneck in robotics production. He questions how the U.S. and its allies can manufacture billions of robots, emphasizing the need for a robust manufacturing strategy. This discussion touches on the implications of relying on existing supply chains, particularly those dominated by China, and the importance of developing a self-sufficient robotics ecosystem.
"But I'm very optimistic about it because it seems to me that the light at the end of the tunnel is in the right direction. I guess there's a different question. If the value is bottlenecked by har..."
Levine explores the circular relationship between robotics and physical work, suggesting that advancements in robotics can facilitate the production of more robots. He contrasts this with digital devices, which do not inherently assist in their own creation. This segment emphasizes the importance of establishing strong feedback loops to enhance robotics capabilities and production efficiency.
"it seems like a different question than, "Well, what is the impact on human wages or something?" For the specifics of how we make that happen, that's a very long conversation that I'm probably no..."
Levine advocates for a holistic view of robotics development, stressing the need to balance AI advancements with hardware and infrastructure considerations. He warns against becoming overly focused on AI at the expense of other critical areas, such as hardware development, and calls for comprehensive discussions on the future of robotics and knowledge work.
"It's not necessarily bad that they help others. But to the extent that a lot of the things which would go into this feedback loop—the sub-component, manufacturing and supply chain, already exist ..."
In this segment, Levine discusses the societal implications of full automation, predicting a future where human labor is complemented by robots, leading to increased wealth and productivity. He emphasizes the need for society to prepare for this shift, ensuring that the benefits of automation are widely shared and that education remains a key focus to mitigate potential negative impacts.
"holistic view of these things. I wish we had more holistic conversations about that sometimes. From the perspective of society as a whole, how should they be thinking about the advances in roboti..."
Levine concludes by highlighting the critical role of education in adapting to the changes brought by automation. He argues that education provides flexibility and resilience against the disruptions of technological advancement. This segment underscores the paradox of Moravec, where tasks that are easy to automate may require the most education for humans, reinforcing the need for a strong educational foundation in an automated future.
"evolves quite the way that people expect. Sometimes the journey is just as important as the destination. It's very difficult to plan ahead for an end state. Directionally, what you said makes a lo..."