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 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 is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep RL algorithms. Support this podcast by signing up with these sponsors: - ExpressVPN at https://www.expressvpn.com/lexpod - Cash App - use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Sergey's Twitter: https://twitter.com/svlevine Sergey's Website: http://rail.eecs.berkeley.edu/ Sergey's Papers: https://scholar.google.com/citations?user=8R35rCwAAAAJ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 3:05 - State-of-the-art robots vs humans 16:13 - Robotics may help us understand intelligence 22:49 - End-to-end learning in robotics 27:01 - Canonical problem in robotics 31:44 - Commonsense reasoning in robotics 34:41 - Can we solve robotics through learning? 44:55 - What is reinforcement learning? 1:06:36 - Tesla Autopilot 1:08:15 - Simulation in reinforcement learning 1:13:46 - Can we learn gravity from data? 1:16:03 - Self-play 1:17:39 - Reward functions 1:27:01 - Bitter lesson by Rich Sutton 1:32:13 - Advice for students interesting in AI 1:33:55 - Meaning of life CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Sergey Levine, co-founder of Physical Intelligence, joins the show to discuss the frontier of general-purpose robotics. Unlike traditional robots designed for specific tasks, Levine is building foundation models that enable robots to understand and interact with the physical world generally. They explore the shift from specialized bots to "physical intelligence," the parallels between LLMs and robotic learning, and how end-to-end AI models are solving complex motor control problems. Levine also covers the challenges of data collection, the "Robot Olympics" benchmarks, and realistic timelines for when robots might actually fold our laundry. This is a deep dive into the technology that could spark a Cambrian explosion in hardware and automation. #robotics #AI #machinelearning #physicalintelligence #automation #futuretech #deeplearning #techinvesting #investlikethebest #sergeylevine Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing Presented by Ramp: https://ramp.com/invest Sponsored by Vanta, WorkOS, Rogo, and Ridgeline: https://www.vanta.com/invest https://workos.com/ https://rogo.ai/invest https://www.ridgelineapps.com/ ****** Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc